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Different heating processes were applied to pretreat the pulp and paper mill sludge to enhance the hydrolysis stage. Hot air oven, autoclave, microwave and water bath were used for comparison. Chemical analyses exposed that all the pretreatment methods have shown improvement in solubilisation of organic matter. Among the pretreatments studied, heat transfer through hot air (80 °C for 90 min) mode had the highest impact on sludge solubilisation. There was an increase in cellulose (P = 0.0096), acid soluble (P = 0.0053) and insoluble lignin (P = 0.0012), but decrease in hemicellulose (P = 0.0009) content after pretreatment. The XRD and FT-IR spectroscopic characterisation shows the development of aliphatic, unsaturated and carbonyl carbon functionalities in the pretreated samples at higher severities. FESEM picture also confirms the change in structure after pretreatment. Thus, pretreatments contribute to disruption of the lignocellulosic structure making the cellulose easily accessible to acidogenic microorganisms. [ABSTRACT FROM AUTHOR]

A solution-processed conductive paper heater (CPH) with metallic features is used as a new class of low voltage ignitor of nanoenergetic materials (nEMs). In contrast to conventional electrothermal ignition techniques that typically require high operating voltages, the combustion of a paper substrate with a relatively low ignition point generates high thermal energy instantaneously, which makes it possible to ignite nEMs even at low voltages as low as ∼1 V. In addition, the ignition and explosion properties of CPH-based low voltage ignitors are highly controllable and reproducible, mainly due to their simple fabrication and reliable electrical properties. Based on the low voltage ignition property, a compact remote ignition system is demonstrated by integrating wireless a switching circuit, portable batteries, and heater mounter in a small plastic package and operated remotely to ignite the nEMs. [ABSTRACT FROM AUTHOR]

Abstract: Carbon paper is commonly used as the gas diffusion layer (GDL) in polymer electrolyte membrane (PEM) fuel cells as it exhibits high chemical and mechanical durability. This diffusion medium is also anisotropic, which directly affects its transport properties and specifically the thermal conductivity. In this study, the in-plane thermal conductivity of the carbon paper GDL was determined using thermal diffusivity measurements for a temperature range from −20 to +120°C and four Teflon loadings (0, 5, 20 and 50wt.%). It is important to understand the effect of temperature on the thermal conductivity since PEM fuel cells are designed to operate under various temperatures depending on the application of use. Further, Teflon is used to change the hydrophobic properties of the carbon paper GDL with 20wt.% as the most widely used percentage. In this study, the Teflon loadings were chosen to gain a comprehensive understanding of the thermal resistance due to Teflon. In this study, a quasi-steady method was used to measure the thermal properties of the carbon paper; hence, the phase transformation in the presence of PTFE was investigated. The thermal conductivity decreases with an increase in temperature for all samples. The addition of as little as 5wt.% Teflon resulted in high thermal resistance decreasing the overall thermal conductivity of the sample. Further addition of Teflon did not have major effects on the thermal conductivity. For all treated samples, the thermal conductivity lies in the range of 10.1–14.7W/mK. Finally, empirical relations for the thermal diffusivity and conductivity with temperature were deduced. [Copyright &y& Elsevier]

In this paper, we generalize a fast Fourier transforms (FFTs) based preconditioner and propose a novel α -absolute value preconditioner for all-at-once systems from heat equations. Our preconditioner is symmetric positive definite and can also be computed efficiently using fast Fourier transforms or discrete sine transforms (DSTs). Furthermore, we prove that the corresponding preconditioned matrix has a unitary-plus-low-rank decomposition, and the eigenvalues of the preconditioned matrix are clustered at ±1, which leads to fast convergence rate of the preconditioned MINRES method. Finally, numerical examples on all-at-once systems from heat equations with different discretization scheme demonstrate the effectiveness of our new preconditioner. [ABSTRACT FROM AUTHOR]

• Saturated-wave theory for snubber modeling is modified to fix three conceptual errors. • The equivalent resistance and inductance of the snubber are recalculated. • Calculating the thickness of eddy-current region no longer relies on external circuit. • The current rise of the model is nearly the same as the experiment with about 10 % over-estimated peak shorting current. Core snubber is a common device used for breakdown protection in auxiliary heating systems. It absorbs energy from the high-voltage power supply to protect the load. The existing snubber models are based on saturation-wave theory, but the theory still has several conceptual problems. In this paper, an improved saturated-wave theory is presented. We consider that the eddy current region of the tape needs to be layered and that there is an unsaturated region in the tape which enables the snubber to be equated to an inductor. Then we divided the working process of snubber into 4 stages and rederived the circuit model of the snubber. The comparison with the existing simulation model shows that the new model in this paper is closer to the experimental data, especially in the rising phase of short-circuit current. It can more accurately analyze the conditions within a few microseconds after breakdown, and give a useful reference for the design of breakdown protection and power supply systems. [ABSTRACT FROM AUTHOR]

Integrated Energy Systems (IES) serve as an effective solution to address grid fluctuations. However, mainstream IES still adhere to established supply strategies such as thermal and electrical non-conversion (TENC), heat determined by power (HDP), and power determined by heat (PDH). These strategies overlook the additional flexibility generated by the synergy between electricity and heat, preventing the full realization of their advantages in supporting the grid. This paper proposes a Flexible Scheduling (FS) operational strategy for IES that maximizes the utilization of flexibility resources to meet the demands of heating, cooling, and electricity while maintaining grid stability. The superiority of this strategy is analyzed through an IES multi-objective optimization scheduling model and a comprehensive flexibility margin model. The results of a case study indicate that the FS strategy reduces total costs by 19.9% compared to TENC, 16.6% compared to HDP, and 5.2% compared to PDH. It exhibits the strongest support for the grid, increasing the overall flexibility margin of IES by 40.5% compared to TENC, 12.7% compared to HDP, and 8.2% compared to PDH. The FS-based IES operational strategy proposed in this paper contributes significantly to the flexible and economical operation of IES. • Established a multi-objective optimization scheduling model for integrated energy systems that considers both economic and environmental factors. • Established a model for the flexibility margin of system electricity and heat. • Proposed the system's "flexible scheduling" operating strategy and validated it. [ABSTRACT FROM AUTHOR]

Although fully automated, operation of the District Heating Systems (DHS) is considered reactive and simplistic since control decisions are most often made based on the real-time ambient temperature. The research presented in this paper is aiming at facilitating proactive DHS control, by using predictive modeling techniques based on modern Artificial Intelligence (AI) architectures, namely the Deep Learning (DL)-based multivariate time-series forecasting. Proactive approach involves controlling fluid temperature in the DHS supply line, based on relevant meteorological features, heat demand in the past and weather forecast. In this paper, we evaluate state-of-the-art DL architectures for multivariate one-step and multi-step prediction based on Long-Short Term Memory (LSTM) approach, namely, stacked LSTM, bi-directional LSTM, encoder-decoder and attention models. The initial results of forecasting heat demand and local explanations are provided for the case of small 8MW DHS. For local explanations, Local Interpretable Model-agnostic Explanations (LIME) was successfully validated. [ABSTRACT FROM AUTHOR]

This paper experimentally investigates the effect of increasing ice thickness at a heater's edge on the thermal energy required to de-ice ice at the leading's edge. A correlation between ice thickness at the stagnation line and ice thickness at the heater edge is developed for the leading edge of a cylinder surface. An experimental approach is used to investigate whether the increase in ice thickness at the heater's edge is linked to the increase in ice thickness at the stagnation line. The de-icing investigation shows that as ice thickness at the heater's edge grows, the de-icing duration significantly increases. The results of this paper suggest that employing a correlation between the ice thickness at both the stagnation line and the heater's edge can help reduce the thermal energy required to remove ice from the leading edge of an airfoil or cylindrical surface. • Defining the ice thickness relationship at the stagnation line and heater edge for cylindrical ice accumulation. • Examining the impact of ice thickness at the heater edge on the thermal energy required for de-icing. • Using imaging techniques to measure accumulated ice on a cylindrical surface. • Using thermal imaging to analyze the behavior of ice during the de-icing process. [ABSTRACT FROM AUTHOR]

In this paper the aerothermal heating of a reusable launch vehicle is reconstructed on the basis of temperature measurements taken in the thermal protection system of this vehicle. The discussed integrated thermal protection system is composed of three layers. Mathematical model, describing the problem, takes into account the dependence on temperature of the material parameters as well as the thermal resistances occurring in the contact zones of the layers, which is a novelty in the proposed approach. For solving the direct problem, the implicit scheme of the finite difference method is applied. Next, by using the solution of the direct problem, the Tikhonov functional is created, which describes the error of the current approximate solution. Whereas for determining the solution of the inverse problem the Levenberg-Marquardt method, modified and adapted to the Tikhonov functional, is used. The paper presents the mathematical model of the problem and the method of solution together with the selected examples illustrating its exactness and stability. In order to better examination of the solution method some various values of parameters are taken in the demonstrated examples. • Reconstructing an aerothermal heating in space vehicle protection system with temperature dependent material properties. • Reconstructing an aerothermal heating taking into account the thermal resistance between layers. • Solving the problem for three layers model of the thermal protection system. • Inverse problem solved for input data taken from NASA measurements. • Heat flux measured by NASA reconstructed with good precision and stability. [ABSTRACT FROM AUTHOR]

Subgrade frost heave is the main reason that affects the safe operation and comfort of trains during the cold season. Solar heating system based on heat pipe technology is a promising method to mitigate the frost heave by raising the subgrade temperature. In this paper, a field experiment was conducted to evaluate the heating performance of the solar heating system based on heat pipe technology in the cold season. In addition, the heat collection performance and stability of the system are compared with other solar heating systems. Finally, its applicability is discussed. Experimental results revealed that the solar heating system based on heat pipe technology can effectively raising the soil temperature and mitigate the frost heave of subgrade. During the experiment period, the soil temperature at the depth of −0.5 m in the experiment site was 5 °C higher than that of the comparison site, and the maximum freezing depth was 0.4 m, which was 56 % lower than that of the comparison site. The mean daily temperature of heat collection units (HCUs)fluctuated around 30 °C, and the mean daily temperature of the two-phase closed thermosyphons (TPCTs) fluctuated between 15 °C and 30 °C. The heat transfer efficiency of System 1 and System 2 is greater than 0.5, and the total effective heat transfer during the experiment period is 512 MJ and 283 MJ, respectively, and the heat transfer difference is about 2 times. Compared with other solar heating systems, the system proposed in this paper has good heat collection performance and more superior system stability. The mean daily heat transfer of System 1 and System 2 is 6.4 MJ and 3.71 MJ, respectively, which is 62.9 % higher and 5.6 % lower than the mean daily heat loss of subgrade of 3.93 MJ, respectively. The system has the application prospect of low maintenance cost and good long-term anti-frost heave effect. • A solar heating system based on heat pipe technology was proposed for mitigating subgrade frost heave. • The soil temperature can be effectively increased under the heating effect of the system. • The system can provide effective heat transfer higher than 62.9 % of the mean daily heat loss of subgrade. • To provide a reference of using solar energy and heat pipe technology for mitigating subgrade frost heave. [ABSTRACT FROM AUTHOR]

District heat load forecasting is a challenging task that involves predicting future heat demand based on historical data and various influencing factors. Accurate forecasting is essential for optimizing energy production and distribution in district heating systems. However, most existing forecasting models lack transparency and interpretability and fail to capture the spatial–temporal dependencies in the data. Moreover, they often require a large amount of annotated data for training, which can be costly and time-consuming to obtain. In this paper, we present a novel approach to district heat load forecasting, which involves predicting future heat demand based on historical data and various influencing factors. The proposed approach is based on an Active Graph Recurrent Network (Ac-GRN), which leverages the strengths of active deep learning and graph neural networks to capture the complex spatial–temporal dependencies in the data. The approach also provides explainability for its predictions by using correlation-based attribution methods. The active deep learning component can effectively select the most informative and representative samples from a large pool of data, reducing the frequency and cost of data collection and human effort. The graph neural network component can model both linear and nonlinear relationships among heat meters using bidirectional recurrent connections, enhancing the accuracy and robustness of the predictions. We conduct extensive experiments and compare our approach with eleven state-of-the-art models on a real-world dataset of district heating consumption data from Danish residential buildings. Our results show that our approach outperforms other models in terms of accuracy, robustness, reliability, and computational efficiency for multi-horizon multi-step district heat load forecasting. Our approach also provides meaningful explanations for its predictions by highlighting the most influential factors and heat meters for each prediction. This paper makes a novel contribution to district heat load forecasting with explainability. • Novel approach for district heat load forecasting with active deep learning and graph neural networks. • Explainable predictions using gradient-based attribution methods. • Outperforms state-of-the-art models in accuracy and robustness. • Evaluates on real-world district heating data and conducts ablation studies. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

Drones and UAVs are gaining worldwide popularity and are now considered a popular choice for both military and commercial applications. Icing poses tremendous safety and performance concerns for the operation of all types of drones. This paper is part of a project that studies icing effects on the hovering 0.66 m diameter Bell APT70 drone rotor and the development of potential ice protection systems. The objective of this paper is to investigate the use of electrothermal heaters with severe icing conditions and to characterize the energy efficiency of anti-icing or de-icing regimes. Rotor speeds of 3880, 4440 and 4950 RPM were used while two air temperatures, −5 and −12 °C, were also tested. The heat transfer profile on the blade was studied using dry run tests where the rotor operated with heating but without water spray. Anti-icing tests with variable heating power were tested to determine the location of the initial ice accumulation as well as the minimum heating power requirement to protect the leading edge. Moreover, anti-icing tests with constant heating rates were done and compared to previous tests of unprotected blades, under similar condition. On the other hand, de-icing tests with various heating cycles were also carried out. Results indicate that although de-icing requires significantly less heating energy compared to anti-icing, there is clear trade-off on rotor stable aerodynamic output, electrical power consumption and vibration levels, depending on the adopted de-icing mode. Future work is about investigating the rise of secondary effects, such as shed ice impact due to de-icing. Work is also planned for a hybrid ice protection system that combines electro-thermal heaters with icophobic or superhydrophobic coatings. [ABSTRACT FROM AUTHOR]

• The optimization problem of EPS and flexibility-enhanced DHSis formulated. • The DHS is retrofitted with BCT and uses a variable mass flow operation strategy. • A conditional-DRO is employed to hedge against the uncertainty of renewable energy. • Propose adouble-layer distributed algorithm to achieve the distributed solution. In an integrated electricity and heating system (IEHS), building flexibility-enhanced district heating systems (DHSs) that use advanced flexibility-enhanced technologies are effective for promoting renewable energy accommodation. However, it is challenging to comprehensively utilize these flexibility-enhanced technologies in IEHS dispatch. This paper proposes a distributed conditional-distributionally robust coordination method for an electrical power and flexibility-enhanced DHS. The DHS is retrofitted with bypass compensation technology and uses a variable mass flow operation strategy to enhance its flexibility and promote renewable energy accommodation. Moreover, conditional-distributionally robust optimization is used to consider the inherent dependency between the forecasting error of a wind farm and the forecast output information, which goes beyond the state-of-the-art models. Additionally, a novel reformulation method is adopted to transform the problem with random variables and bilinear constraints into a more tractable programming problem, yielding a conservative approximative model of the original model that guarantees feasibility. Finally, to protect the privacy of electrical power system and DHS operators, this paper proposes a double-layer distributed algorithm to solve the problem in a distributed fashion while guaranteeing feasibility. Case studies demonstrate that the proposed model with flexibility-enhanced DHSs significantly promotes renewable energy accommodation and reduces dispatch costs, and the proposed double-layer distributed algorithm effectively solves the dispatch problem in a distributed fashion and guarantees the feasibility of dispatch results. [ABSTRACT FROM AUTHOR]

[Display omitted] • Localized heating systems are a proper solution for providing thermal comfort. • Providing personalized heating by radiation or conduction is more convenient, so the inhaled air is kept cool and pleasant. • Local radiant heating systems directly heat the body without heating the environment, leading to lower energy consumption. • With local radiant heating systems, proper thermal comfort and sensation can be achieved even at low air temperatures. Localized heating systems are a proper solution for providing thermal comfort while lowering energy consumption in cold environments. Local radiant heating systems provide thermal comfort at lower air temperatures than convective systems which heats air, resulting in cool and pleasant inhalation air. This paper reviews experimental studies regarding local radiant heating systems considering thermal sensation and comfort. These systems can heat body directly, often with a big view factor toward the body. They do not necessarily heat the air and environment, leading to lower energy consumption. Other types of heating systems combined with local radiant heating systems are also reviewed in this paper. According to reviewed studies, localized methods improved thermal comfort, and the same thermal sensation could be achieved in lower air temperatures, especially when using local radiant heating systems. To design local radiant heating systems properly, improvement of existing local thermal comfort and sensation modeling based on radiant heat transfer is needed. [ABSTRACT FROM AUTHOR]

• Combining deep reinforcement learning DRL and Computational Fluid Dynamics CFD • Control and optimization of two and three dimension conjugate heat transfer applications • Loss function engineering as a key aspect for a good optimization • Policy-Based Optimization improved the temperature homogeneity during heating Gas furnaces are the most widely used means of heating in industry, and with the growing concern for environmental issues, and a global energy crisis at our doorstep, the optimization of the processes related to them becomes a key challenge. This paper aims at introducing a new way of practicing gas furnace control involving simulations, virtual sensors and deep reinforcement learning (DRL) techniques. In order to do so we designed a set of simulations of conjugate heat transfer systems governed by the coupled Navier–Stokes and heat equations for single-step control. The DRL algorithm used in this paper is the policy-based optimization (PBO) algorithm specialized in single-step (or open-loop) control. We explore its ability to find global maxima in different situations and under various constraints. Therefore, various 2D and 3D cases are tackled, in which the position of the work piece, the flow rate, and other parameters are controlled. The obtained results highlight the potential of the DRL framework combined with computational fluid dynamics (CFD) conjugate heat transfer systems for optimizing searches in large parameter spaces. For the 2D case, PPO achieved an increase of 89 % in temperature homogeneity, and for the 3D case an increase of 7 % in final temperature with the same total input. [ABSTRACT FROM AUTHOR]

Accurate heating load prediction is essential to supply-demand collaboration in district heating systems and energy sustainability. With the development of the Internet of Things technology, massive monitoring data is collected that provides abundant data sources for the prediction models. However, the missing data from equipment anomalies, network transmissions and the complexity of heat load changing cause difficulty in realizing accurate heat load prediction. Therefore, the hybrid model based on bidirectional long short-term memory network and temporal convolutional network proposed in this paper to improve the accuracy of the heat load prediction from the aspects of reconstruction of missing values and extraction of complex features. The bidirectional long short-term memory algorithm imputes missing values based on bi-directional features of existing data and provides solid data foundation for subsequent training of predictive models. The temporal convolutional network achieves high-level feature extraction and parallel computation, which contributes to the effective modeling of complex changes in heat load and the realization of accurate heat load prediction. Comparative study was conducted with four heat exchange stations of actual environment to evaluate the performance of the prediction model. The results showed the mean absolute percentage error of the hybrid model decreased to 2.47% which is lower than the State-of-the-Art models, such as long short-term memory, temporal convolutional network, and support vector regression etc. The superior results further validated the feasibility of this hybrid model in heat load prediction. • The missing values in heating system were reconstructed by deep learning model. • A hybrid heating load prediction model was proposed considering missing values. • Detailed experiments were conducted to validate the model's superiority. [ABSTRACT FROM AUTHOR]

Hydrogen propulsion is generally treated as the ultimate net zero-emission option for aviation, therein liquid hydrogen storage appears to provide the only feasible solution for aircraft with the constraints imposed by aircraft weight and volume. Hydrogen liquefaction consumes a great amount of energy, which significantly boosts the cost of hydrogen as an aviation fuel. Besides the H 2 chemical exergy for combustion to provide thrust, the H 2 physical exergy in the form of cold energy is also a kind of high-quality clean energy to produce power output, which offers great opportunities to achieve cost-effective use of hydrogen fuel. Thus, this paper presents a Rankine cycle and H 2 direct expansion cycle (RC-DEC) combined power generation thermal management system for the simultaneous utilization of exhaust gas heat exergy and hydrogen fuel cold exergy from the liquid hydrogen-fueled turbojet engine. In the Rankine cycle, the transcritical mode is applied on the hot side while the liquid separation condensation concept is adopted on the cold side to achieve better thermal matchings with heat and cold sources. The hydrocarbon/inert gas binary mixture is proposed as the working fluid of the Rankine cycle. The effects of key thermodynamic parameters and the influence of Para-Ortho hydrogen conversion on system performance are discussed, then a comparison of the mixture working fluid is conducted. Results show that there exists an optimal separation temperature ratio and condensation pressure of RC to obtain maximum total net power output. As the DEC expander inlet pressure increases, the total net power output first increases and then levels off. The introduction of the Para-Ortho hydrogen conversion has a negligible impact on net power output but leads to a significant reduction in the exergy efficiency, which is attributed to the cold energy release nature of Para-Ortho hydrogen conversion. The results of the working fluid comparison indicate that the optimal inert gas retardant for CH 4 is Kr, while those for C 2 H 6 and C 3 H 8 are Xe to achieve the maximum net power output and exergy efficiency of the proposed power generation system. Among them, C 2 H 6 /Xe(0.6/0.4) obtains the maximum net power output of 2717.9 kW and maximum exergy efficiency of 33.1%, which suggests an opportunity to provide megawatts of electrical power for the aircraft in place of the APU. • RC-DEC combined thermal management system is proposed for LH 2 turbojet engine. • The hydrocarbon/inert gas binary mixture is proposed for LH 2 cold exergy recovery. • The influence of the Para-Ortho hydrogen conversion on the system is revealed. • The optimal inert gas retardant for the selected hydrocarbons is determined. [ABSTRACT FROM AUTHOR]

As the coupling between power distribution systems (PDSs) and district heating systems (DHSs) becomes tighter, it is critical to develop a coordinated strategy for load restoration in integrated electric and heating systems (IEHSs) after natural catastrophes. Similar to PDS reconfiguration, DHSs can modify network topology by remotely operating the ties and sectionalizing valves. A coordinated reconfiguration with soft open points (SOPs) is proposed in this paper for resilience-oriented restoration in IEHSs, which mitigates the fault propagation during fault isolation and explores the flexibility of network topology variation with SOPs for load restoration. To guarantee the privacy of electric and heating systems, we propose an adaptive alternating direction method of multipliers (ADMM), which divides the original problem into PDS and DHS subproblems. The proposed algorithm can intelligently dispatch SOPs and switches in the PDS sub-problem and valves in the DHS subproblem. Comprehensive case studies are carried out to illustrate the effectiveness of coordinated reconfiguration with SOPs for load restoration and verify the proposed algorithm. When considering coordinated reconfiguration with SOPs under the PDS fault scenario, the resilience metrics value increases by 9.6% compared to only PDS reconfiguration. • A Distributed coordinated load restoration strategy in integrated electric and heating systems is proposed. • Soft Open Point flexibility is fully exploited to improve load restoration levels. • Coordinated reconfiguration of the PDS and DHS is provided to improve service restoration level. • The adaptive ADMM is utilized to guarantee the privacy security of electric and heating systems. [ABSTRACT FROM AUTHOR]

Solar thermal energy is one of the important sources of renewable energy. The solar thermal energy gives clean energy which can be utilized for industrial applications. Industrial heaters which require heat energy for many industrial uses can be attained using these solar thermal absorbers. The solar thermal absorber is designed using a layer design that consists of material compositions of Titanium (Ti) and Stannic selenide (SnSe 2) for ground and substrate sections respectively. A graphene layer which has the good properties to be used in solar absorbers is added between the Aluminum and SnSe 2 material. A thin graphene section is inserted above the SnSe 2 substrate and below the Ti resonator. To catch the good absorption in the current design, the best four wavelengths (micrometer) have been attached such as 0.22, 0.8, 2.46, and 2.85. This design's contribution can be studied in 4-atm regimes (from UV, V to NIR, and MIR). According to the wavelength and bandwidth ranges, the current absorption explores more than 97% (97.4%) in between 2.4 and 3 μm (600 nm) bandwidth and exceeds 95% (95.2%) from 0.2 to 1 μm (800 nm) respectively. To the overall range of design, it extracts 90.3% for the 2800 nm band between 0.2 and 3 wavelength regime. The flow of the presentation of the proposed paper is (i) Introduction (ii) Design and Parameters, and (iii) Results and Discussions. The explored absorber can be effective in water heating and swimming pool heating for homes. Moreover, it can also be applied in large industries as a solar thermal absorber for heating applications. [ABSTRACT FROM AUTHOR]

Clarifying the aging characteristics of aluminum alloys during continuous heating is crucial for formulating isothermal and non-isothermal aging (continuous heating aging) processes. This paper investigates the stress relaxation characteristics of an Al-Cu-Mg-Ag alloy under three-point bending during continuous heating. Taking into account the mechanical properties and precipitate analysis, it demonstrates the feasibility of using the stress relaxation method to determine the peak aging temperature during continuous heating. This approach is efficient, requires minimal sample, and yields continuous data. The peak aging temperature of the sample increases from 260 °C to 305 °C when the heating rate increases from 1 to 3 °C/min with the same initial load (3 N). The peak aging temperature is around 280 ℃, when the initial load increases from 1 N to 9 N with the same heating rate (2 °C/min). Under low temperatures (25 ℃∼ 200 ℃), the sample displays the presence of GP zones, and a few Ω precipitates during the continuous heating process of 2 ℃/min. As the temperature increases, the GP zones dissolve and transform into the Ω precipitates. A large number of Ω precipitates appear in the matrix in a continuous precipitation manner when the temperature reaches 235 °C. The precipitation strengthening effect is strongest at 280 °C, and the Ω precipitates begin to coarsen as the temperature exceeds 280 °C. [Display omitted] • A method to determine the continuous heating peak aging temperature of aluminum alloys was proposed. • The peak aging temperature of Al-Cu-Mg-Ag alloy was related to the heating rate. • The evolution of the precipitates during continuous heating aging of Al-Cu-Mg-Ag alloy was obtained. [ABSTRACT FROM AUTHOR]

• A defect model modification method with surface heat flux identification is proposed for thermal protection system. • Levenberg–Marquardt method is adopted to identify surface heat flux, and whether the defect has occurred is determined. • A defect model modification method is proposed from the knowledge of the identified heat flux. • An experiment is designed to prove good accuracy of the proposed method. Thermal protection systems (TPS) have become a key issue in ensuring the reliability of hypersonic flights. Aimed at monitoring the thermal loads and possible defects for TPS, a defect model modification method with surface heat flux identification is proposed in this paper. Firstly, by the measured temperature from a distributed sensor network and the initial TPS model, the surface heat flux was identified with the Levenberg–Marquardt Method (LMM). If there existed outliers in the identified heat flux distribution, it can be visually indicated that the defect occurred. Then, defects were represented by shape parameters and can be determined by the model modification from the knowledge of the identified heat flux and the measured temperature. The effectiveness of the proposed method was verified numerically using TPS containing a surface defect. Meanwhile, the effects of various factors on the identified results were investigated in detail, such as defect sizes, defect shapes, sensor locations, and real aerodynamic heating. Finally, the proposed method was successfully applied to an experiment, and results demonstrated that the proposed method can achieve good identification results by experimentally measured temperature. [ABSTRACT FROM AUTHOR]

Cleaner end-uses of energy, including electrified heating, represent a cornerstone of a decarbonised energy transition. On the other hand, many governments have adopted measures to mitigate energy poverty and facilitate access to modern energy services, including heating. Both objectives may be interrelated, since energy poor people are less likely to use cleaner (and costlier) heating fuels. This paper analyses the impact of energy poverty on the decision to use different heating sources and identifies whether the events of COVID and the Ukraine war have affected this decision. Thus, a multinomial probit model is estimated using information from a large database of Spanish households in 2019, 2021 and 2022. The results show that being energy poor increases the probability to use carbon-intensive energy sources for heating compared to electricity, and that COVID and the war in Ukraine have affected this relationship. The increase in energy poverty over those years has negatively affected the decarbonisation goal with respect to heating choices. The influence of those events on the probability to use electric heating depends on some household and dwelling features. Therefore, mitigating energy poverty increases the welfare of energy poor people while supporting the choice of cleaner heating. [ABSTRACT FROM AUTHOR]

• The main thermal-hydraulic parameters of the ITER IBED PHTS have been assessed under selected operational scenarios. • The Fathom model of the ITER IBED PHTS has been revised and updated. • Three scenarios are presented, i.e. one that simulates hot standby conditions and the other two that simulate the normal operation. • The study confirms the correct operation of the system while highlighting some potential criticalities. One of the key elements for the success of ITER is the efficient removal of the thermal power generated by the deuterium-tritium fusion reaction within the confined plasma. The Integrated Blanket ELM/VS Coils and Divertor (IBED) Primary Heat Transport System (PHTS) is a pressurized, closed-loop cooling system designed to supply cooling water to the in-Vessel components. To support process design of the IBED PHTS, a thermal-hydraulic research campaign has been conducted to analyse the proper operation of the system during selected operational scenarios. The work has been carried out using the commercially available thermal-hydraulic software AFT Fathom, performing steady-state hydraulic and heat transfer analyses of complex hydraulic systems. The present paper summarizes the research activity, describing the computational model used and the scenarios investigated, as well as the results obtained and their critical discussion. [ABSTRACT FROM AUTHOR]

For the current molten salt storage heat to medium and high frequency electromagnetic coupling heating mainly, there are problems such as high failure rates and difficulties in achieving high-voltages and high-power. This paper proposes a power-frequency electromagnetic coupling direct heating technology that can realize high-voltage and high-power heating of molten salt. According to the theory of electromagnetic induction heating and the working principle of transformer, a power frequency electromagnetic coupling heating molten salt heat storage system that can achieve uniform heating effect is designed. By studying the influence of different heating power and different heat storage materials on parameters such as heat storage time, heat storage performance, and heat storage uniformity, a theoretical basis is provided for the system to determine a reasonable heating power and scope of application. A heat exchange tube layout scheme was proposed for the two operating modes of heat release, and simulation analysis was conducted. And the results show that the heat energy can be transferred outward quickly and uniformly. Through theoretical calculation and simulation experiments, the system can realize uniform heating and heat release of molten salt, and proposes a new solution to the current technical problems in the field of high-voltage and high-power direct heating of molten salt heat storage systems. [ABSTRACT FROM AUTHOR]

In defense-related scenarios, high-energy lasers are of great interest to evoke considerable morphological effects on targets, mainly metals or dielectrics. In addition to vulnerability, aspects such as protective measures and hazard (collateral damage) are of great significance. A measurement set-up will be presented in this paper that allows the time-resolved determination of several parameters during the impact. By detecting the reflected radiation directed into a small solid angle, the main source of hazard for personal harm can be qualitatively measured. The time until breakthrough is determined by measuring the transmitted power using a water-cooled integrating sphere. The surface temperature is registered on the front and back of the samples by pyroelectric measurement. In addition, visual observation is performed by several cameras from different perspectives. For illustration, some exemplary results on aluminum and PMMA will be given. In the case of aluminum, for example, an abrupt perforation of a large-area melt film was observed at the time of breakthrough. In the case of PMMA, a channel with solidified bubbles was formed, starting from the back side, which strongly influenced the transmission. [ABSTRACT FROM AUTHOR]

Highlights • Redefine the heating load patterns with people behavior and temperature related sub-patterns. • GMM clustering algorithm is used for heating load patterns in-depth identification. • A case study with four typical operation patterns is analyzed. • The prediction accuracy improvement with clustering result is proved. Abstract The identification of heating load patterns, also known as load profiles, is of vital importance to effective management and operation of district heating system (DHS). Clustering algorithms have been successfully applied in identifying heating load patterns. In this paper, we propose that the heating load patterns should be analyzed more specifically, and Gaussian Mixture Model (GMM) clustering is selected to extract sub-patterns. The novelty of this paper is that a new GMM clustering is applied to identify temperature related sub-pattern and people behavior related sub-pattern, and the clustering result is further utilized to improve the accuracy of prediction models. An energy station in Tianjin is used as case studies and four typical operation patterns are found with their characteristic of occur time and energy signature, which are defined as working pattern, on-duty pattern, daytime–nighttime pattern and nighttime–daytime pattern. The results reveal that this proposed method can make an in-depth identification of heating load patterns and also proves that the prediction accuracies of regression and artificial intelligence model are significantly improved by utilizing the GMM clustering results. [ABSTRACT FROM AUTHOR]

Highlights • In-situ neutron tomography experiments on heated concrete are used for the first time in this paper to support the results (temperature and 3D moisture distribution) of a fully coupled thermo-hydro-mechanical model. • The influence of the experimental boundary condition on the moisture distribution is explored. • A 3D mesoscale extension of the model taking into account explicitly the concrete components (aggregates, cementitious matrix etc.) is proposed. Abstract The exposure of concrete to fire, which often leads to spalling, is a phenomenon that is not yet fully understood. Given the complexity of the thermo-hydro-mechanical phenomena and their strong interaction, a combined numerical-experimental approach is indispensable for understanding the behaviour of concrete at high temperature with respect to spalling. This paper deals with the numerical modeling of moisture distribution in heated concrete, a parameter often linked with spalling, by making use of original experimental data from in-situ neutron tomography. A homogenized continuum model has been employed for investigating various aspects, such as: speed of the drying front, moisture accumulation behind this front, experimental boundary effects etc. In addition, a fully coupled 3D thermo-hydo-mechanical (THM) model on the mesoscale is presented and used for simulating an experiment with a single aggregate for the purpose of investigating the influence of the aggregate on the drying front. [ABSTRACT FROM AUTHOR]

Abstract The mathematical model of heating substation, which is an important part of district heating system, is presented in this paper. The main components of district heating substation system, from the automatic control perspective, that are observed in this paper are heat exchanger and three-way valve. Presented mathematical model is developed so it could operate in real-time, since the crucial aspect of development, deployment and testing of new control techniques and optimization algorithms is their accurate validation on the real system. However, the accessibility to a real system is often not possible. Corresponding to this, an alternative is the existence of the correct mathematical model of a process, with the ability to operate in real-time. First, the presented mathematical model of district heating substation is decomposed to the simpler models of its main elements, in order to provide the analysis of the various parameters on the model performance through numerical simulation. Second, a real-time model is developed in LabVIEW software package and deployed on the embedded controller with a real-time processor as a three-layer application which enables data acquisition and communication with a programmable controller. [ABSTRACT FROM AUTHOR]

Abstract In this study, a thermal de-icing/anti-icing system for aircraft surfaces was designed using flexible graphene film/paper. It was placed between two Carbon Fiber Reinforced Plastic (CFRP) laminates. The flexible heater paper presents high mechanical and thermal properties and requires up to nine times less power than current known resistance heating pad thermal systems to achieve de-icing. The temperature of the surface was controlled by varying the power input. This allows the monitoring of de-icing time, which can be tuned in less than 10 min. The feasibility of the de-icing performance was demonstrated through a series of experiments and the results indicate that the developed method could be a very promising electrical heating strategy able to maintain lightness, efficiency in the de-icing performance and for reducing the environmental impact of de-icing realized through polluting fluids. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

Highlights • Development of an innovative building integrated air based solar thermal collector. • Dynamic modelling and experimental validation of the whole building/device system. • Solar collectors are integrated in the façades of an office building. • Active/passive effects on the building are analysed for several boundary conditions. • Interesting energy and comfort results are achieved. Abstract In this paper the design and the energy performance investigation of a new flat-plate solar thermal air collector prototype is presented. The adoption of cost-effective materials and simple design solutions represent the main novelties of the proposed device when compared to existing commercial collectors. In addition, the prototype is suitably designed to be integrated into the building envelope (façade), which is a key feature for the market uptake of building integrated solar thermal systems. The paper includes the description of the dynamic simulation model, developed for the energy and economic performance analyses of the whole building-prototype system. The simulation model, implemented with a computer code written in MatLab, is able to predict both active (hot air production for building space heating) and passive (winter free heating and summer overheating) effects due to the building integration of the proposed solar collector. By such tool, indoor comfort investigations can also be carried out. The dynamic simulation models of both the building and the collector prototype were successfully validated. In order to show the features of the developed simulation code, a suitable case study was carried out. It refers to an office space, part of a high-rise multi-use building, simulated as located in three different weather zones (Freiburg, Naples and Almeria). The examined solar collector is modelled as vertically integrated into the building façade, and three different orientations (East, South-East and South) were taken into account. Interesting results for the energetic, economic and occupants comfort points of view are obtained. By taking into account an initial cost of the system of about 5 k€, the primary energy savings achieved by the proposed system against the traditional buildings range between 1.9 and 8.0 MWh/y (depending on the selected weather zone and backup system). The shortest payback for all the investigated weather zones is obtained for Naples, which is equal to 6.2 years. [ABSTRACT FROM AUTHOR]

Abstract Studies show that energy efficiency should play a significant role towards achieving cost-efficient 100% renewable energy systems. One way to attain cleaner heating solutions for the future is to utilise waste heat from power plants and industry through district heating. In the European Union, approximately 50% of thermal energy is lost in conversion processes. This research paper investigates the potential for the implementation of district heating in a future energy system originally intended not to include district heating. The paper approaches the problem by utilising TIMES modelling to frame the initial future energy system. However, MARKAL/TIMES is not ideal to investigate the operation of different heating scenarios, due to a lack of hourly modelling. Hence, the study utilises EnergyPLAN to investigate the implementation of district heating as an alternative heating scenario to individual heating. EnergyPLAN allows for simulating the hourly operation of not only electricity systems but also heating systems. The study investigates the implementation of district heating in the CO2-80 scenario created with the Irish TIMES model. As the Irish CO2-80 scenario does not include district heating, this study uses EnergyPLAN to simulate an 80% reduction scenario, with and without district heating, to compare the operation of the two systems. A sensitivity analysis is included to reflect on the uncertainties of the study. The results show that a district heating solution is more fuel-efficient while more investment heavy, however the fuel savings more than compensates for the increased investments. In total, the district heating scenario is close to 300 M € cheaper in annual costs than the individual heating scenario for Ireland, and achieves a fuel efficiency increase of 3.5% in the whole energy system due to an efficiency increase in the heating sector. The article shows both the relevance of using multiple models, and the need to consider district heating in a future Irish energy system. Highlights • EnergyPLAN is capable of simulating energy systems identified in MARKAL/TIMES. • EnergyPLAN and Markal/TIMES combined enables better analysis of heating scenarios. • District heating can compete with individual heating in an Irish energy system. [ABSTRACT FROM AUTHOR]

Abstract In recent years, with the gradual deterioration of atmospheric environment in China, the "coal to electricity" project is being implemented by the government vigorously. Even though "coal to electricity" project has a positive impact on eliminating serious pollution and promoting clean energy, in the rural areas, the power grid enterprises have less interest in implementing this project. Aimed at this background, the present paper explored the economic benefits generated by the implementation of "coal to electricity" project of grid enterprises. The results indicate that the project carried out by grid company has poor economic performance during its life cycle, which could attributable to its large investment, irrational electricity price mechanism and the big seasonal difference between peak and valley power demand. Finally, based on the conclusion, this paper gives some specific suggestions for grid corporation to increase the economic efficiency of "coal to electricity" project, including reforming the existing irrational price mechanism, taking full advantage of distributed renewable energy and so on. Graphical abstract Image 1 Highlights • "Coal to electricity" project implemented by grid company has a poor economic performance. • The deficiency in economic performance mainly caused by large investment and irrational price mechanism. • Measures need to be taken such as reforming the existing irrational price mechanism or use distributed renewable energy. [ABSTRACT FROM AUTHOR]

Abstract This paper presents analyses of complex flows and heat transfer induced by twin oblique turbulent slot-jets of different directions (divergent, convergent or parallel) impinging a heated wall. A comparison of the heat transfer characteristics between perpendicular and three cases of twin oblique jets (parallel, convergent and divergent). The twin slot jets are located on a confining adiabatic wall at a distance of 8 slot jet width. Convective heat is investigated numerically examining the effect of Reynolds number (Re) and jet inclination angle (α). This problem is relevant to a wide range of practical applications including nuclear engineering devices, manufacturing, material processing, electronic cooling, drying paper or textile, tempering of glass, etc. All computations are performed using two dimensional large eddy simulations (LES) approach with Smagorinsky sub-grid scale (SGS) models. For all directions and inclinations of the jets, the location of the stagnation points is changed and hence, the location and magnitude of the maximum Nusselt number on the heated wall vary. When Reynolds number increases, Nusselt number is improved for all types of inclination. The averaged Nusselt number shows that the perpendicular impingement gives better heat transfer than that of the oblique jets. The poor heat transfer is obtained for the parallel oblique jets. For the same angle, divergent jets give smallest heat transfer than the convergent jets. Graphical abstract Twin-jets flow configuration. (a) Perpendicular (b) Divergent (c) Parallel (d) Convergent. Image 1 Highlights • Four types of flow patterns of impinging twin jet are studied by Large Dissipation Simulation. • Nusselt number is enhanced for all types of inclination for increasing Reynolds number. • Averaged Nusselt number of the perpendicular impingement gives better heat transfer than that of the oblique jets. • The poor heat transfer is obtained for the parallel oblique jets. • For the same angle, divergent jets give smallest heat transfer than the convergent jets. [ABSTRACT FROM AUTHOR]

Abstract The heat integrated water networks have the features of complex network structure and high costs. To overcome the problems, this paper presents a new design method for heat integrated water networks with simple structure and low costs. The proposed method includes two steps: the water-using network is designed first by considering the concentration specifications of water-using processes, which is reflected by the values of concentration potentials. Then, the heat exchanger network can be designed based on the water network obtained in the first step. Self heating strategy is adopted in the design of heat exchanger network to reduce the complexity of the network. Examples are illustrated to demonstrate the effectiveness of the proposed method. The network structures obtained by using the proposed design method are simple, and the total annual costs calculated in this paper are comparable to that of literature results. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new method entitled "BBEE (Building Blocks Energy Estimation)" for estimating energy performance at the district level is proposed. It consists of two branches, namely the BBEE algorithm and an energy database of a typical zone. This method is used to keep architects and urban planners informed of the energy performance of their design by clustering zones and looking up corresponding energy intensities from the database. Because the related energy databases are preexisting, energy performance modeling and simulation are not required from the user. On the other hand, the database is set up and continuously replenished by parameterization simulations with a basic model of typical zones. This task was separated from the estimation process and operated by experts, which shortened the process and reduced modeling errors. As a case study, an urban district with eight buildings was estimated under three climate conditions separately following the proposed BBEE method. Results were compared with the full-model simulation using a simulation tool called DeST. The difference in annual heating demand between estimation and simulation was reported as less than 6% in all three cases. The potential applications and limitations of the proposed BBEE method are discussed at the end of this paper. Although further improvements are still required, the new estimation method could be helpful for energy performance estimation at the district level at the early stage of design. [ABSTRACT FROM AUTHOR]

Abstract The possibilities for detecting defects in multilayer carbon composites occurring as a combination of layers of carbon fiber fabric with thin layers of epoxy resin (CFRP) are presented in this paper. The most common defect in this type of structure is delamination. For non-destructive testing of CFRP samples with deliberately introduced defects, infrared thermography with laser thermal stimulation was used and both computer numerical simulations (using the ThermoCalc program) and experimental tests were carried out. In the experimental testing, a semiconductor laser with a wavelength of 808 nm and a maximum power of 32 W was used for thermal stimulation. Changes of temperature field on the surface of the test sample were recorded with a thermal camera, FLIR 7600 SC. Both pulsed and lock-in thermography methods were used, and this paper presents selected results, together with their analysis. The results presented in this paper show that it is possible to use a laser as a heating source in thermographic non-destructive testing of CFRP composite. [ABSTRACT FROM AUTHOR]

Highlight • New on/off controller for residential applications validated using minute-level field data. • On/off controller improves the HVAC accuracy around 19% in terms of NMBE. • On/off cycling makes the operation and associated energy use more realistic. Abstract Heating, ventilation, and air-conditioning (HVAC) systems are sized using design conditions, conditions that occur only a small percentage of time each year. As a result, HVAC systems frequently operate at part-load conditions since their capacity is larger than necessary except in the most extreme weather conditions. Most HVAC systems for residential and small commercial buildings in the U.S. use on/off controls, however, most energy modeling software tools do not simulate the on/off nature of this type of HVAC controls. This paper presents the development and validation of an on/off controller for residential applications in EnergyPlus using a custom EMS (energy management system). This controller is validated using minute-level field data collected for a house located in Sacramento, California with simulated occupancy and internal loads. The results obtained from EnergyPlus with and without the use of the developed on/off controller are also compared. The application of the developed on/off controller improves the HVAC energy use results accuracy around 19% in terms of the Normalized Mean Bias Error (NMBE) at the one-minute level compared to the results without the application of the on/off controller. It also makes the nature of operation and associated energy use signal of the HVAC system more realistic in terms of the on/off nature of the residential direct expansion coils. Furthermore, in this paper, the accuracy improvement of the results dominated by the internal loads is investigated by applying the one-minute schedule compared to the usual hourly schedules for the internal thermal loads. [ABSTRACT FROM AUTHOR]

Highlights • A new experimental procedure to test PCM-enhanced components is presented. • A modified Dynamic Heat Flow Meter Apparatus is used to perform the tests. • The thermal conductivity of PCMs is measured in different phases (solid-liquid). • Indications to solve the issues related to building PCM-specimens are provided. Abstract The implementation, in Building Performance Simulations (BPS) tools, of robust models capable of simulating the thermophysical behaviour of a Phase Change Material (PCM) represents a fundamental step for an appropriate thermal evaluation of buildings that adopt PCM-enhanced envelope components. Reliable and robust measuring procedures are essential, at a material and component level, to provide experimental data for the empirical validation of software tools. The traditional laboratory tests that are generally used for the validation of models present some limitations, because PCMs are usually subjected to conditions that may be very different from the real boundary conditions of the building components in which PCMs are applied. Furthermore, in many experimental full-scale mockups, the relatively small quantity of installed PCM and the combination of several thermal phenomena do not allow software tools to be tested in a reliable way. In this paper, an experimental procedure, based on a modified Heat Flow Meter Apparatus, has been developed to test the behaviour of PCM-enhanced components; the procedure, which is based on the measurement of the sinusoidal response, has been set up to provide data for the comparison and testing of numerical models and of BPS tools. Moreover, general indications and guidelines are provided to solve some issues related to building specimens that contain bulk PCM in order to obtain a more accurate measurement of their performance. The experimental results presented in this paper were obtained from two different bulk PCMs (organic and inorganic). It was found that it is important to evaluate different PCM typologies and different thermophysical boundary conditions, including partial and full phase transitions, to test simulation codes that implement PCM modelling functions. In fact, some phenomena, such as hysteresis and subcooling effects are more evident when partial phase transition takes place. The results related to the characterization of the thermal conductivity of a paraffin-based PCM have shown a significant increase (up to 42%) of the equivalent thermal conductivity from a solid to a liquid state, with an upward heat flux, thus highlighting that further investigations and improvements are needed to measure the equivalent thermal conductivity in the different PCM phases. [ABSTRACT FROM AUTHOR]

Highlights • Photovoltaic powered ventilation system for parked electric car. • Solar photovoltaic car roof charging system for parked car's ventilation. • Photovoltaic powered electro-mechanical system for climate control of parked electric car. Abstract This paper explores the use of electric car roof mounted photovoltaic (PV) powered ventilation for cabin heat management/ventilation. A fully-functional Renault Zoe electric car has been used for performance evaluation of parked electric car ventilation directly powered by PV. This work has been part of a research project of testing electric cars in the urban environment and it has been conducted at the Edinburgh Napier University's Transport Research Institute. In this work, parked electric car ventilation has been reported, when roof-mounted PV modules were used to operate DC powered fans for ventilation. It has been found that the DC motor-fan selection for removing the hot air from cabin space is of important (i.e. operating characteristics of motor-fan have been very close to the maximum power points locus of the PV modules at different solar insolation). The electric car cabin temperature models with reference to solar radiation have been presented and it will be useful for knowing the parked car cabin temperature variations and designing the car roof mounted PV powered ventilation system. In this paper, performance evaluation of parked electric car's cabin heat management is presented and analysed. Results from this work will be helpful for designing electric car ventilation system (operating at parking time), when powered by vehicle roof integrated PV modules. [ABSTRACT FROM AUTHOR]

Highlights • The ITER TBM Program plans to test DEMO-relevant tritium breeding blanket systems. • Each TBS is formed by various sub-systems with different functions and technologies. • For each TBS sub-system, description of its functions and its main components. • Identification of the ITER services required for operating each TBS sub-system. • Helium and water coolant systems, tritium extraction systems, Pb-16Li systems, I&C. Abstract Six Test Blanket Systems (TBS) will be installed and operated in ITER in the framework of the ITER Test Blanket Module (TBM) Program. After a short description of each TBS and of the main operating parameters, this paper gives the main features of the various TBSs sub-systems, in particular, the cooling systems (helium, water and/or lithium-lead), the tritium system for extracting the tritium from the breeder, the coolant purification system, the tritium accountancy system, and several instrumentation & control systems. This paper addresses also the main services that are needed to operate a TBS such as the secondary water cooling system, the chilled water system to cool the helium circulators, the liquid and gaseous nitrogen used in the tritium extraction process, the compressed air needed to operate the various valves present on the circuits, and the electrical power supply for all the active electrical components such as pumps, circulators and heaters. Other important aspects are the required interfaces with other ITER plant systems such as those involving air and process gas detritiation, power supply, radiological and environmental monitoring, remote handling, measurements, and services. [ABSTRACT FROM AUTHOR]

Solar-based radiant heating systems represent a sustainable, and relatively low-cost, technology to raise the temperature of the interior thermal mass of our buildings. Through the use of direct absorption solar thermal collectors, the same working fluid which absorbs the solar energy can be used to transfer the energy for storage in the thermal mass of the structure using a network of pipes embedded in concrete floors. This study investigates a promising working fluid which can be used in such systems – one which is based on multi-walled carbon-nanotubes suspended in normal base fluids. A major stumbling block affecting the wide spread use of carbon-nanotube nanofluids is their low dispersion stability at elevated temperatures, which significantly reduces the absorption capabilities of the nanofluids and could lead to clogging of the pumps used to circulate the fluids. In this paper, we report on a scalable UV-ozone (UVO) treatment technique to produce highly stable dispersions for the elevated temperatures experienced by working fluids in radiant heating systems. To probe suitability of UVO treated multi-walled carbon-nanotube (MWCNTs) for solar-assisted radiant heating systems, this paper investigates the effects of exposure time and temperature on stability, optical absorbance properties, the extent of functionalisation, and the photothermal conversion performance of UVO-treated MWCNT nanofluids. No agglomeration or degradation of the MWCNTs was observed at elevated temperatures (up to 150   °C), highlighting the stability of proposed nanofluids. [ABSTRACT FROM AUTHOR]

In this paper, the problem of dispatching the operation of a distribution feeder comprising a set of heterogeneous resources is investigated. The main objective is to track a power trajectory, called the dispatch plan , which is computed the day before the beginning of operation. In this paper, we propose a method to optimally compute the dispatch plan so as to optimize the operation of the feeder while making sure to allocate enough local reserves to absorb deviations of the realizations. Indeed, during real-time operation, due to the stochasticity of part of the resources in the feeder portfolio, tracking errors need to be absorbed in order to track the committed dispatch plan. This is achieved by modulating the power consumption of a utility-scale battery energy storage system and of the heating, ventilation and air conditioning system of a commercial controllable building. To this end, a hierarchical controller is designed to coordinate these two controllable entities while requiring a minimal communication infrastructure. Due to the inherent different response times of these systems, the power injection of the electrical battery is controlled at a sub-minute time-scale so as to absorb high-frequency tracking errors and, therefore, deliver the dispatch service. At a slower time-scale, the controllable building is controlled to maintain the state of charge for the electrical battery at a scheduled level by means of a model predictive controller. The model predictive controller is designed in order to account for both comfort and operational constraints of the controllable building, as well as power limits for the electrical storage. The effectiveness of the proposed control framework is demonstrated by means of both an extensive simulation analysis, as well as a set of 12 full day experimental results on the 20 kV distribution feeder in the campus of the Swiss institute of technology in Lausanne, that is comprised of: 1) A set of uncontrollable resources represented by five office buildings (350 kWp) and a roof-top photovoltaic installation (90 kWp), 2) a set of controllable resources, namely, a grid-connected electrical storage (720 kVA–500 kWh), and a fully-occupied multi-zone office building (45 kWp). [ABSTRACT FROM AUTHOR]

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD. • This paper reviews data-driven fault detection and diagnostics studies for HVAC systems. • Papers are collected by Sub-keyword Synonym Subtopics Searching method. • Papers are reviewed regarding FDD process, systems studied, and evaluation metrics. • Ongoing efforts, gaps, and challenges are identified. [ABSTRACT FROM AUTHOR]

This paper presents a laboratory study of Offline-trained Reinforcement Learning (RL) control of a Heating Ventilation and Air-Conditioning (HVAC) system. We conducted the experiments on a radiant floor heating system consisting of two temperature zones located in Denmark. The buildings are subjected to real-world weather. A previous paper describes the algorithm we tested, which we summarize in this paper. First, we present a benchmarking test which we conducted during spring 2021 and winter 2021/2022. This data is used in the Offline RL framework to train and deploy the RL policy, which we then tested during winter 2021/2022 and spring 2022. An analysis of the data shows that the RL policy showed predictive control-like behavior, and reduced the oscillations of the system by a minimum of 40%. Additionally, we show that the RL policy is minimum 14% more cost-effective than the traditional control policy used in the benchmarking test. • Real-world data from laboratory test with underfloor heating. • Offline Multi-Agent Reinforcement Learning can eliminate poor behavior during training while converging. • Long Short Term Memory layers are an effective method for obtaining training models for Reinforcement Learning. • Heating costs are reduced by approximately 15% and comfort is maintained. [ABSTRACT FROM AUTHOR]

In an effort to minimise electricity consumption and greenhouse gases emissions, the heating, ventilation and air-conditioning sector has focused its attention on developing alternative solutions to electrically-driven vapour-compression cooling. Liquid desiccant air-conditioning systems represent an energy-efficient and more environmentally friendly alternative technology for dehumidification and cooling, particularly in those cases with high latent loads to maintain indoor air quality and comfort conditions. This technology is considered particularly efficient in hot and humid climates. As a matter of fact, the choice of the desiccant solution influences the overall performance of the system. The current paper reviews the working principle of liquid desiccant systems, focusing on the thermodynamic properties of the desiccant solutions and describes an evaluation of the reference thermodynamic properties of different desiccant solutions to identify which thermodynamic, physical, transport property influences the liquid desiccant process and to what extent. The comparison of these thermodynamic properties for the commonly used desiccants is conducted to estimate which fluid could perform most favourably in the system. The economic factors and the effect of different applications and climatic conditions on the system performance are also described. The paper is intended to be the first step in the evaluation of alternative desiccant fluids able to overcome the problems related to the use of the common desiccant solutions, such as crystallization and corrosion to metals. Ionic liquids seem a promising alternative working fluid in liquid desiccant air-conditioning systems and their characteristics and cost are discussed. [ABSTRACT FROM AUTHOR]

Many applications in additive manufacturing involve the dynamic deposition of powders and the in-flight heating of such material by a laser. In order to characterize such systems, the Discrete Element Method (DEM) is employed. Specifically, this paper focuses on the initial stages of this process by developing a modular discrete-element type multiphysics simulation method for the particle dynamics and heating by a laser and detailed thermal behavior. The objective is to provide researchers with a framework to construct computational tools for this growing industry. In order to achieve this, from a simulation standpoint, the overall particle-mixture system is constructed by coupling submodels for each primary physical process (dynamics and heating) together. An iterative staggering scheme is developed whereby, within every time step, each individual particle is solved “freezing” the state of the remaining multiparticle system. The state of the particle is then updated and the algorithm moves to the next particle in the system and the process is repeated. The overall process sweeps through the entire system repeatedly until convergence is achieved in an appropriate norm. As the system evolves, an error estimate dictates the time-step size that is needed to induce convergence to below an appropriate error level. Thus, the process can be considered as an implicit time-stepping scheme, which is combined with an (internal) iterative staggering process. In order to control rates of convergence within a time-step, the algorithm adjusts the time-step size. If the iterative process does not converge within a desired number of iterations, below an error tolerance, the time-step is reduced. The degree of time-step reduction is determined by utilizing an estimate of the spectral radius of the coupled system. Since the construction of model and solution process is modular, one can easily replace physical submodels with other choices, making it easy to numerically experiment with a variety of models. Qualitative and quantitative analyses are provided, as well as three-dimensional numerical examples. This paper addresses the first of two overall “macrostages” of a complex manufacturing process with DEM. The first stage, addressed in this paper, concentrates on dry-powder materials, which are of a discrete particulate character, using DEM to characterize the deposition and in-flight particle heating. Other papers of the author (Zohdi, 2010, 2013, 2014, 2015 [81,82], 2008) have addressed stage two, namely phase transformations, curing (cooling) and stress analysis, using more appropriate continuum and hybrid DEM-continuum approaches. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air-conditioning (HVAC) are among the major energy demand in the buildings sector globally. Improving the energy efficiency of such systems is a critical objective for mitigating greenhouse gas emissions and transitioning towards renewable sources of energy supply. The interest of this paper is to explore means to increase the efficiency of HVAC systems in accommodating occupants’ behavior in real time. For instance, rooms in office buildings are not always occupied by occupants during scheduled HVAC service periods. This offers an opportunity to reduce unnecessary energy demands of HVAC systems associated with occupants’ behavior. An in-depth analysis of occupants’ stochastic behavior within an office building is conducted in this paper. A demand-driven control strategy is proposed that automatically responds to occupants’ energy-related behavior for reducing energy consumption and maintains room temperature for occupants with similar performances as a static cooling. In this control strategy, two types of machine learning methods – unsupervised and supervised learning – are applied to learn occupants’ behavior in two learning processes. The occupancy-related information learned by the algorithms is used by a set of specified rules to infer real-time room setpoints for controlling the office's space cooling system. This learning-based approach intends to reduce the need for human intervention in the cooling system’s control. The proposed strategy was applied to control the cooling system of the office building under real-world conditions. Eleven case study office spaces were selected, representing three typical office uses: single person offices, multi-person offices, and meeting rooms. The experimental results report between 7% and 52% energy savings as compared to the conventionally-scheduled cooling systems. [ABSTRACT FROM AUTHOR]

In the past decades, increasing attention has been directed to understanding the structural response to various scenario fires, which is stimulated by the trend of considering localised burning in large open plan compartments. Beam-column elements as widely used in modelling steel frames have been extended to account for the thermal impact in OpenSees, the development of which is undertaken to formulate the displacement and force-based beam elements. After including the non-uniform thermal expansion along the element length caused by localised heating, the force-based beam-column element has shown greater efficiency compared to the displacement-based element. A detailed discussion regarding the computational performance of both beam-column elements has been presented in this paper, which concludes that force-based beam-column elements can better represent the localised plasticity of fibres caused by non-uniform heating. A generic simply supported steel beam can be modelled even with two force-based elements to reproduce accurate mid-span deflection under localised heating. A steel beam subjected to a localised fire is presented as a case study in this paper, experiencing run-away collapse after 15 min of exposure when the load ratio reaches 50%. Using a 12 element force-based beam-column model and shows equivalent accuracy against 48 element displacement-based beam-column model. [ABSTRACT FROM AUTHOR]

Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat demand rises and heat pump efficiencies fall. This paper presents the development and field demonstration of a predictive control system for an air-to-air heat pump with backup electric resistance heat. The control system adjusts indoor temperature set-points based on weather forecasts, occupancy conditions, and data-driven models of the building and heating equipment. Field tests from January to March of 2023 in an occupied, all-electric, 208 m 2 detached single-family house in Indiana, USA, included outdoor temperatures as low as − 15 ° C. On average over these tests, the control system reduced daily heating energy use by 19% (95% confidence interval: 13%–24%), energy used for backup heat by 38%, and the frequency of using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. The control system could reduce the house's total annual heating costs by about $300 (95% confidence interval: 23%–34%). These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale. • First field deployment of predictive control for an air-to-air heat pump in a cold climate. • Review of all known field studies of supervisory control for residential HVAC. • Reduced heating energy use by 13%–24% and use of highest-stage backup heat by 83%. • Occupants reported similar thermal comfort to benchmark control system. • Discussion of deployment labor, costs, challenges, and scalability. [ABSTRACT FROM AUTHOR]