Search

In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

Jun Huang,1,2 Chunjie Fan,2 Yindong Ma,2 Guobao Huang2 1Department of Clinical Medicine, Shandong Second Medical University (Weifang Medical University), Weifang, 261000, People’s Republic of China; 2Department of Burns and Reconstructive Surgery, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, People’s Republic of ChinaCorrespondence: Guobao Huang, Department of Burns and Reconstructive Surgery, Central Hospital Affiliated to Shandong First Medical University, No. 105, Jiefang Road, Lixia District, Jinan, Shandong Province, 250013, People’s Republic of China, Tel +86 531-55865707, Email huangguobao@sdu.edu.cnAbstract: Exploring the critical role of thermal dynamics in wound healing, this manuscript navigates through the complex biological responses initiated upon wound infliction and how temperature variations influence the healing trajectory. Integrating biothermal physics, clinical medicine, and biomedical engineering, it highlights the significance of thermal management in wound care, emphasizing the wound microenvironment’s division into internal and external domains and their collaborative impact on tissue repair. Innovations in real-time wound temperature monitoring, especially through intelligent wireless sensor dressings, are spotlighted as transformative, enabling precise wound condition management. The text underscores the necessity for further research to elucidate thermal regulation’s molecular and cellular mechanisms on healing processes. It advocates for standardized protocols for localized heating treatments, integrating them into personalized wound care strategies to enhance therapeutic outcomes, improve patient well-being, and achieve cost-effective healthcare practices. This work presents a forward-looking perspective on refining wound management through sophisticated, evidence-based interventions, emphasizing the interplay between thermal dynamics and wound healing.Keywords: wound healing, thermal dynamics, wound microenvironment, intelligent dressings, localized heating treatment

In the paper, a model of a heated building using a PEM (proton exchange membrane) fuel cell is presented. This work introduces a novel and more comprehensive depiction of the thermal processes occurring within a fuel cell under transient conditions. The developed PEM fuel cell model was synergistically incorporated with a thermodynamic model of a building. The resulting mathematical framework provides insights into the building's performance concerning fluctuating ambient temperatures and the heating system powered by the PEM cell. The developed mathematical model delineates the interplay between the building's thermodynamics and the fuel cell in the context of the devised heating control system featuring an indirect heat distribution mechanism. [ABSTRACT FROM AUTHOR]

A new cleaner micron-sized water-based powder particles (DW) were prepared by physical dispersion method of hydrophobic silica and ultrapure water at different proportions. The effects of chemical composition of metal salts on physical properties of dry water materials such as fluidity and particle size distribution were analyzed. The microscopic morphology of DW particles with different components was observed by optical microscope, and the effects of different filling pressures on their microscopic morphology and fire extinguishing efficiency were analyzed. The thermal decomposition kinetics of different DW were analyzed to get their thermal mechanism to extinguish. The results showed that the size of DW particles is more uniform; the structure edge is clearer, and the structure is more stable when the mass ratio of SiO2 to water is 1:13. The fluidity and filling performance of DW were improved by different metal salinization components at a certain concentration. When the filling pressure is 0.6 MPa, the DW structure can remain stable and the fire extinguishing efficiency is the best. DW material can instantly extinguish the small-size oil pool fire. DW shows the best extinguishing efficiency compared to dry powder and water mist. Combined with the thermal decomposition analysis, the addition of metallic sodium salt would increase the apparent activation energy by more than 10 times; therefore, the fire extinguishing efficiency of sodium carbonate-modified DW shows the most obvious. [ABSTRACT FROM AUTHOR]

Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections. Plain Language Summary: Climate change has caused substantial vegetation growth that was detected by satellite observations ("greening") over northern permafrost regions. However, the consequences or feedbacks of vegetation greening remain largely unknown, hindering the understanding of near‐surface thermal dynamics and bringing considerable uncertainty in model projections. Here we aimed to decipher the biophysical impact of permafrost greening on the summer surface offset (SSO), which is an indicator of permafrost degradation. We found latitude‐ and biome‐dependent patterns of SSO changes and divergent responses of SSO to greening. Increases in satellite‐observed leaf area index lead to higher SSO in relatively cold and wet areas but lower SSO in warm‐dry regions. Biophysical mechanisms associated with surface albedo, evapotranspiration, and SM can help explain various effects of greening on SSO. Our results highlight greening feedbacks on the thermal dynamics of permafrost with climate warming, calling for the improvement of current projections. Key Points: Vegetation greening impacts the thermal dynamics of permafrost surfaceBiophysical effects of greening on surface offset could be related to surface albedo, evapotranspiration, and soil moisture [ABSTRACT FROM AUTHOR]

Turtle nesting habitat can be created as a restoration strategy to increase habitat availability or provide suitable habitat away from threats. Traditional nest habitat restoration consists of creating nesting mounds using a mix of sand and gravel. However, nesting mounds do not resemble natural turtle nesting habitat in a rock barren landscape where turtles nest in crevices and cracks in the bedrock. Therefore, our objective was to design and evaluate the success of a landscape‐appropriate design for turtle nesting habitat in a rock barren landscape. To evaluate success of the nest habitat design, we assessed the (1) survival of transplanted moss and lichen cover on created nest sites, (2) ecohydrological and physical conditions at created and natural sites, and (3) turtle egg hatching success at created and natural sites using a split‐clutch experiment. We found no difference in productivity between lichen transplants and natural sites, indicating that intact lichen transplants were successful. Moss transplant success was more variable due to moisture stress because transplants were conducted during dry conditions. In general, created nest habitat tended to have a more stable thermal and moisture regime compared to natural sites. When accounting for maternal effects, the odds of an egg hatching successfully was 6.6 times higher in a created site than a natural site. Overall, the success of our nest habitat design in the first few years suggests that this landscape‐appropriate design will be a useful restoration strategy for increasing turtle nesting habitat in rock barren landscapes. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications. Plain Language Summary: Lake thermal dynamics are fundamental in controlling mixing processes and have significant implications for biological and geochemical processes. Consequently, the impacts of climate change on these dynamics can have severe consequences for the health of lakes and their aquatic ecosystems. In this context, mathematical models are essential for understanding the potential effects of future climate change on lake thermal dynamics and related processes. This manuscript offers a comprehensive review of lake water temperature modeling. It covers the fundamental physical concepts that govern thermal dynamics in lakes and provides an overview of various sources of observational water temperature data, including in situ monitoring and satellite data used in these models. The study evaluates different types of lake water temperature models, including statistical, process‐based, and hybrid models. It explores emerging modeling approaches such as forecasting, digital twins, combining process‐based modeling with deep learning, ensemble modeling, and climate‐lake models coupling. Model performance is also discussed, highlighting suggested evaluation metrics and providing a comprehensive analysis of the state‐of‐the‐art optimization methods to assess model accuracy. This review targets researchers in limnology, hydrology, ecology, biology, physics, engineering, and remote sensing from the private and public sectors interested in lake water temperature modeling and its applications. Key Points: Lake thermal dynamics are central in shaping mixing processes and the health of aquatic ecosystems, and climate change alters these dynamicsMathematical models are essential to understand past and project future climate change impacts on lake thermal dynamicsThis study reviews lake water temperature modeling, covering concepts, data sources, and model evaluation for applications across disciplines [ABSTRACT FROM AUTHOR]

The tight interactions between energy sectors increase the risk of affecting integrated energy systems (IESs); therefore, reliability assessment is important for identifying vulnerability and improving security in IESs. In the literature, the network topology, transmission loss, and distinguishing thermal dynamics in IESs are seldom addressed for reliability assessment, rendering the results unreliable. To bridge this gap, an integrated electricity and district cooling model is formulated for IESs that involves elaborate network topologies and transmission losses to reflect their impacts on reliability characteristics. In addition, the evaluation of reliability indices for district cooling systems is improved by considering the thermal inertia characteristics of buildings and the transportation delay of circulating water to characterize the reliability levels of district cooling systems more accurately. Numerical results using a 12-node case validate the improved accuracy of the proposed reliability assessment method and reveal the potential of district cooling systems for enhancing the reliability of power systems.

Abstract Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections.

The district heating system (DHS) consumes a lot of energy in winter, and its control accuracy needs to be improved urgently. To apply advanced process control (APC) in DHS, the thermal dynamic model of the existing buildings is essential. This paper uses the subspace method which is a data-driven approach for modelling the thermal dynamics of the building. The model's performance is analyzed using the collected data, and the differences compared to the classical methods are also analyzed. The method reduces the RMSE by about 20% compared with the ARX model for the same complexity. Subsequently, the analysis of the training residuals indicates that the estimate of periodic intra-building disturbance can be obtained by minimizing the training residuals. By introducing the estimated disturbance function, the RMSE on the test set is further reduced by 26%. At the end of the article, a simple parameter extrapolation experiment is conducted, and the result shows that the parameters can be extrapolated to other buildings without large errors. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Viticulture & Enology / Ciência e Técnica Vitivinícola is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wine maturation consists of wine being stored in vessels after alcoholic fermentation and malolactic fermentation and before bottling. This is a key period during the wine’s evolution, with changes in the physicochemical and sensory characteristics, especially in the colour, aroma, flavour and mouthfeel. Traditionally, oak barrels have been the main vessel choice for the maturation period. However, due to new technology and increasing consumer interest, winemakers are now using different vessels which vary in material, shape and size to mature their wines. Despite this gain in popularity, there are key questions remaining regarding how exactly variations in maturation vessel physical parameters affect wine quality during maturation. This review summarises how variations to maturation vessel material, size, and shape influence wine quality, specifically regarding oxygen permeation, thermal conductivity, and the release of exogenous compounds. Overall, the vessel material and size have a significant influence over the oxygen transfer rate of the vessel, and the associated changes to colour, aroma and mouthfeel, which occur with oxygen ingress. Maturation vessels may be conceptually viewed on a scale of ‘inertness’, with more inert vessels, such as stainless-steel tanks, generally having higher thermal conductivity, lower oxygen transfer rate and less release of exogenous compounds, with the opposite being true for the more active vessels such as oak barrels. Finally, this review points out gaps in the literature such as how maturation vessel parameters influence lees-wine interaction. This review aims to lay out paths for future research needed to shed light on how maturation vessel choice affects wine quality.

The essential task of integrated electricity-heat systems (IEHSs) is to provide customers with reliable electric and heating services. From the perspective of customers, it is reasonable to analyze the reliabilities of IEHSs based on the ability to provide energy services with a reasonable assurance of continuity and quality, which are termed as service-based reliabilities. Due to the thermal inertia existing in IEHSs, the heating service performances can present slow dynamic characteristics, which has a great impact on the service satisfaction of customers. The neglect of such thermal dynamics will bring about inaccurate service-based reliability measurement, which can lead to the inefficient dispatch decisions of system operators. Therefore, it is necessary to provide a tool which can analyze the service-based reliabilities of IEHSs considering the impacts of thermal dynamics. This paper firstly models the energy service performance of IEHSs in contingency states. Specifically, the nodal energy supplies are obtained from the optimal power and heat flow model under both variable hydraulic and thermal conditions, in which the transmission-side thermal dynamics are formulated. On this basis, the energy service performances for customers are further determined with the formulation of demand-side thermal dynamics. Moreover, a service-based reliability analysis framework for the IEHSs is proposed utilizing the time-sequential Monte Carlo simulation (TSMCS) technique with the embedded decomposition algorithm. Furthermore, the indices for quantifying service-based reliabilities are defined based on the traditional reliability indices, where dynamic service performances and service satisfactions of customers are both considered. Numerical simulations are carried out with a test system to validate the effectiveness of the proposed framework.

Abstract The penetration of renewable energies is increasing in energy systems worldwide. Consequently, the intermittency of the energy sources raises technical challenges for sustainable energy supply. Demand-side flexibility is an effective solution to counterbalance renewable power fluctuations. In the residential sector, electrical heat pumps exhibit great flexibility potential. In this paper, a novel approach is proposed to generate FlexOffers for individual heat pumps considering the uncertain nature of weather conditions. To achieve the aim, firstly, the thermal dynamic model of residential buildings is presented mathematically. The model addresses different temperature zones. The constant coefficients of the thermal dynamics are estimated using Continuous-Time Stochastic Model (CTSM) in R software. Afterward, the building model is integrated with a success function to generate FlexOffers. The success function is comprised of two objective functions including minimization and maximization of the energy consumption of heat pumps. The FlexOffers are generated considering the existing gap between the minimum and maximum energy consumption. The patterns of energy consumption are defined based on occupants’ thermal comfort temperature setpoint. The FlexOffers are programmed in UPPAAL-STRATEGO software. Finally, a high-fidelity building model with four rooms is used to examine the proficiency of the suggested approaches. The simulation results confirm that the proposed method generates flexibility potentials for the upstream network in both optimistic and pessimistic states of energy consumption patterns.

Heat transfer and phase change in multiphase media involve complex behavior such as multi-physics coupling, unsteady interfaces, and boundary generation/disappearance. The smoothed particle hydrodynamics (SPH) method is used to simulate multicomponent problems (initially solid material surrounded by a fluid) characterized by heat transfer, solid–liquid phase change, and multiphase flow. First, the interface behavior of the melted liquids and ambient fluids are simulated by a multiphase SPH model, where corrected algorithms and techniques are adopted to provide a stable simulation for the interface with a large density ratio. The wetting effect of the solid surface by melted liquids is considered by extending the continuum surface force model to three phases including solid, melted liquid and ambient fluid. Second, a thermal dynamics model is incorporated with the multiphase SPH model. The solid–liquid phase change occurs in a small temperature interval in which the latent heat is incorporated using the equivalent heat capacity. Third, the numerical model is validated using several benchmark examples. Five specially configured cases are simulated to demonstrate the robustness, adaptability, and stability of the model. The intriguing phenomena of evolution of the fluid interface of melted liquids, accumulation of thin-layer liquids, and melted droplet formation are reproduced.

Laser powder bed fusion (LPBF) is a promising additive manufacturing technique that allows layerby- layer fabrication of metallic powders. Distinguished thermal dynamics result in the microstructure of intrinsic features. This review aims to provide a thorough insight into the solidification fundamentals and the microstructural tailoring during LPBF. It begins with the introduction of the LPBF and the challenges of its applications with aluminium alloys. The thermal dynamics and its influence on the microstructure in LPBF were thereafter discussed. The attempts to tailor microstructures by refinement in LPBF fabrication of various aluminium alloys were summarised. Finally, the review provides a conclusive remark on the microstructural controlling and an outlook on the remaining challenges and potential research topics in LPBF with Al alloys. [ABSTRACT FROM AUTHOR]

In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km ^2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

The penetration of renewable energies is increasing in energy systems worldwide. Consequently, the intermittency of the energy sources raises technical challenges for sustainable energy supply. Demand-side flexibility is an effective solution to counterbalance renewable power fluctuations. In the residential sector, electrical heat pumps exhibit great flexibility potential. In this paper, a novel approach is proposed to generate FlexOffers for individual heat pumps considering the uncertain nature of weather conditions. To achieve the aim, firstly, the thermal dynamic model of residential buildings is presented mathematically. The model addresses different temperature zones. The constant coefficients of the thermal dynamics are estimated using Continuous-Time Stochastic Model (CTSM) in R software. Afterward, the building model is integrated with a success function to generate FlexOffers. The success function is comprised of two objective functions including minimization and maximization of the energy consumption of heat pumps. The FlexOffers are generated considering the existing gap between the minimum and maximum energy consumption. The patterns of energy consumption are defined based on occupants' thermal comfort temperature setpoint. The FlexOffers are programmed in UPPAAL-STRATEGO software. Finally, a high-fidelity building model with four rooms is used to examine the proficiency of the suggested approaches. The simulation results confirm that the proposed method generates flexibility potentials for the upstream network in both optimistic and pessimistic states of energy consumption patterns. [ABSTRACT FROM AUTHOR]

Heat transfer and phase change in multiphase media involve complex behavior such as multi-physics coupling, unsteady interfaces, and boundary generation/disappearance. The smoothed particle hydrodynamics (SPH) method is used to simulate multicomponent problems (initially solid material surrounded by a fluid) characterized by heat transfer, solid–liquid phase change, and multiphase flow. First, the interface behavior of the melted liquids and ambient fluids are simulated by a multiphase SPH model, where corrected algorithms and techniques are adopted to provide a stable simulation for the interface with a large density ratio. The wetting effect of the solid surface by melted liquids is considered by extending the continuum surface force model to three phases including solid, melted liquid and ambient fluid. Second, a thermal dynamics model is incorporated with the multiphase SPH model. The solid–liquid phase change occurs in a small temperature interval in which the latent heat is incorporated using the equivalent heat capacity. Third, the numerical model is validated using several benchmark examples. Five specially configured cases are simulated to demonstrate the robustness, adaptability, and stability of the model. The intriguing phenomena of evolution of the fluid interface of melted liquids, accumulation of thin-layer liquids, and melted droplet formation are reproduced. [ABSTRACT FROM AUTHOR]

In actual distributed parameter systems (DPSs), each spatial point has nonlinear energy transfer with its local neighbor points. Also, this energy transfer is affected by the past states. However, these properties are often ignored by most of modeling methods, which causes these methods ineffective in modeling of DPSs. Aiming for this problem, a spatiotemporal kernel-local-embedding (STKLLE) Modeling approach is proposed here to reconstruct the nonlinear spatiotemporal dynamics of DPSs. First, in order to present the complex dynamics on space, a STKLLE strategy is developed to extract space basis functions (SBFs). On the one hand, this STKLLE method represents the energy transfer relation with its neighboring points and maintains this local relation in model. On the other hand, it considers the influence of the adjacent past states to the current state. Then, using T–S fuzzy algorithm, a temporal model is designed to represent the temporal dynamics of DPSs in each sampling period. Integrating these SBFs and the temporal fuzzy model, a spatiotemporal model is constructed to well present and predict the nonlinear spatiotemporal dynamics in DPSs. Through the actual experiment on Lithium-ion batteries and heating oven, the effectiveness of this proposed model is detailly verified, and quantitative comparisons with several data-driven modeling algorithms are further carried out to demonstrate the model efficiency. • A spatiotemporal KLLE method is designed to extract nonlinear relations of the local neighbor points and adjacent states. • A T-S fuzzy temporal model is developed to construct the temporal dynamics of DPSs. • A spatiotemporal model is developed to reconstruct the spatiotemporal dynamics of DPSs. • This proposed method is verified by theoretical analysis and experimental studies. [ABSTRACT FROM AUTHOR]

Localized plasmons formed in ultrathin metallic nanogaps can lead to robust absorption of incident light. Plasmonic metasurfaces based on this effect can efficiently generate energetic charge carriers, also known as hot electrons, owing to their ability to squeeze and enhance electromagnetic fields in confined subwavelength spaces. However, it is very challenging to accurately identify and quantify the dynamics of hot carriers, mainly due to their ultrafast time decay. Their nonequilibrium temperature response is one of the key factors missing to understand the short time decay and overall transient tunable absorption performance of gap-plasmon metasurfaces. Here, we systematically study the temperature dynamics of hot electrons and their transition into thermal carriers at various timescales from femto to nanoseconds by using the two-temperature model. Additionally, the hot electron temperature and generation rate threshold values are investigated by using a hydrodynamic nonlocal model approach that is more accurate when ultrathin gaps are considered. The derived temperature dependent material properties are used to study the ultrafast transient nonlinear modification in the absorption spectrum before plasmon-induced lattice heating is established leading to efficient tunable nanophotonic absorber designs. We also examine the damage threshold of these plasmonic absorbers under various pulsed laser illuminations, an important quantity to derive the ultimate input intensity limits that can be used in various emerging nonlinear optics and other tunable nanophotonic applications. The presented results elucidate the role of hot electrons in the response of gap-plasmon metasurface absorbers which can be used to design more efficient photocatalysis, photovoltaics, and photodetection devices.

The broad development of integrated heat and electricity systems (IHES) improved the energy utilization efficiency, but it also increased the risk of cascaded accidents and the difficulty of operation. The security region that defines the permitted operation range is efficient for the planning and control of IHESs, while the accurate formulation of security region boundary renders its applications. To address this problem, this article first proposes a novel equivalent thermal model (ETM) to build the direct connection between the operations states and the control variables in the heating system. The ETM directly characterizes the network response to the inputs and accurately describes the dynamic process in the heat system. On this basis, the method to construct the security region for IHES is presented considering the thermal dynamics, where the critical boundary is formulated with a set of accurate hyperplanes. To describe the thermal dynamics, numerical simulations from different aspects verify the effectiveness of the ETM. The results of security region indicate that the thermal dynamics influence the operation security and renewable penetration in the power system significantly.

Lakes are classified by thermal mixing regimes, with shallow waterbodies historically categorized as continuously mixing systems. Yet, recent studies demonstrate extended summertime stratification in ponds, underscoring the need to reassess thermal classifications for shallow waterbodies. In this study, we examined the summertime thermal dynamics of 34 ponds and shallow lakes across temperate North America and Europe to categorize and identify the drivers of different mixing regimes. We identified three mixing regimes: rarely (n = 18), intermittently (n = 10), and often (n = 6) mixed, where waterbodies mixed an average of 2%, 26%, and 75% of the study period, respectively. Waterbodies in the often mixed category were larger (≥4.17 ha) and stratification weakened with increased wind shear stress, characteristic of "shallow lakes." In contrast, smaller waterbodies, or "ponds," mixed less frequently, and stratification strengthened with increased shortwave radiation. Shallow ponds (<0.74 m) mixed intermittently, with daytime stratification often breaking down overnight due to convective cooling. Ponds ≥0.74 m deep were rarely or never mixed, likely due to limited wind energy relative to the larger density gradients associated with slightly deeper water columns. Precipitation events weakened stratification, even causing short‐term mixing (hours to days) in some sites. By examining a broad set of shallow waterbodies, we show that mixing regimes are highly sensitive to very small differences in size and depth, with potential implications for ecological and biogeochemical processes. Ultimately, we propose a new framework to characterize the variable mixing regimes of ponds and shallow lakes. Key Points: Shallow lakes and ponds are assumed to be well mixed, yet we identified three mixing regimes: rarely, intermittently, or often mixedAcross 34 temperate shallow lakes and ponds, mixing regimes were primarily regulated by surface area and depthSmall, shallow waters mix due to shortwave radiation and convection; large, shallow waters mix due to wind [ABSTRACT FROM AUTHOR]

Study Design: Experimental study.Objective: In this study, the ambient temperature of a radiofrequency (RF) electrode tip was compared and analyzed in terms of products, mode, flow quantity, and flow rate.Summary Of Background Data: Endoscopic spine surgery is a widely used operation for degenerative lumbar stenosis and herniated lumbar disc. To perform endoscopic spine surgery, dedicated instruments like a RF generator and electrode are essential.Methods: An evaluation system capable of measuring temperature under equal conditions at a certain distance from the electrode tip was manufactured. The distance between the electrode tip and the temperature sensor was set to 1, 5, and 10 mm. The flow quantities of 0, 50, 100, and 150 mL/min and the flow rates of 0, 0.20, 0.53, and 0.80 m/s were compared and statistically analyzed.Results: The temperatures measured in the experiments conducted on the four combinations of RF device showed similar values, and showed differences according to the characteristics of each mode of the RF. As the distance between the electrode tip and the temperature sensor increased, the temperature decreased, and as flow quantity or flow rate increased, the temperature decreased. The maximum temperatures differed significantly according to flow quantity, between flow quantities of 0 and 100 mL/min (P  = 0.03) and between 0 and 150 mL/min (P ≤ 0.01). The maximum temperatures also differed significantly between the flow rate of 0 m/s, and the flow rates of 0.20, 0.53, and 0.80 m/s, with P ≤ 0.01 in all three comparisons.Conclusion: This is the first study in which we made a customized RF temperature evaluation system and verified the temperature changes in various environments. When irrigation was performed, we could confirm that the maximum temperature was less than 60°C. Irrigation is considered essential in endoscopic spine surgery.Level Of Evidence: 3. [ABSTRACT FROM AUTHOR]

Background: Aseptic loosening of the acetabular component remains one of the leading causes of early failure of total hip arthroplasty. Poor apposition of bone onto the implant surface can be due to inaccurate reaming and osteonecrosis of the acetabular bone due to the heat generated while reaming. Methods: New and used acetabular reamers were tested on an MTS system using a clinically relevant force of 87.6 N. A thermal profile and depth achieved by the reamers were analyzed and compared between the 2 cohorts. Heat generated and force required for the community used reamers to achieve the same depth as the new reamers were subsequently analyzed. Results: The new reamers achieved a depth 3.4 mm deeper than the community reamers (P < .001). The new reamers generated 4.1°C less heat than the community reamers (P = .007) under the same force and time. When programmed to ream to the average depth of the new reamers, the community reamers generated 16.8°C more heat (P = .002) and required forces 95-318% greater than the 87.6 N force used by the new reamers. Conclusions: Community use of reamers will cause variations in depth of penetration and increased temperatures at a clinically generated force vs new reamers. When community reamers were forced to the same depths the new reamers achieved, a significantly greater amount of heat was generated, and an increased amount of time was needed, both of which are known risk factors for osteonecrosis.

• An optimal load shedding model for IEDCSs with detailed topology and transmission energy loss is established. • The evaluation method for reliability indices is improved considering the thermal inertia characteristics and the transportation delay. • The sequential MCS is employed to describe the time-varying characteristics of load and the fault and repair characteristics of equipment. The tight interactions between energy sectors increase the risk of affecting integrated energy systems (IESs); therefore, reliability assessment is important for identifying vulnerability and improving security in IESs. In the literature, the network topology, transmission loss, and distinguishing thermal dynamics in IESs are seldom addressed for reliability assessment, rendering the results unreliable. To bridge this gap, an integrated electricity and district cooling model is formulated for IESs that involves elaborate network topologies and transmission losses to reflect their impacts on reliability characteristics. In addition, the evaluation of reliability indices for district cooling systems is improved by considering the thermal inertia characteristics of buildings and the transportation delay of circulating water to characterize the reliability levels of district cooling systems more accurately. Numerical results using a 12-node case validate the improved accuracy of the proposed reliability assessment method and reveal the potential of district cooling systems for enhancing the reliability of power systems. [ABSTRACT FROM AUTHOR]

• A stepwise simulation approach was developed to assess hydropower regulation effects. • Diverted deep-water dropped lake temperature, stratification length and stability. • Advective heat fluxes can strongly influence the dynamics of low-residence time lakes. Interbasin water diversions associated with hydroelectric power operations can influence the physics and water quality of downstream receptor lakes. Little is known about the impact of such diversions in sub-arctic and arctic lakes, which are characterized by weak summer stratification and a high relative contribution of cold and highly turbid tributaries of glacial origin. From 2003 to 2007, Lake Lagarfljót (53 km2, 65°N) experienced a series of changes in its natural hydrological conditions as part of the 690 MW Kárahnjúkar hydroelectric project. The most significant change was the damming and diversion of a second glacial river into the lake, reducing its hydraulic residence time by a factor of three, and increasing its background turbidity levels by one order of magnitude. Here we developed a three-dimensional hydrodynamic modeling approach to assess the impacts of this project on the Lake's physics. We accounted for the uncertainty in pre-dam suspended-solids data and conducted a stepwise assessment of each alteration in the hydrological forcing and lake background conditions. The results revealed abrupt changes in the lake's summer thermal dynamics. Advective heat fluxes now have a similar magnitude to atmospheric heat exchanges or even dominate the lake's heat balance during the second half of the thermal stratification period. The diversion of deep water from Hálslón reservoir led to a net cooling of both the surface (median ≈1°C) and deep (median ≈0.6 °C) layers of the lake during the thermal stratification period, to a ≈ 45 % decrease in the average summer water column Schmidt stability and a two-week decrease in the duration of the thermal stratification period. The lower stability of the water column and deep river-plume intrusions now facilitate nearly complete hypolimnetic water renewal during the thermal stratification period. The proposed modeling approach, which deconstructs the lake's conditions from post- to pre-operational stages, is suitable for lakes with limited pre-operational in-situ data. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) battery faults or failure mechanisms are potentially hazardous to battery health, safety and performance. Thermal fault mechanisms represent a critical subset of such failures. To ensure safety and reliability, battery management systems must have the capability of diagnosing these thermal failures. In line with this requirement, we present a Partial Differential Equation (PDE) model-based scheme for diagnosing thermal faults in Li-ion batteries. For this study, we adopt a distributed parameter one-dimensional thermal model for cylindrical battery cells. The diagnostic scheme objective is to detect and estimate the size of the thermal fault. The scheme consists of two PDE observers arranged in cascade with measured surface temperature feedback. The first observer, denoted as Robust Observer, estimates the distributed temperature inside the cell under nominal (healthy) and faulty conditions. The second observer, denoted as Diagnostic Observer, receives this estimated temperature distribution, and in turn outputs a residual signal that provides the fault information. Furthermore, the residual signal is evaluated against non-zero thresholds to achieve robustness against modeling and measurement uncertainties. Lyapunov stability theory has been utilized to verify the analytical convergence of the observers under heathy and faulty conditions. Simulation studies are presented to illustrate the effectiveness of the proposed scheme.

This paper proposes a bi-level optimal integration scheme for buildings’ space heating loads in the integrated community energy system (ICES). The optimal integration scheme consists of an efficient energy management method and a heating pricing method for the ICES with buildings. At the upper level, the ICES operator optimizes the schedules of energy generation and supply, and the heating prices to buildings to maximize its profit. At the lower level, consumers in buildings optimize the water flow rates in the radiators to minimize their heating costs. The thermal dynamics of the building with controllable indoor radiators is modeled using the model of Resistor-Capacitor thermal network. Moreover, the model predictive control (MPC) is integrated with the bi-level optimization to achieve economic and reliable scheduling of the ICES and buildings in the presence of uncertainties. The bi-level MPC optimization is reformulated as an MPC based mixed-integer linear program using the Karush-Kuhn-Tucker optimality conditions and several linearization techniques. Numerical studies show that the bi-level MPC method can obtain a balanced scheduling scheme between the energy costs of consumers in buildings and the ICES operator's profits. The MPC method can ensure higher profits of the ICES operator and simultaneously, lower energy costs of consumers in buildings. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level optimization framework for buildings to heating grid (B2HG) integration in integrated heating/electricity community energy systems. The proposed B2HG framework enables buildings to carry out heating demand response to the integrated community energy systems (ICES). In upper level, ICES operator with energy conversion equipment devotes to maximizing its profits by optimizing the energy generation/supply schedules and the sale heating price. In lower level, a detailed physical model of the building with adjustable indoor radiators is developed while considering the building thermal dynamics. Consumers minimize their heating costs by adjusting the flow rates of their radiators according to the heating price provided by the upper level. Then, the proposed bi-level optimization problem is converted into a mixed-integer linear programming using Karush-Kuhn-Tucker conditions and strong duality theorem. The piecewise linearization is used to eliminate the nonlinearity of heating network constraints. Numerical results show that consumers can provide heating demand response to ICES operator by considering the thermal dynamics. With the multiple energy conversion equipment, the ICES operator can unlock the flexibility from the supply side. Moreover, the proposed bi-level optimization method can combine the flexibility from both the supply and demand side, and benefits both the ICES operator and building consumers. [ABSTRACT FROM AUTHOR]

Addressing the growing severe heat-generation and temperature-rise issues of the civil aircraft hydraulic system, this paper proposes a thermal dynamic model and thermal management strategies for the system within full mission profile. Firstly, a new thermal dynamic modeling towards general hydraulic components is conducted. Secondly, thermal dynamic governing equations are derived. Then a thermal management mechanism of the system is proposed. The conducted research is prerequisite to future numerical simulation of the thermal dynamic characteristics, evaluation and improvement of thermal management strategies for the system. [ABSTRACT FROM AUTHOR]

Water shortage and salinity are crucial factors affecting plant growth in arid and semi-arid regions, where irrigation water shortage and capillary rise from shallow saline water tables are often encountered. The objectives of this study are to construct a prototype of solar driven desalination plant in an arid area of Jordan Rift Valley, to develop a mathematical model predicting thermal dynamics in the prototype, to calibrate model parameters with measured physical parameters, and to discuss the performance of the prototype as well as its applicability to other areas under different environment. Results of measurement and numerical simulation show that the model is capable to reproduce the thermal dynamics of the desalination plant and to predict dew yield. Overall, the developed model provides a sound basis for describing and explaining the mass and energy balance mechanisms in the developed desalination plant. This study offers also a useful tool for analysis and assessment of the dew yield and thermal dynamics of such a desalination plant in general. Using the constructed prototype, performance analysis based on crop cultivation is ongoing. [ABSTRACT FROM AUTHOR]

• Solar gain was estimated as a function of the azimuth angle with use of splines. • Grey-box models of energy systems improved when solar-gain-splines was used. • Case 1: window orientation captured in the estimated solar gain function. • Case 2: splines outperformed the state-of-the-art incident angle modifier (IAM) of the solar collector field. Modelling the effects of solar irradiation plays an important role in various applications. This paper describes a semi-parametric (combined grey-box and spline-based), data-driven technique that can be used to model systems in which the solar gain depends on the sun position. The solar gain factor is introduced, i.e. the absorbed fraction of measured solar irradiation, and estimated as a continuous non-parametric function of the sun position. The implementation of the spline-based solar gain factor in a grey-box model framework is described. The method is tested in two case studies—in a model of the internal temperature of a dwelling in Aalborg, Denmark, and a model of the return temperature of a solar collector field in Solrød, Denmark. It is shown that the solar gain factor as a function of sun position is able to account for structural variations in solar gain that may occur due to factors such as shading obstacles and window or absorber orientation. In both test cases, the spline-based solar gain function improved the model accuracy significantly, and largely reduced structural errors in prediction residuals. In addition, the shape of the estimated function provided insight into the dynamics of the system and the local solar input characteristics. Accurate representation of such site characteristics was not possible with any data-driven method found in the literature. Besides the grey-box models used in this study, the solar gain factor can be used in a variety of data-driven models, for example in linear regression models. [ABSTRACT FROM AUTHOR]

This letter proposes a thermal modeling method for power electronic components. It represents the thermal dynamics introduced by the ambient temperature variation, which cannot be achieved by existing analytical methods. By using the superposition theorem and time-domain analysis, the limitations of the existing analytical method based on stable ambient temperature are investigated. Then, the proposed thermal modeling method, which considers the thermal dynamics from both power loss and ambient temperature disturbances, is presented. In order to obtain the thermal coefficients in the proposed model, two solutions are provided based on frequency-domain modeling. Experimental verification is given to proof the accuracy of the proposed thermal modeling method considering the ambient temperature dynamics. [ABSTRACT FROM AUTHOR]

The influence of heating rate, gas flow, and biomass particle size on the pyrolysis and thermal dynamics of corncobs (CC) was investigated experimentally using the quantitative method of thermogravimetric analysis (TGA) coupled with mass spectrometry (MS), and the obtained results were compared in depth. For the examined heating rates of 5, 10, and 20 °C/min, the CC pyrolysis at higher heating rates resulted in a more complete decomposition. The initial pyrolysis temperature decreased when gas flow was increased from 30 to 90 mL/min, whereas the weight loss increased. Particle sizes (d ≤ 74 μm, 74 μm < d ≤ 154 μm, 154 μm < d ≤ 280 μm, and 280 μm < d ≤ 450 μm) had pronounced effects on the thermal decomposition and bio-syngas compounds (CO, CO2, CH4, and H2) distribution. The emission intensities of most the gaseous products increased at the elevated heating rate, while they decreased with increasing gas flow. In sum, the pyrolysis of CC particles of 154 μm < d ≤ 280 μm under 20 °C/min and in a gas flow of 30 to 60 mL/min was the most appropriate for bio-syngas production in industrial applications.

Climate change in the Arctic is expected to have a major impact on stream ecosystems, affecting hydrological and thermal regimes. Although temperature is important to a range of in‐stream processes, previous Arctic stream temperature research is limited—focused on glacierised headwaters in summer—with limited attention to snowmelt streams and winter. This is the first high‐resolution study on stream temperature in north‐east Greenland (Zackenberg). Data were collected from five streams from September 2013 to September 2015 (24 months). During the winter, streams were largely frozen solid and water temperature variability low. Spring ice‐off date occurred simultaneously across all streams, but 11 days earlier in 2014 compared with 2015 due to thicker snow insulation. During summer, water temperature was highly variable and exhibited a strong relationship with meteorological variables, particularly incoming shortwave radiation and air temperature. Mean summer water temperature in these snowmelt streams was high compared with streams studied previously in Svalbard, yet was lower in Swedish Lapland, as was expected given latitude. With global warning, Arctic stream thermal variability may be less in summer and increased during the winter due to higher summer air temperature and elevated winter precipitation, and the spring and autumn ice‐on and ice‐off dates may extend the flowing water season—in turn affecting stream productivity and diversity. [ABSTRACT FROM AUTHOR]

Although smart thermostats have increasingly provided homeowners with abundant operational data related to advanced HVAC control and energy usage management in homes, there is a lack of systematic frameworks that can utilize such data to generate actionable information for advanced home comfort system diagnosis and control. Recognizing that a home thermal model, which is capable of connecting weather and HVAC operational data, is crucial to this framework, this paper introduces a home thermal model that is built upon the standard RC (Resistor–Capacitor) approach and the one virtual envelope assumption to describe the thermal dynamics of a home. A parameter estimation scheme is also developed that enables automatic, sequential, and optimal estimation of the model parameters, i.e., the thermal properties, of a home. Finally, the home thermal model and its parameter estimation scheme are tested using data collected over 63 consecutive days from a test home. In the test, the length of data that can provide enough robustness for parameter estimation is investigated. It is found that 10 days of data are enough, although 20 or more days of data can provide more robust results. In addition, when the model is used to make 12-hour-ahead predictions of indoor temperature, the resulting mean, maximum, and 95%-confidence-interval absolute are 0.31°C, 1.72°C, and 0.90°C, respectively, if the model is trained using 10 consecutive days of data, and 0.27°C, 1.19°C, and 0.72°C, respectively, if 20 consecutive days are used. Therefore, the proposed home thermal model requires only a modest amount of training data to yield fairly accurate prediction and is able to perform better with more data. [ABSTRACT FROM AUTHOR]

Specialty paper products made using natural pulps is an attractive field for the paper industry and for researchers. Studying the thermal dynamics of plant pulps is an important step toward improving the thermal stability of papers. This study has the aim of gaining detailed insight into the thermal properties of softwood, hardwood, flax, hemp, mulberry, bamboo, bagasse, and esparto pulps. Chemical composition and thermogravimetric analyses of these non-wood pulps were performed to find the correlations between the chemical, structural, and thermal properties of these pulps. In addition, the Malek model for kinetics of the thermo-decomposition process of pulps is proposed. The kinetics of the most probable mechanism function for G(α) = 1-(1-α) 1/2 of the thermo-decomposition process of plant fibers from 200 to 400 °C is deduced using the Malek model. This study also provides a method to help select the most promising pulps for specialty materials.

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.

This work establishes a computationally efficient model of temperature and diffusion dynamics in ball milled bimetallic Ni-Al particulates. Mechanical contact conditions between adjacent bimetallic domains and temperature fields generated upon impact define the diffusive fluxes through the domains' interfaces. The concentration distributions are studied via Green's function methods, by defining mirror images which reflect the boundary diffusive resistance conditions. Predictions of the model are validated against experimental open-loop scanning electron micrographs and X-ray diffraction spectra at steady-state. Dynamic models of diffusion saturation and internal temperature are developed for the design of closed-loop controller via simulation. This feedback controller is implemented on a laboratory device, equipped with infrared thermometry for external vial temperature measurement, as a self-tuning regulator. The controller adapts an efficiency parameter of the model that thus serves as a real-time observer for internal temperature and diffusion saturation, which are physically inaccessible during processing. Experimental open-loop results are employed to set thresholds of diffusion penetration to avoid early exothermic reaction during fabrication. By controlling the process duration, the regulation system successfully reproduces the material composition of the open-loop reference tests, to ensure desired thermodynamic properties of the ball milled products and safety of the operation. [ABSTRACT FROM AUTHOR]