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Nanofluids are the most attractive mean to enhance the performance of heat transfer devices. Several types of nanoparticles were utilized as they have high thermal conductivity. Renewable energy makes use of these nanofluids special in solar collectors. Evacuated tube solar collector is one of the most utilized solar collectors in thermal applications. CeO 2 nanoparticles were used in the presented study. The mean diameter of CeO 2 was 25 nm. A stable CeO 2 /water was made. The stability was checked using Zeta potential machine. Experiments were carried out using three different volume concentration of CeO 2 nanoparticles of 0.015%, 0.025%, and 0.035%. The thermal performance of the evacuated tube solar collector was examined at different mass flux rates. Results showed that the temperature difference between inlet and outlet flow and absorbed energy increase when nanofluids are used. The volume fraction flow rate of 0.035% at the mass flux rate of 0.017 kg/s.m 2 had the maximum heat removable factor, the thermo-optical characteristic of the collector, and the thermal loss coefficient. The thermo-optical characteristic of the collector of tube solar corrector is raised up to 34%. [ABSTRACT FROM AUTHOR]

A CFD model was performed with commercial software through the adoption of the finite volume method and a SIMPLE algorithm. SiO2-P25 particles were added to water/ethylene glycol as a base fluid. The result is considered a new hybrid nanofluid (HN) for investigating heat transfer (HT). The volume concentrations were 0.5, 1.0, and 1.5%. The Reynolds number was in the range of 5000–17,000. The heat flux (HF) was 7955 W/m2, and the wall temperature was 340.15 K. The numerical experiments were performed strictly following the rules that one should follow in HT experiments. This is important because many studies related to nanofluid HT overlook these details. The empirical correlations that contain the friction factor perform better with higher Reynolds numbers than the correlations based only on Reynolds and Prandtl numbers. When temperature differences are moderate, researchers may consider using constant properties to lower computational costs, as they may give results that are similar to temperature-dependent ones. Compared with previous research, our simulation results are in agreement with the experiments in real time. [ABSTRACT FROM AUTHOR]

The progressive use of renewable energy sources to ensure a continuous and abundant energy supply is the significant target towards a sustainable and secure energy system. Previously, the countries that had relied on fossil fuel as a dominating energy source are now endorsing energy system transition towards renewable energy sources. In this study, a comparative assessment of the energy problems of South Asian countries is summarized. Nevertheless, there are many similarities and differences in the electricity supply system of these countries. Long-term planning for renewable energy development is suggested for a diverse population and dispersed geographical location considering all the significant challenges. However, appropriate schemes are imperative for integrating significant renewable energy sources. This study introduces a foresight plan of the electricity model according to the demand and supply balance for extensive technical analysis. The EnergyPLAN modeling tool was employed to work out a more ambitious VRE integration scenario than the official plans. The reference model was validated according to legitimate and authentic data, and then, the technically most feasible renewable energy-based alternate scenario was built. The additional grid integration cost of variable renewable energy was quantitatively investigated for comprehensive power system modeling for a real-time economic analysis. The results may be adapted and support developing more sustainable power generation serving 1787 million in South Asian countries. [ABSTRACT FROM AUTHOR]

Thermodynamics -- contrary to its name -- is not a dynamic theory. However, some relatively new developments, like finite time, endo-reversible or ordinary thermodynamics introduce time into thermostatics. In this framework the Second Law becomes a set of conditions ensuring the asymptotic stability of equilibrium. In this manuscript some examples are shown, how this basic conceptual change can lead to a profound understanding. [ABSTRACT FROM AUTHOR]

Abstract: A new application of the cost function-based rearrangement of genes (proposed by Liu (2008) ) is presented in this paper through the genetic algorithm-based solution of the inverse heat conduction problem of identifying the temperature dependent thermal conductivity of a solid material using transient temperature histories. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. Through the solution of the inverse problem (using simulated measurements), different approaches of the application of the rearrangement of genes were studied and compared. Application of the rearrangement significantly improved the convergence performance and accuracy of the inverse solution compared to a real-valued genetic algorithm, which was adapted to the problem by the authors. In the algorithm that performed best, the rearrangement was applied in an approach different from Liu’s. The effect of random noise added to the temperature history and the effect of regularization was also studied. With significant improvement in computational efficiency, the proposed algorithm is likely to be very effective in evaluation of real measured temperature histories to determine thermophysical properties. [Copyright &y& Elsevier]

• Irradiance separation and transposition modeling are the two most critical steps. • 13% MAE, 12% RMSE and 23–33% skill score improvement is possible by model selection. • Detailed physical model chains, inlc. reflection and shading, have the lowest MAE. • Simple models have the lowest RMSE, but high bias and underdispersed forecast. • Wind speed has only marginal effect on physical photovoltaic power forecasting. Forecasting the power production of grid-connected photovoltaic (PV) power plants is essential for both the profitability and the prospects of the technology. Physically inspired modelling represents a common approach in calculating the expected power output from numerical weather prediction data. The model selection has a high effect on physical PV power forecasting accuracy, as the difference between the most and least accurate model chains is 13% in mean absolute error (MAE), 12% in root mean square error (RMSE), and 23–33% in skill scores for a PV plant on average. The power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models. The two most critical calculation steps are identified as irradiance separation and transposition modelling, while the inverter models are the least important. Absolute and squared errors are two conflicting metrics, as the more detailed models result in the lowest MAE, while the simplest ones have the lowest RMSE. Wind speed forecasts have only a marginal effect on the PV power prediction. The results of this study contribute to a deeper understanding of the physical forecasting approach in the research community, while the main conclusions are also beneficial for PV plant owners in preparing their generation forecasts. [ABSTRACT FROM AUTHOR]

In the current research, surface-modified SiO2 nanoparticles were used upon immersion in an applied base fluid (ethylene glycol:water = 1:1). The atomic layer deposition method (ALD) was introduced to obtain a thin layer of TiO2 to cover the surface of SiO2 particles. After the ALD modification, the TiO2 content was monitored by energy dispersive X-ray spectroscopy (EDS). Transmission electron microscopy (TEM) and FT-IR spectroscopy were applied for the particle characterization. The nanofluids contained 0.5, 1.0, and 1.5 volume% solid particles and zeta potential measurements were examined in terms of colloid stability. A rotation viscosimeter and thermal conductivity analyzer were used to study the nanofluids' rheological properties and thermal conductivity. These two parameters were investigated in the temperature range of 20 °C and 60 °C. Based on the results, the thin TiO2 coating significant impacted these parameters. [ABSTRACT FROM AUTHOR]

In this paper, we present a study on thermal conductivity and viscosity of nanofluids containing novel atomic layer deposition surface-modified carbon nanosphere (ALD-CNS) and carbon nanopowder (ALD-CNP) core-shell nanocomposites. The nanocomposites were produced by atomic layer deposition of amorphous TiO2. The nanostructures were characterised by scanning (SEM) and transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDX), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, thermogravimetry/differential thermal analysis (TG/DTA) and X-ray powder diffraction (XRD). High-concentration, stable nanofluids were prepared with 1.5, 1.0 and 0.5 vol% nanoparticle content. The thermal conductivity and viscosity of the nanofluids were measured, and their stability was evaluated with Zeta potential measurements. The ALD-CNS enhanced the thermal conductivity of the 1:5 ethanol:water mixture by 4.6% with a 1.5 vol% concentration, and the viscosity increased by 37.5%. The ALD-CNS increased the thermal conductivity of ethylene–glycol by 10.8, whereas the viscosity increased by 15.9%. The use of a surfactant was unnecessary due to the ALD-deposited TiO2 layer. [ABSTRACT FROM AUTHOR]

Abstract: An artificial neural network based solution of the inverse heat conduction problem of simultaneous identification of the temperature-dependent volumetric heat capacity and thermal conductivity function of a solid material is presented in this paper. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The volumetric heat capacity and thermal conductivity vs. temperature functions are to be determined using the measured transient temperature histories of two sensors. In this study noiseless and noisy artificial measurements were generated by the numerical solution of the corresponding direct heat conduction problem. The inverse problem was solved by multi-layer feed-forward neural network trained by back-propagation algorithm (BP) and radial basis function (RBF) type neural network applying the whole history mapping approach. As suggested by Czél et al. [1], a novel (time vs. temperature) representation of the input data was applied. Numerical tests were performed to analyze the accuracy of the two network types with noiseless and noisy inputs. Moreover, it is not necessary to retrain the network when the temperature range of the measurement is changed. Based on the presented results it can be stated that feed-forward neural networks are powerful tools in non-iterative solution of function estimation inverse heat conduction problems and they are likely to be very effective in evaluation of real measured temperature histories to simultaneously determine the volumetric heat capacity and thermal conductivity as an arbitrary function of temperature. [Copyright &y& Elsevier]

Photovoltaic (PV) power is emission-free during operation. However, its life-cycle environmental impacts should be accounted for during the design phase. This paper presents the first methodology to calculate the product environmental footprint (PEF) and the levelized cost of electricity (LCOE) based on a unified technical modeling framework of ground-mounted, grid-connected PV power plants. To test the model, we determined the single- and multiobjective optima of 8 main balance-of-system design parameters applying a genetic algorithm for a case study of two geographical locations with distinct solar resources. Results show that impacts can be reduced by 1–13% in each of the 16 different PEF environmental impact categories. Unfortunately, the different types of impacts cannot be reduced simultaneously with a single design solution, calling for the use of a weighted environmental footprint as the most favorable single-score environmental indicator. Pareto-optimal design solutions for multiple objectives show that increasing the AC/DC ratio, row distance, tilt angle, and cable losses compared to the economic optimum is beneficial for carbon and environmental footprint reduction. By accepting a small cost increase, the majority of the potential impact reduction can be achieved. Trade-offs between economic and environmental objectives are analyzed by assigning a price tag to environmental impacts. Based on the proposed weighting scheme, regions with polluting electricity production mix should choose the most profitable plant design for the largest environmental benefit. In other places eco-design could lead to a 1% overall impact reduction for just 0.1% extra cost. Balancing power requirements, copper recycling scenarios, the uncertainty of ecological impacts, and combined land use are identified as future research areas. Image 1 • First study to integrate environmental footprint method in the design of PV plants. • Economic and environmental optimization results in fairly distinctive designs. • Environmental optimization leads to greater capacity factor but larger land area. • Weighting method between environmental and economic objective is proposed. • Taxing environmental impacts leads to a minor change in least-cost design. [ABSTRACT FROM AUTHOR]

• Technical, economic and environmental modeling frameworks are proposed. • Economic and environmental optima of hybrid renewable energy systems are different. • Local climatic conditions highly influence the impacts and the optimal design. • Single- and multi-objective optimization both have advantages and disadvantages. • Installing photovoltaics is the cheapest way to decrease environmental impacts. The rapid spread of renewables made it essential to design optimal hybrid renewable energy systems (HRES) with the distinctive economic and environmental impacts of each technology in mind. According to a comprehensive literature review, very few studies consider life-cycle environmental impacts in small-scale hybrid renewable energy system optimization. This paper aims to fill this gap by providing a multi-objective design framework for household-scale systems based on the technical modeling of several typical components. Solar photovoltaic, wind turbine, solar heat collector, heat pump, heat storage, battery, and as a novelty, heat insulation thickness are considered. Backup power is either drawn from the grid or produced by a diesel generator in grid-connected and off-grid scenarios, respectively. Single objective optimization using genetic algorithm resulted in the least cost and the least environmental footprint options in a case study of three different locations across Europe. Then, Pareto-optimal solutions between the two extremities were explored with a multi-objective genetic algorithm. Single objective results show substantial differences between environmental and economic optima, while multi-objective optimization proved to be an efficient tool to investigate the trade-offs between the two conflicting goals. Solar photovoltaics is proved to be the most competitive technology to reduce environmental impacts in the case of grid-connected systems. Off-grid systems, however, benefit the most from a balanced mix of different renewable energy sources. Life-cycle impacts in the design of systems involving renewables is proven to be relevant while potential applications of the framework are also revealed. Further research areas, as well as the limitations of the methodology are identified in the conclusion. [ABSTRACT FROM AUTHOR]

The solar chimney power plant (SCPP) is a straightforward and clean technique to generate electricity from solar radiation. However, this technology still faces major challenges, such as low efficiency, which has hindered its industrialization. This study experimentally develops a novel collector design to improve the solar chimney collector's efficiency. The new design includes metallic tubes as solar radiation absorbers hung from the canopy of the collector. The metallic tubes are open at the top and sealed with transparent sheets at the bottom to decrease the solar radiation reflected into the ambient air. Experimental and 3-D computational fluid dynamics (CFD) analyses are performed to validate the new design. The effects of hanging metallic tubes on temperature and velocity distribution are explored. The temperature increased by about 5 K at the chimney inlet, causing a roughly 8% rise in collector efficiency due to the fact that metallic tubes operate as an extended surface. The impact of various tube geometries on solar chimneys' efficiency is examined. The CFD findings reveal that the metallic tube geometry variation has considerably impacted the collector's efficiency. Thus, the collector efficiency is increased by changing tube diameter by around 33.7%, similar to changing tube length by 30%. [Display omitted] • A new solar chimney's collector design was experimentally developed and compared with CFD simulation. • Effects of hung metallic tubes on air temperature and velocity distribution were explored. • Effects of various metallic tube geometries on the collector's efficiency were studied and compared. • The collector efficiency increased from 25% in conventional solar chimneys to 33.90% in the improved ones. [ABSTRACT FROM AUTHOR]

Halloysite nanotube (HNT) which is cheap, natural, and easily accessible 1D clay, can be used in many applications, particularly heat transfer enhancement. The aim of this research is to study experimentally the pool boiling heat transfer (PBHT) performance of novel halloysite nanofluids at atmospheric pressure condition from typical horizontal heater. The nanofluids are prepared from halloysite nanotubes (HNTs) nanomaterials-based deionized water (DI water) with the presence of sodium hydroxide (NaOH) solution to control pH = 12 to obtain stable nanofluid. The nanofluids were prepared with dilute volume concentrations of 0.01–0.5 vol%. The performance of PBHT is studied via pool boiling curve and pool boiling heat transfer coefficient (PBHTC) from the typical heater which is the copper horizontal tube with a thickness of 1 mm and a diameter of 22 mm. The temperatures of the heated tube surface are measured to obtain the PBHTC. The results show an improvement of PBHTC for halloysite nanofluids compared to the base fluid. At 0.05 vol% concentration, HNT nanofluid has the best enhancement of 5.8% at moderate heat flux (HF). This indicates that HNT is a potential material in heat transfer applications. [ABSTRACT FROM AUTHOR]

A comparative research on stability, viscosity (µ), and thermal conductivity (k) of carbon nanosphere (CNS) and carbon nanopowder (CNP) nanofluids was performed. CNS was synthesized by the hydrothermal method, while CNP was provided by the manufacturer. Stable nanofluids at high concentrations 0.5, 1.0, and 1.5 vol% were prepared successfully. The properties of CNS and CNP nanoparticles were analyzed with Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), specific surface area (SBET), X-ray powder diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and energy dispersive X-ray analysis (EDX). The CNP nanofluids have the highest k enhancement of 10.61% for 1.5 vol% concentration compared to the base fluid, while the CNS does not make the thermal conductivity of nanofluids (knf) significantly higher. The studied nanofluids were Newtonian. The relative µ of CNS and CNP nanofluids was 1.04 and 1.07 at 0.5 vol% concentration and 30 °C. These results can be explained by the different sizes and crystallinity of the used nanoparticles. [ABSTRACT FROM AUTHOR]

Nanofluids obtained from halloysite and de-ionized water (DI) were prepared by using surfactants and changing pH for heat-transfer applications. The halloysite nanotubes (HNTs) nanofluids were studied for several volume fractions (0.5, 1.0, and 1.5 vol%) and temperatures (20, 30, 40, 50, and 60 °C). The properties of HNTs were studied with a scanning electron microscope (SEM), energy-dispersive X-ray analysis (EDX), Fourier-transform infrared (FT-IR) spectroscopy, X-ray powder diffraction (XRD), Raman spectroscopy and thermogravimetry/differential thermal analysis (TG/DTA). The stability of the nanofluids was proven by zeta potentials measurements and visual observation. With surfactants, the HNT nanofluids had the highest thermal conductivity increment of 18.30% for 1.5 vol% concentration in comparison with the base fluid. The thermal conductivity enhancement of nanofluids containing surfactant was slightly higher than nanofluids with pH = 12. The prepared nanofluids were Newtonian. The viscosity enhancements of the nanofluid were 11% and 12.8% at 30 °C for 0.5% volume concentration with surfactants and at pH = 12, respectively. Empirical correlations of viscosity and thermal conductivity for these nanofluids were proposed for practical applications. [ABSTRACT FROM AUTHOR]

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811). [ABSTRACT FROM AUTHOR]

We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides. [ABSTRACT FROM AUTHOR]

The non-Fourier heat conduction phenomenon on room temperature is analyzed from various aspects. The first one shows its experimental side, in what form it occurs, and how we treated it. It is demonstrated that the Guyer-Krumhansl equation can be the next appropriate extension of Fourier's law for room-temperature phenomena in modeling of heterogeneous materials. The second approach provides an interpretation of generalized heat conduction equations using a simple thermo-mechanical background. Here, Fourier heat conduction is coupled to elasticity via thermal expansion, resulting in a particular generalized heat equation for the temperature field. Both aforementioned approaches show the size dependency of non-Fourier heat conduction. Finally, a third approach is presented, called pseudo-temperature modeling. It is shown that non-Fourier temperature history can be produced by mixing different solutions of Fourier's law. That kind of explanation indicates the interpretation of underlying heat conduction mechanics behind non-Fourier phenomena. [ABSTRACT FROM AUTHOR]