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The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins.

The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm.

The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers.

A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.

Traditional central courtyards have been advocated for being thermally efficient for hot-climate regions. However, exploring previous literature shows that it is not clear to what extent courtyards are truly thermally comfortable. This study determines the level of thermal comfort in residential courtyards in hot-climate regions, taking Baghdad as a case study.

This study develops a novel Courtyard Thermal Usability Index (CTUI) to quantify the ability of courtyards to provide thermal comfort to occupants. CTUI is the fraction of useable thermally comfortable hours in courtyards of the total occupation hours during a specific period. To operationalise CTUI, the research employs the Envi-met 4.2 simulation tool to determine the annual thermal conditions of 360 courtyards. An adaptive thermal comfort model developed by Al-Hafith in 2020 for Iraq is used to judge simulated thermal conditions and determine CTUI.

CTUI enables determining the level of thermal comfort courtyards offer to occupants by showing the ratio of the thermally comfortable period versus the occupation period. Results show that, in Iraq, annually, courtyards offer up to 38% comfortable hours out of the total potential occupation hours. The rest of the time the courtyard will not be comfortable, mostly due to overheating. When designing courtyards, the most effective geometric property impacting courtyards' thermal conditions is width/height. The most important microclimatic factor impacting occupants' thermal sensation is mean radiant temperature (MRT). This study can be used to inform designing thermally efficient courtyards for hot-climate regions.

This study presents the first assessment of the thermal efficiency of courtyards in hot-climate regions depending on an assessment of their ability to provide thermal comfort to occupants. The study presents a novel index that can be used to quantify the ability of courtyards to provide a thermally comfortable environment to occupants.

Heat exchangers (HEs) which provide heat transfer and transfer energy through direct or indirect contact between fluids have an essential role in many processes as a part of various industries from pharmaceutical production to electronic devices. Using nanofluid as working fluid and integrating different types of turbulators could be used to upgrade the thermal effectiveness of HEs. Recently, to obtain more increment in thermal effectiveness, hybrid nanofluids are used that are prepared by mixing two or more various nanoparticles. The purpose of this experimental and numerical study is investigating different scenarios for improving the effectiveness of a concentric U-tube type HE.

In the numerical section of this study, different turbulator modifications, including circular and quarter circular rings, were modeled to determine the effect of adding turbulator on thermal performance. In addition, Al₂O₃/water and SiO₂/water single and Al₂O₃–SiO₂/water hybrid nanofluids were experimentally tested in an unmodified concentric U-tube HE in two different modes, including counter flow and parallel flow. Al₂O₃–SiO₂/water hybrid nanofluid was prepared at 2% (wt./wt.) particle ratio and compared with Al₂O₃/water and SiO₂/water single type nanofluids at same particle ratios and with distilled water.

Numerical modeling findings exhibited that integrating turbulators to the concentric tube type HE caused to raise in the effectiveness by improving heat transfer area. Also, experimental results indicated that using both hybrid and single type nanofluids notably upgraded the thermal performance of the concentric U-tube HE. Integrating turbulators cannot be an effective alternative in a concentric U-tube type HE with lower diameter because of raise in pressure drop. Numerically achieved findings exhibited that using quarter circular turbulators decreased pressure drop in comparison with circular turbulators. According to the experimental outcomes, using hybrid Al₂O₃–SiO₂/water nanofluid leads to obtain more thermal performance in comparison with single type nanofluids. The highest increment in overall heat transfer coefficient of HE by using Al₂O₃–SiO₂/water nanofluid achieved as 58.97% experimentally.

The overall outcomes of the current research exhibited the positive impacts of using hybrid nanofluid and integrating turbulators. In this empirical and numerical survey, numerical simulations were performed to specify the impact of applying different turbulators and hybrid nanofluid on the flow and thermal characteristics in a concentric U-tube HE. The achieved outcomes exhibited that using hybrid nanofluid can notably increase the thermal performance with negligible pressure drop in comparison with two different turbulator modifications.

This study aims to evaluate the thermal performance of sodium alginate (SA) aerogel attached to nano SiO₂ and its radiative cooling effect on firefighting clothing without environmental pollution.

SA/SiO₂ aerogel with refractory heat insulation and enhanced radiative cooling performance was fabricated by freeze-drying method, which can be used in firefighting clothing. The microstructure, chemical composition, thermal stability, and thermal emissivity were analyzed using Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analyzer and infrared emissivity measurement instrument. The radiative cooling effect of aerogel was studied using thermal infrared imager and thermocouple.

When the addition of SiO₂ is 25% of SA, the prepared aerogel has excellent heat insulation and a high radiative cooling effect. Under a clear sky, the temperature of SA/SiO₂ aerogel is 9.4°C lower than that of pure SA aerogel and 22.1°C lower than that of the simulated environment. In addition, aerogel has more exceptional heat insulation effect than other common fabrics in the heat insulation performance test.

SA/SiO₂ aerogel has passive radiative cooling function, which can efficaciously economize global energy, and it is paramount to environment-friendly cooling.

This method could pave the way for high-performance cooling materials designed for firefighting clothing to keep maintain the wearing comfort of firefighters.

SA/SiO₂ aerogel used in firefighting clothing can release heat to the low-temperature outer space in the form of thermal radiation to achieve its own cooling purpose, without additional energy supply.

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

2. Investment strategy of energy storage power stations on the supply side of wind power generators.

3. Impact of pricing method on the investment decisions of energy storage power stations.

4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

Lightweight, open construction cotton knitted fabrics generally do not impart good protection from solar ultraviolet radiation (UVR). As lightweight 100% cotton single jersey is highly cherished for summerwear, it is sine qua non to understand the structural parameters that effectively strike a good balance between UV protection and thermophysiological comfort of the wearer. Relatively heavy fabrics protect from UVR, but comfort is compromised because of waning porosity, increase in thickness and thermal insulation. The purpose of this paper is to engineer knits that will bestow maximum UV protection while preserving the thermophysiological comfort of the wearer.

In total, 27 cotton single jersey fabrics with different areal densities and yarn counts were selected. Ultraviolet protection factor (UPF) was calculated based on the work of Imrith (2022). To précis, the authors constructed a UV box to measure the UPF of fabrics, denoted as UPF_B. UPF_B data were correlated with AATCC 183-2004 and yielded high correlation, R² 0.977. It was concluded that UPF 50 corresponds to UPF_B 94.3. Thermal comfort properties were measured on the Alambeta and water-vapour resistance on the Permetest. Linear programming (LP) was used to optimize UPF_B and comfort. Linear optimization focused on maximizing UPF_B while keeping the thermophysiological comfort and areal density as constraints.

The resulting linear geometrical and sensitivity analyses generated multiple technically feasible solutions of fabrics thickness and porosity that gave valid UPF_B, thermal absorptivity and water-vapour and thermal resistance. Subsequently, an interactive optimization software was developed to predict the stitch length, tightness factor and yarn count for optimum UPF_B from a given areal density. The predicted values were then used to knit seven 100% cotton single jersey fabrics and were tested for UV protection. All seven fabrics gave UPF_B above the threshold, that is, higher than 94.3. The mathematical model demonstrated good correlations with the optimized parameters and experimental values.

The optimization software predicted the optimum UPF_B reasonably well, starting from the fabric structural and constructional parameters. In addition, the models were developed as interactive user interfaces, which can be used by knitted fabric developers to engineer cotton knits for maximizing UV protection without compromising thermophysiological comfort. It has been demonstrated that LP is an efficient tool for the optimization and prediction of targeted knitted fabrics parameters.

Understanding and tailoring the solidification characteristics and microstructure evolution in as-built parts fabricated by laser powder bed fusion (LPBF) is crucial as they influence the final properties. Experimental approaches to address this issue are time and capital-intensive. This study aims to develop an efficient numerical modeling approach to develop the process–structure (P-S) linkage for LPBF-processed Inconel 718.

In this study, a numerical approach based on the finite element method and cellular automata was used to model the multilayer, multitrack LPBF build for predicting the solidification characteristics (thermal gradient G and solidification rate R) and the average grain size. Validations from published experimental studies were also carried out to ensure the reliability of the proposed numerical approach. Furthermore, microstructure simulations were used to develop P-S linkage by evaluating the effects of key LPBF process parameters on G × R, G/R and average grain size. A solidification or G-R map was also developed to comprehend the P-S linkage.

It was concluded from the developed G-R map that low laser power and high scan speed will result in a finer microstructure due to an increase in G × R, but due to a decrease in G/R, columnar characteristics are also reduced. Moreover, increasing the layer thickness and decreasing the hatch spacing lowers the G × R, raises the G/R and generates a coarse columnar microstructure.

The proposed numerical modeling approach was used to parametrically investigate the effect of LPBF parameters on the resulting microstructure. A G-R map was also developed that enables the tailoring of the as-built LPBF microstructure through solidification characteristics by tuning the process parameters.

The thermophysiological comfort of fabrics is prerequisite as customers covet adequate moisture, heat management-supported and UV protective clothing that measure up to their levels of activities and environmental conditions. Hitherto, scant tasks have been reported with the purpose of engineering both comfort and UV protection simultaneously. From that vantage point, the objective of this work is to develop a model for optimum UPF, air permeability, water-vapour resistance, thermal resistance, thermal absorptivity and areal density of knitted fabrics.

Weft knitted fabrics of various compositions were investigated. UPF was tested using the Labsphere UV transmittance analyser. The FX 3300 (Textest instruments) air permeability tester was used to test air permeability. Thermal comfort and water-vapour resistance were evaluated using the Alambeta and Permetest instruments, respectively. Based on image processing, the porosity was measured. Fabrics thickness and areal density were measured according to standard methods. Furthermore, parametric and non-parametric statistical test methods were applied to the data for analysis.

Linear regression was substantiated by Kolmogorov-Smirnov test. Then multiple linear regression of porosity and thickness together on UPF and comfort parameters were visually depicted by virtue of 3D linear plots. Residual analysis with quantile-quantile and probability plots, advocated the tests using the Shapiro-Wilk test. The result was validated by comparison with experimental data tested. The samples gave satisfactory relative errors and were supported by the z-test method. All tests indicated failure to reject the null hypothesis.

The predictive models were embedded into an interactive computer program. Fabric thickness and porosity are the inputs needed to run the program. It will predict the optimum UPF, areal density and thermophysiological comfort parameters. In a nutshell, knitters may use the program to determine optimum structural parameters for diverse permutations of UPF and thermophysiological comfort parameters; scilicet high UV protection together with low thermal insulation combined with low water-vapour resistance and high air permeability.

Steel-reinforced concrete-filled steel tubular (SRCFST) columns have been increasingly popular in engineering practice for the columns' excellent seismic and fire performance. Significant design progress guidance has been made through continuous numerical and experimental research in recent years. This paper tested and analysed the residual loading capacity of SRCFST columns under axial loading after experiencing non-uniform ISO-834 standard fire.

The experimental research covered the main parameter of heating conditions, 1-side and 2-side fire, through two specimens. Two specimens were heated and loaded simultaneously in the furnace for 240 min. After cooling, the columns were moved to the hydraulic loading system and loaded to failure to determine the columns' residual capacity.

The experimental results indicated that the non-uniform heating area plays an essential role in the overall performance of SRCFST columns, the increasing heating area of columns results in lower residual loading capacity and stiffness. The SRCFST columns still had a high loading capacity after heating and loading in the fire.

The comparison of experimental data against design results showed that the design method generated a 16% safety margin for S2H4 and a 39% safety margin for S1H4.

This study aims to formulate a user-friendly pre-design model that could be a decision support tool for green wall systems to assist designers in selecting an optimal green wall system aligned with specified performance criteria while concurrently addressing project requirements linked to social and economic parameters. This approach seeks to enhance overall project satisfaction for the designer and the owner.

A correlation between the green wall context and design requirements and its performance on the buildings have been defined by considering its social and economic parameters, which represented the owner preferences to ensure the most satisfaction from installation as it achieves the required performance that is defined by the designer such as maximizing thermal insulation, improving indoor air quality, reducing the needed heating and cooling loads, etc. and also to achieve the satisfaction in social and economic requirements defined by the owner such as system installation cost, system maintenance cost, adding beauty value, etc.

The research developed an easy pre-design model to be a tool for green wall system decision-making for the most suitable system, which contains three main steps: the first one is defining the required performance of the green wall (designer requirements), the second step is limiting the context of the project which is made by designer and the owner requirements and finally the third step is choosing the system components that ensures achieving the requirements of both owners and designer, related to the building and climate context.

The added value lies in developing a green wall decision-making tool, essentially a pre-design model. This model considers the correlation between the project’s context, encompassing climate and building conditions. It provides a structured approach for decision-making in the early stages of green wall design. It offers valuable insights into the optimal choices related to system type, installation methods and plant characteristics. This enhanced decision-making tool contributes to more informed and efficient design processes, considering each project’s specific needs and conditions.