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Life experience in hot and arid areas of Iran has proved that in the transitional seasons (spring and autumn) in which the climate is not too hot, passive cooling systems such as windcatchers (baadgir) have functioned well. This paper intends to investigate the efficiency of a single-side windcatcher as a passive cooling strategy; the case study is the Bina House windcatcher, located in Khousf town, near Birjand city, Iran.

To achieve the aim, air temperature, relative humidity, wind data and mean radiant temperature were measured by the related tools over five days from September 23 to October 23. Then, the thermal performance of the windcatcher was examined by analyzing the effects of all these factors on human thermal comfort. Quantitative assessment of the indoor environment was estimated using DesignBuilder and its computational fluid dynamics (CFD) tool, a thermal comfort simulation method to compare the cooling potential of the windcatcher. Windcatcher performance was then compared with two other common cooling systems in the area: single-side window, and evaporative cooler.

The results showed that both windcatcher and evaporative cooler can provide thermal comfort for Khousf residents in the transitional seasons; but the difference is that an evaporative cooler needs to consume water and electricity power, while a windcatcher is a passive cooling system that uses clean energy of wind.

The present study, by quantitative study of single-side windcatchers in a desert region, measured the climatic factors of a historical house and compared it with thermal comfort criteria. Therefore, the results of field measurements were analyzed, and the efficiency of the windcatcher was compared with two other cooling systems, namely single-side ventilation and evaporative cooler, in the two seasons of summer and autumn (transition seasons).

The purpose of this study is to achieve lower and lower temperature as infrared sensors works faster and better used for space application. For getting good quality images from space, the infrared sensors are need to keep in cryogenic temperature. Cooling to cryogenic temperatures is necessary for space-borne sensors used for space applications. Infrared sensors work faster or better at lower temperatures. It is the need for time to achieve lower and lower temperatures.

This study presents the investigation of the critical Stirling cryocooler parameters that influence the cold end temperature. In the paper, the design approach, the dimensions gained through thermal analysis, experimental procedure and testing results are discussed.

The effect of parameters such as multilayer insulation, helium gas charging pressure, compressor input voltage and cooling load was investigated. The performance of gold-plated and aluminized multilayer insulation is checked. The tests were done with multilayer insulation covering inside and outside the Perspex cover.

By using aluminized multilayer insulation inside and outside the Perspex cover, the improvement of 16 K in cool-down temperature was achieved. The cryocooler is charged with helium gas. The pressure varies between 14 and 18 bar. The optimum cooling is obtained for 17 bar gas pressure. The piston stroke increased as the compressor voltage increased, resulting in total helium gas compression. The optimum cool-down temperature was attained at 85 V.

The cryocooler is designed to achieve the cool-down temperature of 2 W cooling load at 100 K. The lowest cool-down temperature recorded was 105 K at a 2 W cooling load. Multilayer insulation is the major item that keeps the thermal radiation from the sun from reaching the copper tip.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This purpose of this paper is to address the problem of reducing energy consumption in existing buildings using advanced noninvasive interventions (NVIs).

The study methodology involves systematically developing and testing 18 different NVIs in six categories (glazing types, window films, external shading devices, automated internal shades, lighting systems and nanopainting) to identify the most effective individual NVIs. The impact of each individual NVI was examined on an exemplary university educational building in a hot climate zone in Egypt using a computational energy simulation tool, and the results were used to develop 39 combination scenarios of dual, triple and quadruple combinations of NVIs.

The optimal 10 combination scenarios of NVIs were determined based on achieving the highest percentages of energy reduction. The optimal percentage of energy reduction is 47.1%, and it was obtained from a combination of nanowindow film, nanopainting, LED lighting and horizontal louver external. The study found that appropriate mixture of NVIs is the most key factor in achieving the highest percentages of energy reduction.

These results have important implications for optimizing energy savings in existing buildings. The results can guide architects, owners and policymakers in selecting the most appropriate interventions in existing buildings to achieve the optimal reduction in energy consumption.

The novelty of this research unfolds in two significant ways: first, through the exploration of the potential effects arising from the integration of advanced NVIs into existing building facades. Second, it lies in the systematic development of a series of scenarios that amalgamate these NVIs, thereby pinpointing the most efficient strategies to optimize energy savings, all without necessitating any disruptive alterations to the existing building structure. These combination scenarios encompass the incorporation of both passive and active NVIs. The potential application of these diverse scenarios to a real-life case study is presented to underscore the substantial impact that these advanced NVIs can have on the energy performance of the building.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes essential in this scenario to realize the global net-zero goals. The purpose of the proposed study is to evaluate the impact of the widespread adoption of such guidelines in a building community in the context of mixed-mode buildings.

This study decentralizes the theme of improving the energy efficiency of the national building stock in parcels by proposing a community-based hybrid bottom-up modelling approach using urban building energy modelling (UBEM) techniques to analyze the effectiveness of the community-wide implementation of energy conservation guidelines.

In this study, the UBEM is developed and validated for the 14-building residential community in Mumbai, India, adopting the framework. Employing Energy Conservation Building Code (ECBC) compliance on the UBEM shows an energy use reduction potential of up to 15%. The results also reveal that ECBC compliance is more advantageous considering the effects of climate change.

In developing countries where the availability of existing building stock information is minimal, the proposed study formulates a holistic framework for developing a detailed UBEM for the residential building stock from scratch. A unique method of assessing the actual cooling load of the developed UBEM is presented. A thorough sensitivity analysis approach to investigate the effect of cooling space fraction on the energy consumption of the building stock is presented, which would assist in choosing the appropriate retrofit strategies. The proposed study's outcomes can significantly transform the formulation and validation of appropriate energy policies.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Residential buildings in Greece constitute an important portion of the existing building stock. Furthermore, most of these buildings were built prior to the first Thermal Insulation Code of 1981. The article focuses on existing, typical residences built after 1920, which are found mostly in suburban areas and settlements all around Greece. The purpose of the research is to evaluate the effect of simple bioclimatic interventions focused on the improvement of their diurnal, inter-seasonal and annual thermal performance.

The applied strategies include application of thermal insulation in the building shell and openings, passive solar systems for the heating period and shading and natural ventilation for the summer period. The effect of the strategies is analysed with the use of building energy analysis. The simulation method was selected because it provides the possibility of parametric analysis and comparisons for different proposals in different orientations.

The results show that the increased thermal mass of the construction is the most decisive parameter of the thermal behaviour throughout the year.

The typical residences under investigation are often found in urban and/or suburban surroundings. These mostly refer to free-standing buildings situated, which, in many cases, do not have the disadvantages and limitations that the geometrical characteristics of densely built urban locations impose on incident solar radiation (e.g. overshadowing during the winter) and air circulation (e.g. reduce natural ventilation during the summer). Nevertheless, even in these cases, the surrounding built environment may also have relevant negative effects, which were not taken under consideration and could be included in further, future research that will include the effect of various orientations, as well as of neighbouring buildings.

Existing residences built prior to the first Thermal Insulation Code (1981) form an important part of the building stock. Consequently their energy upgrade could contribute to significant conventional energy savings for heating and cooling, along with the inter-seasonal improvement of interior thermal comfort conditions.

The proposed interventions can improve thermal comfort conditions and lead to a reduction of energy consumption for heating and cooling, which is an important step against energy poverty and the on-going energy crisis.

The proposed interventions only involve the building envelope and are simple with relatively low cost.