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This study aims to focus on understanding how different jet angles and Reynolds numbers influence the phase change materials’ (PCMs) melting process and their capacity to store energy. This approach is intended to offer novel insights into enhancing thermal energy storage systems, particularly for applications where heat transfer efficiency and energy storage are critical.

The research involved an experimental and numerical analysis of PCM with a melting temperature range of 22 °C–26°C under various conditions. Three different jet angles (45°, 90° and 135°) and two container angles (45° and 90°) were tested. Additionally, two different Reynolds numbers (2,235 and 4,470) were used to explore the effects of jet outlet velocities on PCM melting behaviour. The study used a circular container and analysed the melting process using the hot air inclined jet impingement (HAIJI) method.

The obtained results showed that the average temperature for the last time step at Ф = 90° and Re = 4,470 is 6.26% higher for Ф = 135° and 14.23% higher for Ф = 90° compared with the 45° jet angle. It is also observed that the jet angle, especially for Ф = 90°, is a much more important factor in energy storage than the Reynolds number. In other words, the jet angle can be used as a passive control parameter for energy storage.

This study offers a novel perspective on the effective storage of waste heat transferred with air, such as exhaust gases. It provides valuable insights into the role of jet inclination angles and Reynolds numbers in optimizing the melting and energy storage performance of PCMs, which can be crucial for enhancing the efficiency of thermal energy storage systems.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

This study has assessed the thermal performance of locally fabricated bio-based building envelopes made of coconut and corn husk composite bricks to reduce building wall heat transmission load and energy consumption towards green building adaptation.

Samples of coconut fiber (coir) and corn husk fiber bricks were fabricated and tested for their thermophysical properties using the Transient Plane Source (TPS) 2500s instrument. A simulation was conducted using Dynamic Energy Response of Building - Lunds Tekniska Hogskola (DEROB-LTH) to determine indoor temperature variation over 24 h. The time lag and decrement factor, two important parameters in evaluating building envelopes, were also determined.

The time lag of the bio-based composite building envelope was found to be in the range of 4.2–4.6 h for 100 mm thickness block and 10.64–11.5 h for 200 mm thickness block. The decrement factor was also determined to be in the range of 0.87–0.88. The bio-based composite building envelopes were able to maintain the indoor temperature of the model from 25.4 to 27.4 °C, providing a closely stable indoor thermal comfort despite varying outdoor temperatures. The temperature variation in 24 h, was very stable for about 8 h before a degree increment, providing a comfortable indoor temperature for occupants and the need not to rely on air conditions and other mechanical forms of cooling. Potential energy savings also peaked at 529.14 kWh per year.

The findings of this study present opportunities to building developers and engineers in terms of selecting vernacular materials for building envelopes towards green building adaptation, energy savings, reduced construction costs and job creation.

This study presents for the first time, time lag and decrement factor for bio-based composite building envelopes for green building adaptation in hot climates, as found in Ghana.

Nowadays, thermal comfort plays a prominent role in contemporary construction practices. Appropriate thermal insulation not only offers energy efficiency benefits in buildings but also enhances occupant well-being, comfort, and productivity. Therefore, a comprehensive understanding of the thermal properties of building materials is essential. This research aims to prepare and investigate a lightweight gypsum-based composite incorporating nano montmorillonite with advanced thermal insulation properties, considering both quality and cost-effectiveness while ensuring environmental compatibility.

This study adopts a laboratory experimental approach. A gypsum sample (without additives) and seven samples of gypsum combined with varying percentages of sodium and calcium montmorillonite nanoclays undergo extensive testing and analysis. Subsequently, the properties of these samples are compared.

The results indicate that adding montmorillonite nanoclays to gypsum composites reduces the density of the tested samples and increases their porosity. Moreover, the thermal conductivity coefficient decreases in these samples, significantly improving the thermal insulation properties of the lightweight gypsum plaster. This improvement is more pronounced in samples containing sodium montmorillonite nanoclay compared to calcium-based samples. Additionally, the investigations reveal that compressive strength decreases with the addition of montmorillonite to the samples.

In this research, laboratory experiments were conducted to investigate the physical and mechanical properties of gypsum plaster with varying percentages of sodium and calcium montmorillonite nanoclays. The studied properties include density, porosity, thermal conductivity coefficient, and compressive strength. Additionally, stress-strain diagrams, elastic modulus, and initial and secondary critical stresses were analyzed for each specimen.

Multiple encapsulated phase change materials (PCMs) are used in a wide range of applications, including convective drying, electronic cooling, waste heat recovery and air conditioning. Therefore, it is important to understand the performance of multiple PCMs in channels with flow separation and develop methods to increase their effectiveness. The aim of the study is to analyze the phase transition dynamics of multiple encapsulated PCMs mounted in a U-shaped tube under inclined magnetic field by using ternary nanofluid.

The PCMs used in the upper horizontal channel, vertical channel and lower horizontal channel are denoted by M1, M2 and M3. Magnetic field is uniform and inclined while finite element method is used as the solution technique. Triple encapsulated-PCM system study is carried out taking into account different values of Reynolds number (Re, ranges from 300 to 1,000), Hartmann number (Ha ranges from 0 and 60), magnetic field inclination (between 0 and 90) and solid volume fraction of ternary nanofluid (between 0 and 0.03). The dynamic response of the liquid fraction is estimated for each PCM with varying Re, Ha and t using an artificial neural network.

It is observed that for PCMs M2 and M3, the influence of Re on the phase transition is more effective. For M2 and M3, entire transition time (t-F) lowers by approximately 47% and 47.5% when Re is increased to its maximum value, whereas it only falls by 10% for M1. The dynamic characteristics of the phase transition are impacted by imposing MGF and varying its strength and inclination. When Ha is raised from Ha = 0 to Ha = 50, the t-F for PCM-M2 (PCM-M3) falls (increases) by around 30% (29%). For PCMs M1, M2 and M3, the phase transition process accelerates by around 20%, 30% and 28% when the solid volume fraction is increased to its maximum value.

Outcomes of this research is useful for understanding the phase change behavior of multiple PCMs in separated flow and using various methods such as nano-enhanced magnetic field to improve their effectiveness. Research outputs are beneficial for initial design and optimization of using multiple PCMs in diverse energy system technologies, including solar power, waste heat recovery, air conditioning, thermal management and drying.

Determining the suitable material and accurate thickness of the thermal insulation layer used in exterior walls during the design phase of a building can be challenging. This study aims to determine suitable material and optimum thickness for the insulation layer considering both operational and embodied factors by a comprehensive assessment of the energy, economic and environmental (3E) parameters.

First, the energy model of an existing building was created by using Autodesk Revit software according to the as-built floor layout to evaluate the impact of five alternative insulating materials in varying thickness values. Second, using the results derived from the model, a thorough evaluation was conducted to ascertain the optimal insulation material and thickness through individual analysis of 3E factors, followed by a comprehensive analysis considering the three aforementioned factors simultaneously.

The findings indicated that polyurethane with 13 cm thickness, rockwool with 10 cm thickness and EPS with 20 cm thickness were the best states based on energy consumption, cost and environmental footprint, respectively. After completing the 3E investigation, the 15-cm-thick mineral wool insulation was presented as the ideal state.

This study explores how suitable material and thickness of insulating material can be determined in advance during the design phase of a building, which is a lot more accurate and cost-effective than applying insulating materials by assumed thickness in the construction phase.

To the best of the authors’ knowledge, this paper is unique in investigating the advantages of using thermally insulating materials in the context of a mosque structure, taking into account its distinctive attributes that deviate from those of typical buildings. Furthermore, there has been no prior analysis of the cost and sustainability implications of these materials concerning the characteristics of subtropical monsoon climate.

This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.

The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.

The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.

Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

This paper aims to map the evolution of hydrogen-based technologies (HBTs) by examining the patenting activity associated to these technlogies from 1930 to 2020. In doing so, the study provides a novel perspective on the development of HBTs and offers implications for managers and policymakers.

We collected patent data at the level of patent families (PFs). Our sample includes 317,089 PFs related to hydrogen production and 62,496 PFs to hydrogen storage. We examined PF data to delineate the state of the art and major technical advancements of HBTs.

Our analysis provides evidence of an increasing patenting activity in the area of HBTs, hence suggesting relatively high levels of expectations on the economic potential of these technologies. US and Japan hold the largest proportion of PFs related to HBTs (about 60%), while European applicants hold the highest proportion of highly cited PFs (about 60%). While firms represent the applicant with the highest share of PFs, our analysis reveals that firms holding HBT PFs are primarily from the chemical sector.

While our analysis is limited to examining patent data which capture some aspects of the innovation activity around HBTs (namelly, patented inventions), our study enriches existing literature by performinng a patent analysis on a much larger sample of data when compared to previous studies.

Two main implications emerge from our study. Firstly, there seems to be an urgent need to support the emergence of a dominant design so as to facilitate the consolidation and diffusion of the HBTs, hence the transition to a more sustainable energy production. Secondly, the majority of HBT PFs are held by a small number of countries. This, in turn, suggests opportunities to develop cross-country cooperation (e.g. international agreements, research and technology offices) to support the development and adoption of HBTs globally.

Considering the results obtained in this study, from a social point of view, the attention that organizations have paid to hydrogen related technologies is evident. This suggests that the development HBTs can function as a social enabler for a sustianable energy transition.

Extant research has focused on the individual components of the hydrogen chain. As a result, we lack a comprehensive understanding of the progress made in the area of HBTs. To address this gap, this study examined HBTs by focusing on both production and storage technologies since their initial developments, hence adopting an observation period of about 70 years.

This paper aims to assess the economic, environmental, policy-related and social implications of establishing green hydrogen production in Jordan.

The comprehensive analysis has been investigated, including economic assessments, environmental impact evaluations, policy examinations and social considerations. Furthermore, the research methodology encompasses energy demand, sector, security and supply analysis, as well as an assessment of the availability of renewable energy resources.

The results indicate substantial economic benefits associated with green hydrogen production, including job creation, increased tax revenue and a reduction in energy imports. Additionally, the study identifies positive environmental impacts, such as decreased greenhouse gas emissions and air pollution. Noteworthy, two methods could be used to produce hydrogen, namely: electrolysis and thermochemical water splitting. As a recommendation, the study proposes that Jordan, particularly Aqaba, take proactive measures to foster the development of a green hydrogen industry and collaborate with international partners to exchange best practices and establish the necessary infrastructure.

To the best of the authors’ knowledge, this paper is among the first to provide a comprehensive perspective on the potential of green hydrogen production as a driving force for Jordan’s economy, while also benefiting the environment and society. However, the research recognizes several challenges that must be addressed to materialize green hydrogen production in Jordan, encompassing high renewable energy costs, infrastructure development requirements and community concerns. Despite these obstacles, the study asserts that the potential advantages of green hydrogen production outweigh the associated risks.

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.