Search Results (1,139)

This study investigates the thermal dynamics and material behavior involved in the baking process for Lebanese flatbread, focusing on the heat transfer mechanisms, water loss, and dough expansion under high-temperature conditions. Despite previous studies on flatbread baking using impingement or conventional ovens, this work presents the first experimental investigation of the traditional Lebanese flatbread baking process under realistic industrial conditions, specifically using a high-temperature tunnel oven with direct flame heating, extremely short baking times (~10–12 s), and peak temperatures reaching ~650 °C, which are essential to achieving the characteristic pocket formation and texture of Lebanese bread. This experimental study characterizes the baking kinetics of traditional Lebanese flatbread, recording mass loss pre- and post-baking, thermal profiles, and dough expansion through real-time temperature measurements and video recordings, providing insights into the dough’s thermal response and expansion behavior under high-temperature conditions. A custom-designed instrumented oven with a steel conveyor and a direct flame burner was employed. The dough, prepared following a traditional recipe, was analyzed during the baking process using K-type thermocouples and visual monitoring. Results revealed that Lebanese bread undergoes significant water loss due to high baking temperatures (~650 °C), leading to rapid crust formation and pocket development. Empirical equations modeling the relationship between baking time, temperature, and expansion were developed with high predictive accuracy. Additionally, an energy analysis revealed that the total energy required to bake Lebanese bread is approximately 667 kJ/kg, with an overall thermal efficiency of only 21%, dropping to 16% when preheating is included. According to previous CFD (Computational Fluid Dynamics) simulations, most heat loss in similar tunnel ovens occurs via the chimney (50%) and oven walls (29%). These findings contribute to understanding the broader thermophysical principles that can be applied to the development of more efficient baking processes for various types of bread. The empirical models developed in this study can be applied to automating and refining the industrial production of Lebanese flatbread, ensuring consistent product quality across different baking environments. Future studies will extend this work to alternative oven designs and dough formulations. Full article

Though the pursuit of sustainable desalination processes with high water recovery has intensified the research interest in membrane distillation (MD), the influence of module connection configuration on performance stability remains poorly explored. The current study provided a comprehensive multiparameter assessment of hollow fibre membrane modules connected in parallel and series in direct contact membrane distillation (DCMD) for the first time. The configurations were evaluated under varying process parameters such as temperature (50–70 °C), flow rates (22.1–32.3 mL·s⁻¹), magnesium concentration as scalant (1.0–4.0 g·L⁻¹), and flow direction (co-current and counter-current), assessing their influence on temperature gradients (∆T), flux and pH stability, salt rejection, and crystallisation. Interestingly, the parallel module configuration maintained high operational stability with uniform flux and temperature differences (∆T) even at high recovery factors (>75%). On one hand, the serial configuration experienced fluctuating ∆T caused by thermal and concentration polarisation, causing an early crystallisation (abrupt drop in feed conductivity). Intensified polarisation effects with accelerated crystallisation increased the membrane risk of wetting, particularly at high recovery factors. Despite these changes, the salt rejection remained relatively high (99.9%) for both configurations across all tested conditions. The findings revealed that acidification trends caused by MgSO₄ were configuration-dependent, where the parallel setup-controlled rate of pH collapse. This study presented a novel framework connecting membrane module architecture to mass and heat transfer phenomena, providing a transformative DCMD module configuration design in water desalination. These findings not only provide the critical knowledge gaps in DCMD module configurations but also inform optimisation of MD water desalination to achieve high recovery and stable operation conditions under realistic brine composition. Full article

Surface Air-Cooled Oil Coolers (SACOCs) can become a critical component in managing the increasing thermal loads of modern turbofan engines. Installed within the bypass duct, SACOCs utilize high-mass flow bypass air for convective heat rejection, reducing reliance on traditional Fuel-Oil Heat Exchangers. This review explores SACOC design principles, integration challenges, aerodynamic impacts, and performance trade-offs. Emphasis is placed on the balance between thermal efficiency and aerodynamic penalties such as pressure drop and flow distortion. Experimental techniques, including wind tunnel testing, are discussed alongside numerical methods, and Conjugate Heat Transfer modeling. Presented studies mostly demonstrate the impact of fin geometry and placement on both heat transfer and drag. Optimization strategies and Additive Manufacturing techniques are also covered. SACOCs are positioned to play a central role in future propulsion systems, especially in ultra-high bypass ratio and hybrid-electric architectures, where traditional cooling strategies are insufficient. This review highlights current advancements, identifies limitations, and outlines research directions to enhance SACOC efficiency in aerospace applications. Full article

Rammed earth (RE), a traditional material aligned with circular economy (CE) principles, has been gaining renewed interest in contemporary construction due to its low environmental impact and compatibility with sustainable building strategies. Though not a modern invention, it is being reintroduced in response to the increasingly strict European Union (EU) regulations on carbon footprint, life cycle performance, and thermal efficiency. RE walls offer multiple benefits, including humidity regulation, thermal mass, plasticity, and structural strength. This study also draws attention to their often-overlooked ability to mitigate indoor overheating. To preserve these advantages while enhancing thermal performance, this study explores insulation strategies that maintain the vapor-permeable nature of RE walls. A parametric analysis using Delphin 6.1 software was conducted to simulate heat and moisture transfer in two main configurations: (a) a ventilated system insulated with mineral wool (MW), wood wool (WW), hemp shives (HS), and cellulose fiber (CF), protected by a jute mat wind barrier and finished with wooden cladding; (b) a closed system using MW and WW panels finished with lime plaster. In both cases, clay plaster was applied on the interior side. The results reveal distinct hygrothermal behavior among the insulation types and confirm the potential of natural, low-processed materials to support thermal comfort, moisture buffering, and the alignment with CE objectives in energy-efficient construction. Full article

The precise forecasting of mesoscale eddy trajectories holds significant importance for understanding their mechanisms in driving global oceanic mass and heat transport. However, mesoscale eddies are influenced by numerous stochastic and uncertain factors, leading to substantial fluctuations in their attribute variables. Additionally, the trajectories of eddies are related to historical trends and interact with surrounding eddies. These render the accurate forecasting of mesoscale eddy trajectories a formidable challenge. This study proposes a novel dynamic forecasting framework for eddies’ trajectories, termed EddyGnet, a dual graph neural network framework that synergistically models the complex multivariable association and the spatiotemporal eddy association. In this framework, the dynamic association among eddy attribute variables is first explored by a multivariable association graph (MAG) learning module. Subsequently, the spatial and temporal association among eddies are concurrently analyzed using a spatiotemporal eddy association graph (STEAG) learning module. Finally, a decayed volatility loss function is designed to properly handle the complex and variable data features and improve the forecasting performance. The experimental results on the eddy dataset verify the effectiveness of our proposed EddyGnet, demonstrating superior predictive accuracy and stability compared with existing classical methods. The findings advance the mechanistic understanding of eddy dynamics and provide a transferable paradigm for geoscientific spatiotemporal modeling. Full article

In recent years, packed-bed systems have emerged as an attractive design for thermal energy storage systems due to their high thermal efficiency and economic feasibility. As integral components of numerous large-scale applications systems, packed-bed thermal energy stores can be successfully paired with renewable energy and waste heat to improve energy efficiency. An analysis of the thermal performances of two packed beds (hot and cold) during six-hour charging and discharging cycles has been conducted in this paper using COMSOL Multiphysics software, utilizing the optimal design parameters that have been determined in previous studies, including porosity (0.2), particle diameters (4 mm) for porous media, air as a heat transfer fluid, magnesia as a storage medium, mass flow rate (13.7 kg/s), and aspect ratio (1). The performance has been evaluated during both the charging and discharging cycles, in terms of the system’s capacity factor, the energy stored, and the thermal power, in order to understand the system’s performance and draw operational recommendations. Based on the results, operating the hot/cold storage in the range of 20–80% of the full charge was found to be a suitable range for the packed-bed system, ensuring that the charging/discharging power remains within 80% of the maximum. Full article

The spray evaporation process in the boiler desuperheater involves complex droplet behaviors and fluid–thermal coupling, and its temperature distribution characteristics greatly affect the performance and safety of industrial processes. To better understand the process characteristics and develop the optimal desuperheater design, computational fluid dynamics (CFDs) was applied to numerically investigate the flow and thermal characteristics. The Eulerian–Lagrangian approach was used to describe the two-phase flow characteristics. Both primary and secondary droplet breakup, the coupling effect of gas–liquid and stochastic collision and coalescence of droplets were considered in the model. The plain-orifice atomizer model was applied to simulate the atomization process. The numerical model was validated with the plant data. The spray tube structure was found to greatly affect the flow pattern, resulting in the uneven velocity distribution, significant temperature difference, and local reverse flow downstream of the orifices. The velocity and temperature distributions tend to be more uniform due to the complete evaporation and turbulent mixing. Smaller orifices are beneficial for generating smaller-sized droplets, thereby promoting the mass and heat transfer between the steam and droplets. Under the same operating conditions, the desuperheating range of cases with 21, 15, and 9 orifices is 33.7 K, 32.0 K, and 29.8 K, respectively, indicating that the desuperheater with more orifices (i.e., with smaller orifices) shows better desuperheating ability. Additionally, a venturi-type desuperheater was numerically studied and compared with the straight liner case. By contrast, discernible differences in velocity and temperature distribution characteristics can be observed in the venturi case. The desuperheating range of the venturi and straight liner cases is 38.1 K and 35.4 K, respectively. The velocity acceleration through the venturi throat facilitates the droplet breakup and improves mixing, thereby achieving better desuperheating ability and temperature uniformity. Based on the investigation of the spray evaporation process, the complex droplet behaviors and fluid–thermal coupling characteristics in an industrial boiler desuperheater under high temperature and high pressure can be better understood, and effective guidance for the process and design optimizations can be provided. Full article

This study describes both experimental and numerical investigations into the heat and mass transfer processes governing the vacuum freeze drying of camel milk, with a specific focus on improving the energy efficiency. A three-dimensional model was developed and solved using the finite element method to simulate temperature evolution and sublimation interface progression during drying. The numerical model was validated against experimental data, achieving strong agreement, with an $R^2$ value of 0.94. A detailed parametric analysis examined the effects of the shelf temperature, sample thickness, and chamber pressure on the drying kinetics and energy input. The results indicate that optimising these parameters can significantly reduce the energy consumption and processing time while maintaining product quality. Notably, reducing the sample thickness to 4 mm shortened the drying time by up to 40% and reduced the specific energy consumption (SEC) from 358 to 149 kWh/kg. These findings offer valuable insights for the design of more energy-efficient freeze drying systems, with implications for sustainable milk powder production and industrial-scale process optimisation. Full article

Permeate Gap Membrane Distillation (PGMD) is an emerging desalination technology that offers a promising alternative for freshwater production, particularly in energy-efficient and sustainable applications. This review provides a comprehensive analysis of PGMD, covering its fundamental principles, heat and mass transfer mechanisms, and key challenges such as temperature and concentration polarization. Various optimisation strategies, including Response Surface Morphology (RSM), Differential Evolution techniques, and Computational Fluid Dynamics (CFD) modelling, are explored to enhance PGMD performance. The study further discusses the latest advancements in system design, highlighting optimal configurations and the integration of PGMD with renewable energy sources. Factors influencing PGMD performance, such as operational parameters (flow rates, temperature, and feed concentration) and physical parameters (gap width, membrane properties, and cooling plate conductivity), are systematically analysed. Additionally, the techno-economic feasibility of PGMD for large-scale freshwater production is evaluated, with a focus on cost reduction strategies, energy efficiency, and hybrid system innovations. Finally, this review outlines the current limitations and future research directions for PGMD, emphasising novel system modifications, improved heat recovery techniques, and potential industrial applications. By consolidating recent advancements and identifying key challenges, this paper aims to guide future research and facilitate the broader adoption of PGMD in sustainable desalination and water purification processes. Full article

As the demand for advanced cooling solutions increases with the rise in artificial intelligence and high-performance computing, efficient thermal management becomes critical, particularly for data centers and electronic systems. Triply Periodic Minimal Surface (TPMS) heat sinks have shown superior thermal performance over conventional designs by enhancing heat transfer efficiency. In this study, a novel Fischer–Koch-S (FKS) TPMS heat sink was experimentally tested with four porosity configurations, 0.6 (identified as P6), 0.7 (identified as P7), 0.8 (identified as P8), and a gradient porosity ranging from 0.6 to 0.8 (identified as P678) along the flow direction, under a mass flow rate range of 0.012 to 0.019 kg/s. Key thermal parameters including surface temperature, thermal resistance, heat transfer coefficient, and Nusselt number were analyzed and compared to the conventional straight-channel heat sink (SCHS) using numerical modeling. Among all configurations, the P6 design demonstrated the best performance, with surface temperature differences ranging from 13.1 to 14.2 °C at 0.019 kg/s and a 54.46% higher heat transfer coefficient compared to the P8 design at the lowest mass flow rate. Thermal resistance decreased consistently with an increasing mass flow rate, with P6 achieving a 31.8% reduction compared to P8 at 0.019 kg/s. The P678 gradient design offered improved temperature uniformity and performance at higher mass flow rates. Nusselt number ratios confirmed that low-porosity and gradient TPMS designs outperform the SCHS, with performance advantages increasing as the mass flow rate rises. To further enhance the experimental process, a deep learning model based on a Temporal Convolutional Network (TCN) was developed to predict steady-state surface temperatures using early-stage time-series data, to reduce test time and enable efficient validation. Full article

Mixed ionic–electronic conducting (MIEC) oxygen-permeable membranes have emerged as a frontier in oxygen separation technology due to their high efficiency, low energy consumption, and broad application potential. In recent years, computational fluid dynamics (CFD) has become a pivotal tool in advancing MIEC membrane technology, offering precise insights into the intricate mechanisms of oxygen permeation, heat transfer, and mass transfer through numerical simulations of coupled multiphysics phenomena. In this review, we comprehensively explore the application of CFD in MIEC membrane research, heat and mass transfer analysis, reactor design optimization, and the enhancement of membrane module performance. Additionally, we delve into how CFD, through multiscale modeling and parameter optimization, improves separation efficiency and facilitates practical engineering applications. We also highlight the challenges in current CFD research, such as high computational costs, parameter uncertainties, and model complexities, while discussing the potential of emerging technologies, such as machine learning, to enhance CFD modeling capabilities. This study underscores CFD’s critical role in bridging the fundamental research and industrial applications of MIEC membranes, providing theoretical guidance and practical insights for innovation in clean energy and sustainable technologies. Full article

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

An analysis of entropy generation and a performance comparison are carried out for a solid oxide fuel cell with an embedded porous pipe in the air supply channel of a mono-block-layer-build geometry (MOLB-PPA SOFC) and a planar geometry with trapezoidal baffles inside the fuel and air channels (P-TBFA SOFC). The results for power density at different current densities are discussed. Also, a comparison of the field of species concentration, temperature, and current density on the electrode–electrolyte interface is analyzed at a defined power density. Finally, a comparison of maps of the local entropy generation rate and the global entropy generation due to heat transfer, fluid flow, mass transfer, activation loss, and ohmic loss are studied. The results show that the MOLB-PPA SOFC reaches a 7.5% higher power density than the P-TBFA SOFC. Furthermore, the P-TBFA SOFC has a more homogeneous temperature distribution than the MOLB-type SOFC. The entropy generation analysis indicates that the MOLB-PPA SOFC exhibits lower global entropy generation due to heat transfer compared to the P-TBFA SOFC. The entropy generation due to ohmic losses is predominant for both geometries. Finally, the total irreversibilities are 24.75% higher in the P-TBFA SOFC than in the MOLB-PPA SOFC. Full article

Pyrolysis is an important methodology for achieving efficient and clean utilization of coal. Lump coal pyrolysis demonstrates distinct advantages over pulverized coal processing, particularly in enhanced gas yield and superior coke quality. As a critical parameter in lump coal pyrolysis, particle size significantly influences heat transfer and mass transfer during pyrolysis, yet its governing mechanisms remain insufficiently explored. This research systematically investigates pyrolysis characteristics of the low-rank coal from Ordos, Inner Mongolia, across graded particle sizes (2–5 mm, 5–10 mm, 10–20 mm, and 20–30 mm) through pyrolysis experiments. Real-time central temperature monitoring of coal bed coupled with advanced characterization techniques—including X-ray diffraction (XRD), Raman spectroscopy, Brunauer–Emmett–Teller (BET) analysis, scanning electron microscopy (SEM), gas chromatography (GC), and GC–mass spectrometry (GC-MS)—reveals particle-size-dependent pyrolysis mechanisms. Key findings demonstrate that the larger particles enhance bed-scale convective heat transfer, accelerating temperature propagation from reactor walls to the coal center. However, excessive sizes cause significant intra-particle thermal gradients, impeding core pyrolysis. The 10–20 mm group emerges as optimal—balancing these effects to achieve uniform thermal attainment, evidenced by 20.99 vol% peak hydrogen yield and maximum char graphitization. Tar yield first demonstrates a tendency to rise and then decline, peaking at 14.66 wt.% for 5–10 mm particles. This behavior reflects competing mechanisms: enlarging particle size can improve bed permeability (reducing tar residence time and secondary reactions), but it can also inhibit volatile release and intensify thermal cracking of tar in oversized coal blocks. The BET analysis result reveals elevated specific surface area and pore volume with increasing particle size, except for the 10–20 mm group, showing abrupt porosity reduction—attributed to pore collapse caused by intense polycondensation reactions. Contrasting previous studies predominantly focused on less than 2 mm pulverized coal, this research selects large-size (from 2 mm to 30 mm) lump coal to clarify the effect of particle size on coal pyrolysis, providing critical guidance for industrial-scale lump coal pyrolysis optimization. Full article

The storage enclosures are vital for stabilizing the micro-environment within, facilitating preventive conservation efforts, and enabling energy savings by reducing the need for extensive macro-environmental control within the room. However, real-time conformity monitoring of the micro-environment to ensure compliance with preventive conservation specifications poses a practical challenge due to a limitation in implementing physical sensors for each enclosure. This study aims to address this challenge by using an ANN (Artificial Neural Network)-based prediction for temperature and RH (Relative Humidity) changes in response to macro-environmental fluctuations. A numerical model was developed to simulate transient heat and mass transfer between macro- and micro-environments and then employed to determine an acceptable macro-environmental range for sustainable preventive conservation and to generate a dataset to train a sequence-to-sequence ANN model. This model was specially designed for 24 h real-time prediction of heat and mass transfer and to simulate the micro-environmental conditions under varying levels of control accuracy over the macro-environment. The effectiveness of the prediction model was tested through a real trial application in the laboratory, which revealed a robust prediction of micro-environments inside different enclosures under various macro-environmental conditions. This modeling approach offers a promising solution for monitoring the micro-environmental conformity and further implementing the relaxing control strategy in the macro-environment without compromising the integrity of the collections stored inside the enclosures. Full article