Search Results (216)

Air-to-air heat exchangers are vital for energy recovery and thermal management but often suffer from reduced effectiveness, high pressure losses, and increased pumping power in conventional designs. Advances in additive manufacturing have enabled nature-inspired geometries, such as lattice and triply periodic minimal surface (TPMS) structures, which enhance heat transfer through complex first-order surfaces but are frequently associated with excessive pressure drops. This study proposes an optimized higher-order harmonic heat transfer surface tessellation developed through an optimization framework integrating analytical and numerical methods. The goal is to improve the overall thermal–hydraulic performance of the heat exchanger over a range of operating conditions. Results of the sensitivity analysis show that secondary surface modification of this type can yield a significant increase in effectiveness reaching up to 14%, although with an associated increase in the pressure drop. Additionally, we show that the optimized second-order harmonic-type structure achieved relatively higher effectiveness for comparable but slightly lower pressure drop than the gyroid structure across the flow regimes considered. While the gyroid structure outperformed the optimized harmonic-type structure in terms of the fin geometry heat transfer efficiency assessed based on the London goodness factor, $j/f^{1/3}$ by a factor of 1.9, the optimized harmonic-type structure had relatively higher energy efficiency than that of the gyroid structure across the entire spectrum of operating conditions reaching up to a factor of about 1.6 in the laminar flow regime. The findings from this study demonstrate a balanced pathway for additively manufacturable, high-performance air-to-air heat exchangers, offering compact, energy-efficient solutions for applications in building ventilation, aerospace, and electronics cooling. Full article

Hydraulic fracturing is a key reservoir stimulation technology for enhancing hydrocarbon recovery from unconventional reservoirs. However, fracture initiation and propagation are governed by complex thermo-hydro-mechanical (THM) coupling processes, which strongly influence fracture geometry, propagation dynamics, and overall reservoir stimulation efficiency. In this study, a fully coupled THM numerical model is developed to investigate the multiphysics mechanisms governing hydraulic fracture initiation, propagation, and evolution under realistic reservoir conditions. Beyond hydraulic fracturing applications, the proposed framework can also be extended to analyze fracture propagation, multiphase fluid migration, and coupled rock–fluid interactions in subsurface energy systems, including geological carbon storage and geothermal energy extraction. The simulation results demonstrate that reservoir temperature and reservoir pressure significantly influence fracture propagation by altering the pressure differential between the hydraulic fracture and the in situ stress field. Elevated reservoir pressure promotes fracture extension, whereas elevated reservoir temperature suppresses fracture propagation by increasing fluid leak-off and accelerating pressure dissipation. Furthermore, molecular-scale analyses are conducted to interpret how thermodynamic conditions influence intermolecular interactions, fluid behavior, stress transfer, and fracture evolution, and the macroscopic simulation results are interpreted in light of molecular-scale hypotheses drawn from previous literature concerning intermolecular interactions, hydrogen bond network evolution and polymer adsorption. These findings provide new insights into the coupled THM mechanisms controlling fracture evolution and establish a theoretical basis for optimizing hydraulic fracturing strategies, improving energy recovery efficiency, and advancing subsurface energy engineering applications. The proposed framework also provides valuable guidance for the sustainable development of unconventional hydrocarbon resources, enhanced geothermal systems, and geological carbon storage technologies, thereby contributing to long-term energy security and sustainable energy supply. Full article

Double-skin façades (DSFs) offer a promising building-envelope strategy for improving thermal management by promoting heat’s removal from the façade cavity before it is transferred toward the indoor environment. This study numerically investigates the influence of cavity width on the thermo-fluid performance of a mechanically ventilated DSF under summer operating conditions. A two-dimensional computational fluid dynamics (CFD) model was developed using the RNG k-ε turbulence model together with the discrete ordinates radiation model. Mechanical ventilation was imposed through a velocity inlet of 0.765 m s⁻¹, with an inlet air temperature of 17 °C and a solar radiation intensity of 365.4 W·m⁻². The numerical model was validated against published experimental temperature measurements, yielding an average absolute relative error of approximately 5.65%. The validated model was subsequently applied to cavity widths ranging from 0.10 to 0.70 m. Increasing the cavity width substantially modified the airflow development and thermal field. The monitored temperature decreased from 31.66 °C at 0.10 m to 17.64 °C at 0.50 m, while further enlargement produced only minor reductions to 17.43 and 17.28 °C at 0.60 and 0.70 m, respectively. The total heat-transfer rate increased from approximately 1000 W at 0.10 m to a maximum of 1388 W at 0.50 m before slightly decreasing to 1379 and 1376 W at 0.60 and 0.70 m, respectively. This temperature reduction enhances heat removal from the façade cavity, helping to limit heat transfer toward the indoor environment and improve indoor thermal comfort under summer conditions. These results demonstrate a non-monotonic relationship between cavity width and heat-removal performance, with 0.50 m providing the highest heat-transfer rate among the investigated configurations. This result is specific to the geometry, boundary conditions, ventilation rate, and operating conditions considered in the present study. It should not be interpreted as a universally optimal cavity width for mechanically ventilated DSFs. The findings highlight the importance of cavity-width selection in the thermal management and design of mechanically ventilated DSFs for energy-efficient building envelopes. Full article

Solar thermal calorimeters serve as foundational reference instruments for quantifying absorbed thermal power, evaluating optical-to-thermal conversion efficiency, and validating computational models in concentrated solar thermal (CST) research. This review synthesizes recent numerical, thermo-hydraulic, and experimental advancements, evaluating the evolution from basic single-tube configurations to high-confinement cavity calorimeters (achieving apparent absorptances $>0.99$) and flat-plate architecture enhanced with impinging jets and internal fin arrays. The selection of working fluids is examined; water accounts for approximately $87\%$ of reported implementations due to low property uncertainty, whereas synthetic oils and gaseous coolants expand operating temperature ranges at the expense of thermochemical degradation and parasitic pumping penalties. Furthermore, critical thermo-hydraulic challenges induced by extreme non-uniform heat fluxes are identified, including structural thermal bowing, parallel-channel flow maldistribution, recirculation traps, and buoyancy-driven instabilities occurring at Richardson numbers $\mathrm{Ri}\gg 10$. Metrological constraints related to solar reflection interference in non-contact thermometry and calibration drift in photometric target arrays are also critically addressed. Finally, key strategic research directions are outlined, emphasizing high-temperature advanced materials, active flow equalization, real-time multi-physics digital twins, and standardized dynamic testing metrology. Full article

This research numerically evaluates the effects of various geometric parameters on the optimization of flow reversers on a rotating spiral heat exchanger utilizing computational fluid dynamics. There were three important geometric parameters investigated to find their impact on thermo-hydraulic characteristics: (i) distribution frequency of reversers along the spiral channel, (ii) reverser turn diameter, and (iii) reverser tube diameter. Thermo-hydraulic characteristics of the proposed designs were studied according to the velocity and temperature distributions, average Nusselt number, pressure drop, and performance evaluation criterion. From all the evaluated designs, the reduction in reverser tube diameter resulted in the most desirable performance as the average Nusselt number increased about 44.6% while the pressure drop increased merely by 10% compared to the base design. Thus, the obtained design gave the best performance evaluation criterion of around 1.77. Decrease in reverser turn diameter led to an increase in Nusselt number about 6.3%, although with 40% increase in pressure drop, resulting in the worst performance evaluation criterion of approximately 1.20. Overall, the results show that proper geometric optimization of flow reversers is able to noticeably enhance the thermo-hydraulic performance of the heat exchangers. Full article

Existing piston–cylinder lubrication studies often simplify the pressure boundary as a constant load or a single field, making it difficult to capture pump-level pressure excitation, local oil-film response, and non-concentric posture under variable loading. This paper establishes a thermo–fluid–solid coupling framework integrating an AMESim full-pump model, a Fluent transient oil-film model, and a Transient Structural model; UDF transfer of periodic pressure, dynamic meshes, and a calibrated Roelands law were used to analyze parallel-offset and center-tilted postures. As the load pressure increased from 10 to 30 MPa, the maximum discharge–half-cycle temperature rose from 28.39 to 36.95 °C, and the average positive leakage during the third-cycle high-pressure stage increased from 0.0201 to 0.1026 L/min; increasing speed from 1000 to 3000 r/min reduced cycle-averaged leakage by 8.93%. At 500 r/min and 30 MPa, the parallel-offset case reached 46.34 °C, 41 kPa, and 0.0990 L/min in maximum temperature, maximum shear stress, and average leakage, whereas the center-tilted case produced a peak resultant force of 3537.12 N, a cylinder inner-wall high-stress band of 76.96 MPa, and a maximum piston deformation and equivalent stress of 4.31 μm and 83.16 MPa. These results clarify the distinct lubrication behavior and potential uneven-wear risk associated with the two representative non-concentric postures, and provide a basis for clearance design and posture-sensitive condition assessment of axial piston pumps. Full article

Accurate prediction of coupled heat-transfer and fluid-flow phenomena is essential for the thermal design, performance assessment, and optimization of heat exchangers (HXs). Among key HXs, concentric tube heat exchangers (CTHXs) are widely used in thermal energy systems, where their performance is governed by the coupled interaction of fluid flow and heat transfer. Although computational fluid dynamics (CFD) provides detailed insights into these transport phenomena, its high computational cost limits its applicability in design optimization and real-time monitoring applications. To overcome this limitation, the present study proposes a physics-guided neural network (PGNN) for the accurate and efficient prediction of CTHX thermo-hydraulic performance, including the overall heat-transfer coefficient (U) and the pressure drops of the cold (ΔP_c) and hot (ΔP_h) streams. The PGNN introduces correlation-based physical guidance through established Nusselt number, overall thermal-resistance, and Darcy–Weisbach pressure-drop relations. Accordingly, the proposed framework is a correlation-guided PGNN rather than a residual-based physics-informed model, because the local conservation-equation residuals are not explicitly enforced during training. For comparison, a standard artificial neural network (ANN) with the same architecture and input parameters was also developed. Both models were trained on a dataset generated from 1575 CFD simulations covering a wide range of operating and geometric conditions, including the Reynolds and Prandtl numbers of both fluids, inner and outer tube diameters, and inlet temperatures. A comprehensive error analysis demonstrates the superior predictive capability of the PGNN over the ANN under various flow and geometric conditions. On the unseen test dataset, the PGNN achieved mean absolute percentage errors of 2.03%, 1.09%, and 1.11% for predicting U, ΔP_c, and ΔP_h, respectively. The proposed PGNN therefore provides a reliable, high-fidelity, and computationally efficient alternative to CFD, supporting the analysis, optimization, and operation of thermal energy systems. Full article

The oral cavity represents one of the most demanding operating environments for biomedical coatings, subjecting materials to constant masticatory shear and tribological stress, dynamic temperature fluctuations, salivary enzymatic activity, and continuous fluid turnover that collectively challenge coating adhesion, durability, and longevity. Thermosensitive hydrogels that undergo reversible sol–gel transitions near body temperature offer a uniquely versatile platform for in situ coating formation on complex oral surfaces, enabling minimally invasive application and conformal coverage of irregular anatomical structures—from periodontal pockets and root canal systems to extraction sockets and bone defects. This review examines the application of thermo-sensitive hydrogel coatings across six major oral disease categories: periodontitis, peri-implantitis, bone defects, endodontic diseases, extraction wounds, and oral cancer. We further discuss practical hurdles facing clinical translation, noting that sterilization often degrades these materials, mechanical properties may prove inadequate under masticatory loads, and long-term biosafety data remains limited. This review critically evaluates how these smart coatings can bridge the gap between laboratory innovation and clinical application, offering insights to guide the development of next-generation precision therapies for oral diseases. Full article

Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with higher-level neural interpretation that places those signals in context. Inspired by this layered architecture, we present Mechanics-AI, an open-source framework that mirrors the same sensing-then-interpretation logic computationally. A first learning layer (ML1) emulates expensive finite-element, computational fluid dynamics and multiphysics simulations to produce interpretable physical metrics such as stress, strain, shear, and thermal and moisture fields, while a second layer (ML2) fuses these metrics with heterogeneous real-world metadata to predict categorical outcomes and design recommendations. Eight algorithms are benchmarked automatically, the most accurate is selected for each task, and Shapley additive explanations expose the dominant physical drivers to preserve interpretability. The framework is demonstrated across three independent domains using a single unchanged pipeline: forensic traumatic brain injury prediction, optimisation of a bio-inspired humanoid bioreactor for tissue engineering, and a zero-emission building (ZEBAI) framework that couples thermo-hygro-mechanical simulation with Sobol-sampled surrogate modelling to design sustainable, low-carbon envelopes from recycled aggregate concrete by balancing structural safety, energy and embodied carbon. Despite entirely different physics, data and objectives, the same architecture generalises across all three, showing that bio-inspired, layered coupling of mechanistic simulation and contextual learning offers a reusable, interpretable route to cross-domain engineering prediction and sustainable design. Full article

Pump start-up and shutdown, flow-rate adjustment, and tripping operations during drilling can induce bottomhole pressure fluctuations. These fluctuations may alter fracture aperture and change the development of lost-circulation pathways. To investigate the dynamic evolution of fracture aperture under fluctuating pressure, a thermo-hydro-mechanical (THM) coupled numerical model was established using ABAQUS. Bottomhole pressure fluctuations were induced by applying periodic perturbations to the inlet flow rate. The effects of fluctuation amplitude, fluctuation duration, and drilling-fluid temperature were then analyzed. The results indicate that fracture aperture exhibits a transient response before reaching a stable state. The fluctuation amplitude has a significant effect on the maximum transient fracture aperture. When the fluctuation amplitude increases to 30%, the maximum fracture aperture increases by 44%. In contrast, the fracture that has already formed may undergo reclosure during the low-pressure stage. The fluctuation duration mainly affects the persistence of the fracture opening and reclosure process, but has a relatively weak effect on the maximum fracture aperture. A decrease in drilling-fluid temperature promotes fracture opening and tip propagation. When the formation temperature is 100 °C, low-temperature drilling fluid increases the maximum fracture aperture by 3.06% and the fracture length by 13.89% compared with the isothermal reference case. These findings indicate that fracture aperture under fluctuating pressure cannot be characterized only by its stabilized value. The maximum transient fracture aperture, minimum fracture aperture, and temperature-induced changes in fracture morphology should also be considered. This study provides a numerical insight into the transient response of fracture aperture to bottomhole pressure fluctuations and drilling-fluid temperature during drilling in stress-sensitive fractured formations. Full article

Computational fluid dynamics provides detailed spatial distributions of physical fields in biomass boiler combustion, but the computational cost of each simulation limits its application in parametric studies and near-real-time workflows. This work investigates whether a Graph Neural Operator (GNO) can serve as a fast surrogate model that maps boiler operating parameters to six coupled CFD field distributions simultaneously. The reference case is a 10 kW wood-pellet boiler with internal flue gas recirculation (FGR), described and experimentally validated in an earlier publication by the authors. CFD data were collected on the symmetry plane of the combustion chamber for 80 operating points defined by the thermal load ratio (P/P₀) and the excess air ratio λ. A GNO surrogate was trained on 64 cases to predict temperature, velocity magnitude, static pressure, and mole fractions of CO, O₂, and CO₂ at each node of an unstructured spatial graph. On a held-out validation set of 16 operating cases, the model achieved R² values of 0.988 for temperature, 0.919 for velocity magnitude, 0.982 for pressure, 0.999 for CO, 0.992 for O₂, and 0.985 for CO₂. After training, each prediction is generated in a single forward pass, providing a computationally efficient approximation compared to the full CFD solver. A dedicated generalisation study on independent off-grid CFD cases confirmed that the surrogate interpolates within the parameter domain with essentially no loss of accuracy and degrades only moderately when extrapolated towards a higher thermal load and leaner mixtures. The results demonstrate that a baseline GNO surrogate can capture the spatial structure of coupled thermo-fluid and species fields in a realistic combustion geometry within the investigated parameter range and suggest applicability to digital-twin-oriented workflows where repeated parametric queries of boiler operation are required. Full article

In response to the challenges encountered in high-throughput resilience assessment of zonal ship central cooling systems, including numerical stiffness in physics-based dynamic models, abnormal solver termination, and insufficient continuity in batch simulation campaigns, a physics-informed co-simulation framework for resilience-oriented assessment is proposed. With control–physics orthogonal decoupling as its core, the framework separates the control-scheduling layer from the thermo-hydraulic solver at the software-execution level, while retaining information exchange through standardized interfaces. In addition, physics constraint-based pre-filtering, process-level fault isolation, and automatic recovery mechanisms are integrated to improve the robustness and continuity of automated batch assessment. A hierarchical reduced-order thermo-hydraulic model of the zonal ship central cooling system is established. Subsequently, the numerical stiffness characteristics of the fluid network and heat-transfer units under valve topology switching conditions are analyzed. A standalone C++ solver kernel is generated from a Simulink prototype model, and a Java/Web-based collaborative scheduling platform is constructed. Cross-environment consistency tests show that the C++ solver reproduces the Simulink prototype results under representative fast hydraulic and slow thermal scenarios, with steady-state and transient discrepancies below 0.05% and 1.08%, respectively. Physics constraint-based pre-filtering intercepted 42.6% of infeasible samples and reduced the total wall-clock runtime of the tested optimization task by approximately 38%. In 1000 fault-injection tests, the process-isolation mechanism isolated 12 abnormal solver terminations, while the main scheduling process remained alive and the remaining batch tasks were completed under the tested conditions. Finally, an abrupt pulse thermal-load increase in the forward zone was used as a representative scenario to demonstrate automatic extraction of temperature trajectories and quantitative evaluation using the cumulative temperature-exceedance severity (CTS) index. The results indicate that the proposed framework can support offline resilience-oriented assessment, reconfiguration-strategy screening, and batch evaluation of shipboard fluid–thermal systems under the tested conditions. Full article

Rock-bed thermal energy storage (RTES) systems are attracting growing interest as low-cost, robust, and scalable sensible heat storage solutions for applications ranging from low-temperature building and greenhouse heating to medium- and high-temperature solar or waste-heat recovery systems. However, their thermo-hydraulic performance is strongly influenced by the complex interactions among heat-transfer-fluid flow, irregular rock morphology, porosity, pressure drop, interphase heat transfer, and transient thermal-front development. This review provides a focused evaluation of computational fluid dynamics (CFD) modelling strategies for packed beds of rocks, with particular attention to continuum porous-medium approaches and the closure correlations required for reliable simulation. First, the distinction between pore-scale and volume-averaged continuum modelling is discussed in terms of the trade-off between physical resolution and computational feasibility. The main pressure-drop and friction-factor correlations are then reviewed and compared, including classical packed-bed models and rock-bed-specific formulations. It is shown that hydraulic-resistance predictions are highly sensitive to particle shape, surface roughness, porosity, the bed-to-particle diameter ratio, and packing arrangement. Particle-fluid heat-transfer correlations are also examined and, when possible, converted into a consistent particle Nusselt-number form to enable direct comparison. Particular attention is given to generalized correlations, dispersion-corrected models, and air–rock-bed correlations applicable to thermal storage systems. Finally, a methodological framework for modelling RTES systems using local thermal equilibrium (LTE) and local thermal non-equilibrium (LTNE) formulations is proposed. Dimensionless criteria, including the interphase thermal coupling number and particle Biot number, are introduced to support the selection between LTE and LTNE formulations. The selection of pressure-drop/friction-factor and solid–fluid heat-transfer/particle Nusselt-number correlations should be based on the similarity between the original experimental conditions and the target RTES system, and system-specific validation is recommended whenever possible. Full article

The safety and stability of onboard Liquid Hydrogen (LH₂) storage systems depend strongly on gas–liquid two-phase flow, heat transfer, and phase change under sloshing; however, the coupled influence of filling ratio and sloshing on thermo-hydrodynamic behavior remains underexplored. We develop a Volume of Fluid (VOF)-based two-phase Computational Fluid Dynamics (CFD) model in ANSYS Fluent to quantify interfacial dynamics, pressure response, and temperature-field evolution in LH₂ tanks subjected to sinusoidal acceleration for filling ratios from 10% to 90%. Increasing the filling ratio strengthens net condensation in the ullage and thus intensifies depressurization. As the filling ratio increases from 10% to 90%, the pressure reduction over the 2.0 s sloshing process increases from 0.418 kPa to 2.410 kPa, and the corresponding initial depressurization rate rises from 0.209 to 1.205 kPa s⁻¹. Free-surface motion decreases with filling ratio: at 10%, large interface excursions can induce gas-cavity formation and splashing, increasing the risk of intermittent propellant supply, whereas at 90% the interface is constrained and oscillations are suppressed. Higher filling ratios lead to faster ullage cooling and larger temperature oscillations. The liquid warms modestly, and its warming rate decreases nonlinearly with filling ratio, consistent with the larger effective thermal mass at higher fillings. Overall, the obtained mechanistic understanding can support the engineering design of onboard LH₂ tanks, including filling-ratio selection and thermal-management optimization under sloshing conditions. Full article

A solar air heater generates heated air using solar energy. This system has a relatively simple design, which reduces the initial cost and facilitates maintenance compared with other solar systems. However, its thermal conversion efficiency is limited by the poor thermal conductivity of air. Previous studies have improved thermal efficiency by enhancing either the heat transfer area or the heat transfer coefficient, but most have applied only one of these approaches. In this work, a novel solar air heater with longitudinal fins and blocks, designed to simultaneously enhance the heat transfer area and heat transfer coefficient, is investigated for various block shapes (rectangular, forward-chamfered, backward-chamfered, and triangular blocks) utilizing computational fluid dynamics. Compared to the smooth fin channel, heat transfer is enhanced by a maximum of 1.61 times with the backward-chamfered block, while the corresponding enhancement factors for the rectangular, forward-chamfered, and triangular blocks are 1.52, 1.46, and 1.54, respectively. The thermo-hydraulic performance parameter, which simultaneously evaluates heat transfer augmentation and frictional penalty, further indicates that the backward-chamfered block is most effective at Reynolds numbers below 6000, while the rectangular block performs best above 9000. Full article