Thermal Load Research Articles (Page 1)

A probabilistic framework with recurrent mixture density network for reliability analysis of bridge expansion joint under thermal loading

Load transfer analysis of driven energy pile under combined thermal and mechanical loading

Thermal load estimation with non-gray radiation and soot for upper stage engine

Stratified combustion optimization in ammonia/hydrogen rotary engines through asynchronous dual-direct-injection with thermal load management

Topology optimization of thermal-fluid systems with non-uniform thermal loads using a novel objective function

Purpose The purpose of this study is to evaluate the thermal and tribological performance of silica and alumina sol–gel coatings under combined thermal and structural loading. Using finite element analysis (FEA) and experimental data from a pin-on-disc (POD) tribometer, the study assesses contact parameters such as frictional stress, contact pressure and heat flux at elevated temperatures. The goal is to understand the effectiveness of these coatings in enhancing wear resistance and reducing friction, providing insights for improving material performance in high-temperature applications. Design/methodology/approach The study combines experimental and numerical approaches to evaluate coated samples. Silica and alumina coatings were applied on aluminium alloy substrates using the sol–gel dip-coating method. Tribological tests were conducted using a POD tribometer at elevated temperatures against an EN31 hardened disc to measure coefficient of friction (COF) and volumetric wear loss (VWL). FEA was performed using ANSYS Workbench with static structural and steady-state thermal templates to simulate contact pressure, frictional stress and heat flux. Scanning electron microscope (SEM) and energy dissipation X-ray spectroscopy (EDAX) analyses were conducted to examine surface morphology and elemental composition of uncoated and coated samples before and after testing. Findings The study revealed that both silica and alumina coatings significantly improved tribological performance at elevated temperatures. Coated samples showed reduced COF and lower (VWL) compared to uncoated samples. FEA results indicated decreased contact pressure, frictional stress and heat flux in coated specimens, confirming better thermal stability. SEM analysis showed smoother wear tracks on coated surfaces, while EDAX confirmed uniform coating distribution. Among the coatings, alumina demonstrated slightly superior performance. Overall, the findings highlight the effectiveness of sol–gel coatings in enhancing wear resistance and thermal performance under combined loading conditions. Originality/value This study uniquely integrates experimental tribological testing with FEA to evaluate material performance under combined thermal and structural loading. By focusing on sol–gel coated silica and alumina surfaces, it provides a comprehensive understanding of how such coatings influence wear behaviour and thermal resistance at elevated temperatures. The research offers valuable insights for industries operating in high-temperature environments, demonstrating how advanced coatings can extend component life and reduce energy loss due to friction. The dual approach enhances the reliability of the findings and contributes to the development of more durable and efficient material systems. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2025-0236/

Next-generation mobility systems—ranging from electric vertical takeoff and landing (eVTOL) vehicles to hypersonic aircraft and high-efficiency autonomous drones—require advanced aerothermal and structural optimization techniques to meet demands for safety, performance, and sustainability. Traditional computational approaches, such as finite element analysis, computational fluid dynamics (CFD), and multi-physics simulation, face increasing computational burdens due to the nonlinear interactions among thermal loads, aerodynamic behavior, material deformation, and structural integrity. This study investigates the role of quantum computing in accelerating and improving aerothermal and structural optimization workflows. Leveraging quantum algorithms, including quantum annealing, variational quantum eigensolvers (VQE), quantum approximate optimization algorithms (QAOA), and quantum-inspired multi-objective solvers, the proposed framework enhances the exploration of high-dimensional design spaces and improves the computational efficiency of complex optimization tasks. Early experiments on benchmark aerothermal–structural models demonstrate improved convergence rates, superior Pareto-optimal solutions, and enhanced predictive accuracy compared to classical methods. The findings show that quantum computing has the potential to fundamentally transform mobility system design by enabling faster iteration cycles, better thermal–structural co-optimization, and more energy-efficient architectures suitable for sustainable transportation futures.

This research develops a novel, integrated workflow to evaluate the performance of a code-compliant RC slab panel designed for gravity and lateral loads in ETABS under consecutive thermal and blast loading. A prototype one-way slab panel is designed and detailed as per Indian Standards (IS 456:2000). The critical reinforcement data and geometry are then translated into a high-fidelity finite element model in ANSYS Workbench for advanced nonlinear analysis. A sequentially coupled thermo-mechanical simulation is performed. First, a Transient Thermal analysis applies the ISO-834 standard fire curve for one hour to induce thermal degradation. Subsequently, an Explicit Dynamics analysis subjects the fire-damaged slab to a blast load, modeled using the CONWEP methodology with a centrally-located charge. The results quantify the severe performance degradation in the combined fire-blast scenario compared to isolated events. The study concludes by deriving resilience-oriented design and detailing recommendations for RC slabs in disaster-prone urban environments. The primary contribution of this work is the established ETABS-to-ANSYS workflow, providing a practical tool for engineers to assess the multi-hazard resilience of as-designed structural elements.

Abstract In recent years, the manufacturing industry has increasingly emphasized sustainable machining practices to mitigate environmental pollution and enhance operator safety. One of the sustainable techniques widely explored in the past few years is cryogenic cooling. Cryogenic cooling has emerged as a highly effective and environmentally benign alternative. The application of liquid nitrogen (LN2) as a cryogenic coolant significantly influences tool performance and process efficiency by reducing the temperature at the tool–chip–workpiece interface, improving dimensional accuracy, minimizing tool wear, decreasing cutting forces, and enhancing surface integrity. This paper primarily reviews previous and current research articles on cryogenic cooling techniques in machining operations, including turning, milling, drilling, and grinding, for various difficult-to-machine materials such as titanium alloys, nickel-based alloys, tool steels, stainless steels, ceramics, and composites. The discussion highlights the limitations of conventional cutting fluids, challenges encountered during machining of these materials, and the comparative performance of LN2 and LCO2 cooling systems. The outcomes from reviewed studies reveal that cryogenic cooling substantially improves machinability, chip breakability, and surface quality while reducing thermal and mechanical loads on the cutting tool. Furthermore, the paper identifies recent advances in hybrid cryogenic approaches and outlines potential future research directions to enhance the sustainability and industrial adaptability of cryogenic machining systems.

Long-endurance hypersonic vehicles face the dual challenge of withstanding extreme aerodynamic heating while meeting onboard power requirements. Integrating thermoelectric generators within thermal protection systems offers a solution by converting thermal loads into electrical power. However, accurate prediction requires resolving coupled multiphysics, where three-dimensional simulations are computationally prohibitive and existing one-dimensional models lack accuracy. This study develops a quasi-two-dimensional distributed thermal network incorporating shape-factor corrections for rapid, high-fidelity prediction. Multi-objective optimization is performed to balance specific power, thermal expansion mismatch, and thermal margin. Analysis reveals fundamental trade-offs: a maximum-power design achieves 28.1 W/kg but only a 0.8% thermal margin, whereas a balanced design delivers 24.5 W/kg with a 5.1% thermal margin and significantly reduced thermal stress. Despite geometric variations, peak conversion efficiency converges to approximately 13%. This indicates that efficiency is primarily governed by material properties, while geometric optimization effectively tunes temperature and thermal strain distributions, providing guidelines for reliable system development.

Solid Oxide Fuel Cells (SOFC) are a promising technology for the future of energy production in that they offer high efficiencies and generate low emissions relative to traditional combustion technology. Coupling SOFC technology within a traditional gas turbine Brayton cycle offers even more improvement in system efficiency as the turbine is able to utilize the high-quality heat from the cell to extract additional work. These hybrid SOFC systems have been demonstrated for energy production on the ground, but have recently gained attention as a potential candidate for electric aviation. Use of these hybrid SOFC systems for propulsion have been shown to reduce both carbon and NOx emissions while providing competitive mission ranges for both commercial and specialized sectors. Though these results are promising, much work still needs to be done to prove that hybrid SOFC technologies are a viable option for use in aviation. Much of the future work with this technology deals with how the SOFC will be able to handle the transient loads (thermal and mechanical) that are seen during flight. For example, during take-off and landing large mechanical forces are transmitted through the aircraft structure. These forces will inevitably be transmitted to the fuel cells, and their effects need to be mitigated as they have the potential to permanently damage the cells.This work serves to address the thermal loads that the SOFC will experience during flight. Conventional ground based SOFC systems are usually ran at a constant operating condition for long periods of time. The main transient periods for these systems are during start-up and shutdown, though these are uncommon occurrences. Onboard a commercial aircraft, these cells will need to be warmed-up and cooled down multiple times in a single day as the aircraft will need to have its engines shutdown during passenger boarding. Furthermore, these transient warm-up period will need to be expedited (typically ~15 min.) compared to the hours of warm-up that ground systems can afford. These cyclic loadings will cause the cells to experience large thermal gradients that can cause stresses. These thermal loads will also cause the electrodes to degrade at a faster rate than they would experience during constant operation. To understand effects of thermal degradation of the electrode and its effects on cell performance, it is required to develop a multi-scale modelling environment that can couple the change in the SOFC electrode microstructure with an electrochemical model that can measure cell performance.To understand and model the thermal degradation of the SOFC electrodes, the phase field method is employed to investigate the microstructural evolution of the cell as a function of time. This work primarily focuses on the Ni-YSZ anode, where the main avenue of degradation is the agglomeration of Ni particles due to Ostwald ripening. This coarsening will cause Ni particles to merge into large clusters. This will reduce the triple phase boundary (TPB) of the anode, a linear network of overlap between all 3 phases of the anode where the electrochemical reaction is able to take place. The agglomeration of Ni will also decrease the overall electronic conductivity of the anode as there will be a loss in Ni connectivity to the current collector. Phase field methods will be able to capture the evolution of the Ni-YSZ grains with respect to time. GeoDict is used to produce realistic microstructures that will be used as initial conditions for the phase field method. This software can generate a microstructure as a function of particle size distributions, grain geometry, cell grading, and thickness. These structures will then be fed to phase field models that can evolve the structure as a function of temperature and time. GeoDict also offers several useful algorithms for computing important parameters like the TPB, conductivities (thermal and electronic), and porosity. These will be used as inputs for the electrochemical model providing a direct linkage to operating conditions and cell performance degradation.Once a cell has been degraded over time, it is important to understand the combined impact that all microstructural property evolutions have on single cell performance. To capture this, MOOSE is used to simulate a typical cell where the TPB, conductivities, and porosity are used as coefficients for the electrochemical partial differential equations. With these coefficients generated at each point in time (measured form GeoDict), the maximum cell power density can be calculated. This serves as a direct input to hybrid SOFC system models, allowing a prediction of system level degradation based on operating conditions in flight. Figure 1

Abstract The thermal management systems of new energy vehicles (NEVs) have become increasingly complex due to the integration of multiple heat sources, such as batteries, motors, and controllers, each with distinct heat dissipation rates and temperature requirements. Traditional flow regulation mechanisms, relying on external power supplies, add to the system's volume, weight, and control complexity. To address these challenges, this study introduces a novel flow regulator driven by shape memory alloy (SMA) springs. The SMA spring-based regulator enables self-adaptive flow regulation through phase changes caused by temperature variations, eliminating the need for an external power supply. This simplifies system design while enhancing responsiveness and adaptability. Computational fluid dynamics (CFD) simulations were used to compare the performance of circular and square valve plate designs, revealing that the square valve plate offers superior flow characteristics, including lower pressure drops and better flow uniformity. Experimental studies confirmed the effectiveness of the SMA spring-based regulator in dynamically adjusting flow under varying thermal loads. The results indicated that the regulator reduced temperature fluctuations at the outlets of two heat sources by 9% and 15%, respectively, demonstrating its ability to optimize flow distribution and stabilize system performance.

Abstract The study aimed to improve the water condensation process in a pyramidal solar still through thermal techniques, focusing on Karbala, Iraq’s hot and dry climate. Various cooling methods—including air and thermoelectric cooling with water were integrated and tested in a multi-stage pyramid-shaped solar still to enhance condensation on separate glass surfaces. The system uniquely combines two cooling techniques to address the high thermal load resulting from multiple condensation surfaces. Air cooling (2–8 m/s) and water cooling (105–620 W) were evaluated. Air cooling was applied at speeds of 2, 4, 6, and 8 m/s with corresponding wattages of 80, 120, 160, and 200 W. Water cooling with thermoelectric and heat sink methods involved wattages of 105, 210, 315, and 410 W for each condensation glass, with initial solar radiation intensity measured at 995 W/m² on 24 May 2024. Air cooling increased condensation speed by up to 12% at noon, aided by the dry environment. Water temperature in the basin without cooling reached 65°C, dropping to 53.3°C with a maximum 620 W cooling power consumption. Productivity analysis showed a 48.3% improvement in the morning at an input power of 330 W, which increased to 55% at 620 W. The system achieved a maximum water productivity of 2797 mL/m², with an estimated production cost of 0.078 USD per liter. However, increased energy consumption for cooling reduced overall thermal efficiency due to larger condensation areas in the pyramid solar still requiring more energy, despite enhancing water productivity.

Rising energy demand for building cooling exacerbates the environmental challenges associated with energy consumption. Incorporating phase change materials (PCMs) into building envelopes, particularly sun‑exposed roofs, can substantially reduce energy use. This study examines the thermal‑storage efficiency of metallic, spherical PCM modules embedded within a reinforced concrete roof, designed for hot‑climate conditions. The roof is divided into four distinct thermal zones: Zone‑1 (conventional concrete), Zone‑2 (empty spherical modules), and Zones 3–4 (modules filled with organic PCMs, organic mixture, 35 °C (OM35) and organic mixture, 37 °C (OM37)). Important thermal performance metrics, such as temperature distribution, heat flux, thermal load, time lag, decrement factor, key response index, and carbon emissions savings, are evaluated. Integrating spherical PCM modules led to significant improvements. These include an average reduction in indoor surface temperature of 10.2 °C, a decrease in cooling load of upto 69%, and a reduced decrement factor. In addition, OM35 showed a higher key response index and enhanced thermal performance than OM37. The findings demonstrate the practical viability of spherical PCM‑integrated roofs as a passive‑cooling strategy for buildings in hot climates.

The objective of this study was to improve the accuracy of performance verification and reduce the time required for long-term ground service life testing by increasing the thermal and dynamic load on the afterburner above operational values (increased temperature and pressure in the afterburner). This study relates to transport engineering, specifically to aircraft gas turbine engines, and is applicable to ground testing of afterburners on test rigs and airfields. During testing, a computer program disconnects the air supply from the gas turbine engine compressor via the afterburner fuel flow regulator and connects it to the process air supply from an independent fuel supply system via a pressure regulator. The process air pressure at the pressure regulator outlet from the independent fuel system changes compared to the air pressure downstream the gas turbine engine compressor, as per the program, increasing the fuel flow through the afterburner above operational values. The process air pressure changes N = k · R/T times, where k is the average number of afterburner ignitions per flight, R is the service life of the gas turbine engine, T is the average time of one flight according to the cyclogram.

Abstract To address the extreme aerodynamic heating challenges encountered by the leading edges of hypersonic vehicles, this study develops an aerogel-based thermal insulation material with engineering applicability. It proposes three deep neural operator models, Fourier Neural Operator, Deep Operator Network (DeepONet), and Transformer, for rapid prediction of the temperature field. These models establish an end-to-end mapping from multiple design parameters to the spatial temperature distribution. A global sensitivity analysis involving coupled design parameters is conducted to investigate the influence of different variables on thermal insulation performance. Results demonstrate that all three neural operator models achieve a maximum temperature prediction error of less than 5%, with prediction times reduced to the second level, representing a four-order-of-magnitude acceleration compared to conventional computational fluid dynamics methods. Furthermore, the Fourier Neural Operator model is employed as a surrogate to explore the impact of multiparameter design on thermal insulation performance. Sensitivity analysis indicates that thermal load and thermophysical properties (heat conduction phase and radiative attenuation) dominate the system response, contributing 87–91% of the total variance. The proposed neural operator framework offers a flexible and efficient alternative for predicting temperature fields in aerogel-based insulation systems, overcoming the limitations of traditional computational fluid dynamics methods in handling high-dimensional input spaces and providing valuable guidance for designing and optimizing advanced thermal insulation materials.

Compact accelerator-driven neutron sources (CANSs) demonstrate significant potential for applications in both scientific and industrial fields. A critical challenge for further expanding CANS’s applications is to improve the neutron yield, which requires a highly efficient thermal dissipation ability for neutron targets to bear the high power of incident beam. By employing the rotating target, the bearable thermal load of neutron target can be significantly enhanced. Therefore, a quantitative evaluation of the thermal dissipation capacity of rotating target is in need. With the finite volume method, we conduct a systematic numerical investigation on the thermal performance of the rotating target. Particularly, we study the effects of the number of target piece, the rotation speed, the flow rate of coolant, and the incident beam power with different beam spot distributions on the temperature of the neutron-producing layer of the target. These results allow us to quantify the thermal performance of rotating target while meeting stringent engineering criteria. Our results provide a robust foundation for the application of rotating targets in CANS.

Understanding the thermal behaviour of radioisotope generators under Martian conditions is essential for the safe and efficient operation of planetary exploration rovers. This study investigates the heat transfer and flow mechanisms around a simplified full-scale model of the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) by means of Computational Fluid Dynamics (CFD) simulations performed with ANSYS Fluent 2023 R1. The model consists of a central cylindrical core and eight radial fins, operating under pure CO2 at a pressure of approximately 600 Pa, representative of the Martian atmosphere. Four cases were simulated, varying both the reactor surface temperature (373–453 K) and the ambient temperature (248 to 173 K) to reproduce typical diurnal and seasonal scenarios on Mars. The results show the formation of a buoyancy-driven plume rising above the generator, with peak velocities between 1 and 3.5 m/s depending on the thermal load. Temperature fields reveal that the fins generate multiple localized hot spots that merge into a single vertical plume at higher elevations. The calculated dimensionless numbers (Grashof ≈ 105, Rayleigh ≈ 105, Reynolds ≈ 102, Prandtl ≈ 0.7, Nusselt ≈ 4) satisfy the expected range for natural convection in low-density CO2 atmospheres, confirming the laminar regime. These results contribute to a better understanding of heat dissipation processes in Martian environments and may guide future design improvements of thermoelectric generators and passive thermal management systems for space missions.

While community energy initiatives and pilot projects have demonstrated technical feasibility and economic benefits, their site-specific nature limits transferability to systematic, scalable investment models. This study addresses this gap by proposing a modular framework for Renewable Energy Valleys (REVs), developed from real-world Community Energy Lab (CEL) demonstrations in Crete, Greece, which is an island with pronounced seasonal demand fluctuation, strong renewable potential, and ongoing hydrogen valley initiatives. Four modular business schemes are defined, each representing different sectoral contexts by combining a baseline of 50 residential units with one representative large consumer (hotel, rural households with thermal loads, municipal swimming pool, or hydrogen bus). For each scheme, a mixed-integer linear programming model is applied to optimally size and operate integrated solar PV, wind, battery (BAT) energy storage, and hydrogen systems across three renewable energy penetration (REP) targets: 90%, 95%, and 99.9%. The framework incorporates stochastic demand modeling, sector coupling, and hierarchical dispatch schemes. Results highlight optimal technology configurations that minimize dependency on external sources and curtailment while enhancing reliability and sustainability under Mediterranean conditions. Results demonstrate significant variation in optimal configurations across sectors and targets, with PV capacity ranging from 217 kW to 2840 kW, battery storage from 624 kWh to 2822 kWh, and hydrogen systems scaling from 65.2 kg to 192 kg storage capacity. The modular design of the framework enables replication beyond the specific context of Crete, supporting the scalable development of Renewable Energy Valleys that can adapt to diverse sectoral mixes and regional conditions.

Accurate cooling load forecasting in chiller units is critical for building energy optimization, yet remains challenging due to non-stationary nonlinear dynamics driven by coupled external weather variability (solar radiation, ambient temperature) and internal thermal loads. Conventional models fail to capture the spatiotemporal coupling inherent in load time series, violating their stationarity assumptions. To address this, this research proposes OptiNet, a spatiotemporal forecasting framework integrating patch-specific dynamic filtering with graph neural networks. OptiNet partitions multi-sensor data into non-overlapping time patches to develop a dynamic spatiotemporal graph. A learnable routing mechanism then performs adaptive dependency filtering to capture time-varying temporal–spatial correlations, followed by graph convolution for load prediction. Validated on long-term industrial logs (52,075 multi-sensor samples at 20 min; district cooling plant in Zhangjiang, Shanghai, with multiple chillers, towers, pumps, building meters, and a weather station), OptiNet achieves consistently lower MAE and MSE than Graph WaveNet across 6–144-step horizons and sampling frequencies of 20–60 min; among 30 set-tings it leads in 26, with MSE reductions up to 27.8% (60 min, 72-step) and typical long-horizon (72–144 steps) gains of ≈2–18% MSE and ≈1–15% MAE. Crucially, the model provides interpretable spatial-temporal dependencies (e.g., “Zone B solar radiation influences Unit 2 load with 4-h lag”), enabling data-driven chiller sequencing strategies that reduce electricity consumption by 12.7% in real-world deployments—directly advancing energy-efficient building operations.