Prediction Of Temperature Distribution Research Articles

A modified spatiotemporal nonlocal thermoelasticity theory with higher-order phase delays for a viscoelastic micropolar medium exposed to short-pulse laser excitation

At the microscale and nanoscale, materials exhibit size-dependent behaviors that classical models cannot capture. This analysis introduces a size-dependent higher-order thermoelastic heat conduction model, incorporating spatial and temporal nonlocal effects in a micropolar visco-thermoelastic medium subjected to laser pulse heat flux. The two-phase delay model, featuring higher-order temporal derivatives, captures the complex interactions among mechanical, thermal, and viscous properties in materials where size effects are significant. By including phase lag, the model effectively addresses non-Fourier heat conduction in short-duration laser pulse scenarios. It accurately predicts temperature distribution, stress response, and microrotation effects in microscale and nanoscale materials. The study visually represents how factors such as micropolarity, higher-order effects, phase delay, nonlocal index, and viscosity influence the size-dependent mechanical behavior of the half-space structure. The numerical results highlight the importance of size-dependent phenomena in nanostructures, revealing deviations from classical predictions due to nonlocal interactions. Overall, the proposed spatiotemporal nonlocal homogenization model serves as a valuable tool for analyzing the complex mechanical and thermal characteristics of nanomaterials.

Analysis of Bioheat Transfer with Thermoelastic Effect

The development of thermal therapy always requires more reasonable temperature distribution predictions. Controlling the amount of heating is a common practice within general thermal therapy operations. This paper used a modified three-phase lag (TPL) bioheat transfer equation to describe the behavior of heat conduction in tissue with thermoelastic effect. To explore the effect of thermal load, the tissue was subjected to a constant surface temperature and a pulsed surface heat flux, respectively. The modified TPL bioheat transfer equation involves mixed derivative terms and higher-order time derivatives of temperature. In analyzing such problems, there are mathematical difficulties. Therefore, the hybrid numerical scheme based on the Laplace transform and an improved discrete method was proposed to solve the present problem. The influence of thermoelastic parameters on the behavior of heat transfer in tissue has been investigated. The results depict the effect of thermal load on thermal response within heat transfer medium is obvious. The thermoelastic effect excites the thermal response oscillation, which is intensified for the reduction of material constant characteristic [Formula: see text] and phase lag [Formula: see text], in the heat conduction medium.

Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks

To address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. Design variables of the hot-air anti-icing cavity are used as inputs of the two models, and the corresponding surface temperature distribution data serve as outputs, and then the performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. The proposed models not only provide faster predictions of anti-icing surface temperature distributions than traditional numerical simulation methods but also ensure acceptable accuracy, which supports the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.

A hybrid mechanism-based and data-driven model for efficient indoor temperature distribution prediction with transfer learning

Efficient prediction of indoor temperature distribution (ITD) is crucial for various building-related applications, particularly in real-time thermal management. Despite efforts from both physics-based and data-driven perspectives, ITD prediction still faces challenges, particularly in balancing prediction accuracy with computational speed and addressing the need for extensive high-quality data in practical applications. To address these challenges, this study develops a hybrid temperature distribution prediction model (HTDPM) that integrates deep learning with physical mechanisms, enabling the effective capture of spatial-temporal dependencies and uncertainties with limited input parameters, achieving a balance between prediction accuracy and computational efficiency. Meanwhile, a novel transfer learning approach is applied to HTDPM (HTDPM-TL), significantly reducing data requirements and enhancing model generalization across various practical scenarios. Besides, orthogonal and randomized experiments are designed to simulate multiple real-world scenarios, thus constructing an extensive source domain dataset. The HTDPM-TL was tested in various real-world scenarios with several baseline methods, demonstrating an average RMSE of 0.794∘C, a computational time of 0.289 s, and limited data volume. The computational speed was improved by three orders of magnitude compared to CFD simulations, and the prediction resolution was enhanced threefold compared to traditional data-driven models. These results highlight the potential of HTDPM-TL to achieve an optimal trade-off between prediction accuracy, computational efficiency, and data requirement.

Study of thermal and humidity environment and prediction model in impinging jet ventilation rooms based on thermal and moisture coupling

Variations of indoor humidity have significant influence on thermal comfort, calculation accuracy of air-conditioning load and selection of design parameters. As one of the most promising advanced ventilation strategies, impinging jet ventilation (IJV) has received much attention. However, few study concerned its coupled indoor thermal and humidity environment and there is also no complete mathematical model for the IJV to synchronously predict its indoor temperature and humidity distributions. Therefore, this study first simulated the thermal and humidity environment in IJV rooms with considering the coupled thermal and moisture transfer effect. The results demonstrate a significant correlation between temperature and humidity distributions in IJV, with a correlation coefficient of approximately 0.4. Then, a simplified theoretical model for predicting temperature and humidity distributions in the IJV was established based on the theory of nodal model, and the key parameters within the model that are influenced by indoor airflow pattern were also identified. Next, the model results were compared with the simulation ones and showed that the proposed model is with a mean absolute error of 0.4 °C for the prediction of temperature distribution and of 0.21 g/kg for the prediction of humidity distribution. These discrepancies are sufficiently minor to validate the accuracy of the proposed model. Last, the potential application value of the proposed model was discussed. Overall, the current study contributes to extend the application of the existing coupled thermal and humidity models to IJV scenarios, and provides a robust theoretical foundation for the more rational design and practical application of the IJV.

Numerical and experimental study of thermal stabilization system for satellite electronics with integrated phase-change capacitor

In this paper experimental and numerical investigations were conducted to analyze the application of phase change material (PCM) for temperature stabilization of small satellite electronics. The reference electronic equipment is a data processing unit generating a nominal power of 9.6 W during its operation. To stabilize its temperature, an integrated PCM module was designed, built, and tested experimentally in a thermal–vacuum chamber. The experimental results were then used to validate a numerical model, developed using commercial software Simcenter FloEFD. The results showed good agreement between measured and predicted temperature evolution in critical locations of the unit. The model predictions were further improved by conducting analysis of uncertain input data, model settings, and by inclusion of the effect of natural convection in the PCM. The improved model showed better agreement between the model predictions and experiments. The comparison showed that the maximum difference between the measured and calculated temperature at the most important location (the heater) was 1.6 °C and difference of the peak values was 0.8 °C. Finally, the model was used to compare the constructed module containing the PCM with a system consisting of an aluminum block of the same volume as PCM. The comparison showed that the use of PCM allows for a reduction of the maximum temperature by 6.2 °C while reducing the mass almost 3.5 times. The results demonstrate that the developed numerical model allows for accurate predictions of temperature distributions in the designed device, and can be effectively used to design PCM modules dedicated to in-space electronics, thereby increasing their peak capabilities and lifetime.

Improving turbulence modeling for gas turbine blades: A novel approach to address flow transition and stagnation point anomalies

Accurate prediction of temperature and Heat Transfer Coefficient (HTC) distributions over gas turbine blades is crucial for the design process and life assessment of these components. Numerical studies of flow over gas turbine blades face significant challenges in accurately simulating two complex phenomena: (1) the transition of flow from laminar to turbulent, and (2) stagnation point flow at the leading edge. Many turbulence models tend to overpredict the temperature on turbine blades, leading to incorrect identification of hot-spot regions and, consequently, erroneous estimations of blade life. This paper investigates the performance of various turbulence models in simulating flow and heat transfer over gas turbine vanes. The study includes three full turbulence models, i.e., Spalart-Allmaras (SA), Shear Stress Transport k−ω (SST-kw), and v2−f (V2F), as well as two transitional models, i.e., Transition SST (Trans-SST) and k−kL−ω (k-kl-w). Simulation results indicate that the v2−f, Trans-SST, and k−kL−ω models can detect flow transition. However, the transition length and onset location predicted by the Trans-SST and k−kL−ω models do not align with experimental data. Conversely, the v2−f model suffers from over-predictions at the leading edge due to stagnation point anomaly. To address these issues and due to capacities of the V2F model, this study proposes two modifications to enhance the performance of the V2F model. First, the production term of turbulent kinetic energy is redefined to mitigate the stagnation point anomaly. Second, the model is recalibrated to improve the prediction of flow transition. The new model, named the Production Modified V2F (PMV2F) model, shows promising results in predicting temperature and heat transfer coefficients.

The thermal impact of the self-heating effect on airless bodies. The case of Mercury’s north polar craters

Thermal models are essential for studying airless planetary surfaces, as the interaction between topography and thermophysical properties plays a crucial role in determining a surface’s response to localized illumination. Accurate temperature distribution calculations require a comprehensive investigation of sunlight scattering, a process that, despite its computational challenges, cannot be overlooked, especially when high resolution is necessary. Furthermore, thermal analysis is fundamental for assessing the stability of volatiles in polar regions. In this study, we introduce a novel approach by discretizing the Sun into 100 individual elements, allowing for a highly precise simulation of solar flux—an innovation crucial for accurately capturing temperature distributions in Mercury’s polar craters, given the planet’s proximity to the Sun. This level of discretization significantly enhances the accuracy of the thermal model, ensuring a more realistic depiction of how sunlight interacts with crater topography. We developed a dual-model approach that simulates both direct solar illumination and its scattering on two craters, Laxness and Fuller, located at Mercury’s north pole. The illumination and thermal model predict temperature distribution and heat transfer based on the material’s thermal properties and topography. The study examines the interaction between direct sunlight, causing localized heating, and scattered light, which influences the thermal response of surface materials. Detailed illumination maps and temperature profiles were generated over two Hermean years, revealing the significant impact of the self-heating effect on temperature distribution. The results show that specific regions experience indirect solar flux due to the craters’ morphology, particularly in permanently shadowed regions (PSRs) that are heated exclusively by scattered radiation. Maximum temperature profiles for the Laxness and Fuller craters show a substantial temperature increase within PSRs compared to areas exposed to direct illumination. However, while self-heating does not affect the stability of water ice in the Laxness crater, in the Fuller crater, a section within the radar-bright material reaches temperatures of up to 210 K, potentially threatening the stability of water ice. Further investigation with the onboard SIMBIO-SYS instrument on the BepiColombo mission will help to better understand the current state of these craters and their volatile deposits.

Measurement and prediction of vertical temperature distribution above urban building roofs at an ultra-microenvironment scale

Microenvironment is critical for building thermal performance. Heat transfer through building envelope (such as roof and window) and mass transfer through building mechanical systems that are adjacent to building surfaces (such as roof-top unit) are influenced directly by the air conditions near the surfaces (e.g., within 1.5 m). These air environments are within the sublayer of the air thermal boundary layer around the buildings – here defined as ultra-microenvironments, to be distinct from the conventional microenvironment scale of 2–5 m from building surfaces. This study particularly investigates the vertical temperature profiles within the ultra-microenvironments above urban building roofs. Field tests were conducted on two roof tops in Wuhan, China and Boulder, US, respectively. Consistent vertical air temperature profiles above the roofs were observed for both days and nights. The study further identifies one recent above-roof air temperature model and validates it with the field test data, which can effectively predict the vertical air temperature at various heights within the ultra-microenvironment above the roof. Applications of this model using different local and climate data including weather station data are analyzed and discussed, along with their performance on prediction accuracy.

Toroidal mean flow and complex temperature distribution in a thermoacoustic core

Increasing the capacity of thermoacoustic machines (engines or refrigerators) requires increasing the diameter of the regenerator, which may result in non-uniform temperature distributions inside the regenerator. In the current study, the temperature is measured at different radial and axial locations inside the regenerator of a thermoacoustic refrigerator with a diameter of 148 mm. The working gas is a mixture of helium and Argon at a static pressure of 40 bar. Non-uniform radial temperature distributions are observed at different axial positions. Also, the predicted axial temperature distributions by a 1-D linear model (DeltaEC) do not agree with the measured temperature distributions. We suspect that the non-uniform radial temperature distribution generates a steady flow through the regenerator which increases the radial temperature non-uniformity and results in non-linear axial temperature distribution. Given the measured temperature distribution, the presence of a toroidal steady flow inside the regenerator is suspected. To confirm this hypothesis, the regenerator is divided into three segments connected in parallel in the DeltaEC model. Mean flows are added in each segment of the regenerator to simulate the presence of toroidal flow in the regenerator. The predicted temperature distributions from this modified DeltaEC model agree with the measured temperature distributions.

Numerical simulation of co-firing LRC and ammonia in Pangkalan Susu 3 & 4 coal-fired steam power plant (CFSPP) capacity 210 megawatts

The effort to reduce CO2 and NOx emissions plays a crucial role in mitigating climate change, improving air quality, complying with environmental regulations, and promoting clean technology innovation. Ammonia, as an emission-free fuel, shows significant potential as a co-firing agent with Coal in coal-fired steam power plants (CFSPP). Previous studies have demonstrated promising results in emission reduction through ammonia co-firing. This research presents a numerical analysis based on Computational Fluid Dynamics (CFD) to investigate the co-firing of ammonia with low-calorific Coal (LRC) in the CFSPP Pangkalan Susu Units 3 and 4, with a capacity of 210 MW. The study employs fluid flow modelling and chemical reaction analysis using the Discrete Phase Model (DPM) to provide accurate predictions of temperature distribution and pollutant concentrations in pulverized coal boilers. Cofiring simulations were conducted with ammonia additions of 5 % and 15 % on a calorific basis. Injection experiments from each burner (A-D) were performed to identify the optimal injection location. The simulation results indicate changes in combustion characteristics, particularly in temperature distribution. The main finding reveals a temperature decrease when ammonia is added as a co-firing material, attributed to the higher H2O content, which leads to increased moisture losses. In terms of efficiency, co-firing showed a decline compared to the baseline combustion of 100 % LRC coal due to the more significant moisture losses. The highest reduction in CO2 emissions was observed when 15 % ammonia was injected from burner B in case #6, with a mass fraction value of 0.171 at the boiler outlet. Similarly, the most significant reduction in NOx emissions occurred with a 15 % ammonia co-firing from burner B, yielding a mass fraction value of 8.81E-04 at the boiler outlet. This co-firing technology is expected to enhance decarbonization efforts and optimize the use of renewable energy in the future.

Melt flow analysis in rotational nozzle fused filament fabrication process

Fused filament fabrication (FFF) is a widely used processing method; however, heat transfer limitations within a conventional nozzle result in relatively low flow rates, leading to lengthy production times, compared to traditional processing methods, ultimately restricting its industrial application. Recently, a novel rotational nozzle FFF three-dimensional (3 D) printer has been patented and developed to enhance processing efficiency. Despite this achievement, the fundamental mechanisms behind this novel process remain unclear. In this study, both analytical analysis and numerical simulations were conducted based on a force-controlled scaled-down experimental setup. This setup, designed according to the pressure-induced melt removal theory, provided melt throughput data under varying heater temperatures, extrusion forces, and rotational speeds. Agreement between the modeling and experimental results confirms the generalizability of the models. Modeling predictions of temperature and velocity distributions indicate that viscous dissipation affects the average temperature and filament velocity. To simulate the real-world working conditions of FFF 3 D printing, a velocity-controlled simulation was introduced. It was observed that the average melt film thickness increases with nozzle rotational speed due to viscous dissipation. Additionally, the extrusion force required for the same printing speed decreases with increasing nozzle rotational speed, primarily due to the higher shear rate reducing melt viscosity.

Experimental investigation and numerical modeling of an innovative horizontal coaxial ground heat exchanger (HCGHE) for geothermal heat pump applications

Geothermal is an ideal renewable energy source for building heating and cooling via a ground source heat pump (GSHP) system. However, the high initial cost of installing the ground heat exchanger limits its adoption, especially among owners of residential and smaller non-residential buildings. This study aims to develop a new methodology for installing an innovative horizontal coaxial ground heat exchanger (HCGHE) and to investigate its thermal performance in GSHP applications through field testing and numerical simulation. Horizontal directional drilling and a new manifold design were used to install the new HCGHE, significantly reducing costs by minimizing time, materials, labor, and landscape disruption. The transient numerical model, developed in Ansys Fluent using the finite volume method, was validated against experimental data and employed to predict temperature distributions in the supply pipe, return pipe, and surrounding soil. The study determined the heat transfer rate per unit borehole length and entire thermal resistance of the HCGHE to vary from 23.0 to 35.7 W/m and 0.28 to 0.74 K·m/W in winter, and from 24.3 to 37.4 W/m and 0.12 to 0.52 K·m/W in summer. The short-term soil temperature distributions in both the radial and longitudinal directions of the HCGHE are modeled using second-order polynomial equations, with R2 values ranging from 0.994 to near unity, while the long-term soil temperature distributions follow a power law. This research not only confirmed that the innovative HCGHE design and installation can achieve good thermal performance but also provided valuable insights into continual design improvement. The innovative HCGHE-based GSHP system provides an efficient and sustainable solution for heating and cooling buildings by harnessing the earth’s natural heat, offering the advantages of simplified installation and reduced initial investment.

Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study’s major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.

A novel meshless radial basis reproducing kernel particle approach for 3D thermo-mechanical analysis of mushy zone phase-change problems

This paper presents a three-dimensional thermo-elastoplastic computational model for the analysis of phase change problems involving the mushy zone. The model utilizes a novel meshless radial basis reproducing kernel particle approach, incorporating a high-order kernel that integrates Gaussian and cosine functions. An energy balance equation for the entire domain is formulated using the effective heat capacity method, based on the enthalpy formulation. The governing equations are discretized using the Galerkin weak form to compute thermal residual stresses, with essential boundary conditions enforced through the penalty method. Plastic deformation is characterized using the von Mises criterion with isotropic hardening, and the strain term resulting from temperature-dependent material properties is incorporated into the incremental solution process. Determination of the plastic strain increment is based on the Prandtl–Reuss flow rule. The accuracy and efficiency of the proposed meshless approach were rigorously evaluated through numerical examples encompassing two- and three-dimensional problems. The computational results were compared against available analytical and validated numerical solutions. This comprehensive assessment substantiated the robustness and precision of the developed technique. The case studies presented elucidate the capacity of the novel meshless model to effectively predict temperature distributions and residual stress fields within mushy zone phase change phenomena, accommodating both regular and irregular geometries under various boundary conditions. Notably, the model demonstrated consistent stability and high accuracy across the spectrum of simulated scenarios, thus underscoring its potential as a valuable tool for investigating complex phase change processes.

Deep Neural Network-Based Temperature Mapping Technique for Heat Sink on Electronic Devices Using Local Thermocouple Sensors

ABSTRACT Heat generated by electronic devices can lead to thermal deformation, damage, and fatigue failure, underscoring the importance of monitoring heat distribution. This study introduces an artificial neural network using two thermocouples for cost-effective temperature distribution prediction. Experimental data from heated systems on chip with attached heat sinks were used for training and validation, integrating thermocouple measurements and infrared camera data. The method’s applicability was verified across four different heat sinks. Additionally, finite element analysis compared stress and strain based on predicted and actual temperature distributions, addressing conventional limitations that focus solely on temperature validation. Furthermore, the temperature of any coordinate could be output by including the coordinate as an input of neural networks, eliminating the hassle of re-constructing or learning the DNN to obtain the temperature of the desired coordinate. Results showed that the temperature distribution could be predicted with high accuracy (over 0.95), and the maximum error rate for stress and strain predictions was 7% in the worst case. This confirmed the feasibility of artificial neural networks to predict the temperature distribution using a minimal number of sensors and ensure robust performance even if the heat sink changes.

Detection of hydrogen gas leak using distributed temperature sensor in green hydrogen system

This study addresses the challenges of hydrogen gas detection in pipelines, focusing on the highly flammable nature and low ignition energy of hydrogen. It highlights the limitations of traditional point-based detection methods and emphasizes the need for advanced safety technologies. An integrated approach combining onsite monitoring with Internet of Things (IoT) technology is proposed to enhance systematic safety management and quick leak detection in hydrogen infrastructures. The study reviews various hydrogen detection methods, emphasizing distributed temperature sensing (DTS) techniques to identify leaks through temperature variations. Over nine months of monitoring, the results indicate that DTS temperature variations are more influenced by sensor location than chamber configuration, suggesting that external DTS chamber installation is more effective for leak detection. Additionally, the study integrates Gaussian Process Regression with Machine Learning to predict temperature distribution in pipelines, providing valuable insights for future hydrogen leak detection research.

Cure Kinetic Modelling and Experimental Analysis to Predict Temperature Distribution during Microwave Curing of Carbon Fiber Composites

Cure Kinetic Modelling and Experimental Analysis to Predict Temperature Distribution during Microwave Curing of Carbon Fiber Composites

Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure

Constrained by the substantial computational time required for numerical simulation, a deep learning technique is applied to investigate fluid flow and heat transfer processes in metal foam with a hierarchical pore structure. This work adopted a 3D convolutional neural network (CNN) combining U-Net architecture to predict velocity and temperature distributions, alongside corresponding permeability and overall heat transfer coefficient. This approach demonstrates excellent capability in intricate image segmentation. The training sets were acquired by lattice Boltzmann method (LBM) simulations. The CNN model, trained on a substantial amount of data, demonstrates remarkable precision, exhibiting mean relative errors of 0.57% for permeability prediction and 2.27% for overall heat transfer coefficient prediction. Moreover, in CNN prediction, a broader range of structure parameters and boundary conditions beyond those in the training set was used to evaluate the practicability of the trained CNN model. In contrast to numerical simulation, the CNN model economizes approximately 95.41% and 99.57% of computational time for velocity and temperature distribution prediction, respectively, providing a novel approach for exploring transport processes in metal foam with hierarchical pore structure.

Physics-Informed Fully Convolutional Networks for Forward Prediction of Temperature Field and Inverse Estimation of Thermal Diffusivity

Abstract Physics-informed neural networks (PINNs) are a novel approach to solving partial differential equations (PDEs) through deep learning. They offer a unified manner for solving forward and inverse problems, which is beneficial for various engineering problems, including heat transfer analysis. However, traditional PINNs suffer from low accuracy and efficiency due to the fully-connected neural network framework and the method to incorporate physical laws. In this paper, a novel physics-informed learning architecture, named physics-informed fully convolutional networks (PIFCNs), is developed to simultaneously solve forward and inverse problems in thermal conduction. The use of fully convolutional networks (FCNs) significantly reduces the density of connections. Thus, the computational cost is reduced. With the advantage of the nodal-level match between inputs and outputs in FCNs, the output solution can be used directly to formulate discretized PDEs via a finite difference method, which is more accurate and efficient than the traditional approach in PINNs. The results demonstrate that PIFCNs can flexibly implement Dirichlet and Neumann boundary conditions to predict temperature distribution. Remarkably, PIFCNs can also estimate unknown thermal diffusivity with an accuracy exceeding 99%, even with incomplete boundaries and limited sampling data. The results obtained from PIFCNs outperform those obtained from PINNs.