Prediction Of Temperature Field Research Articles

Simulating induction heating of fabric based thermoplastic composites using measured electrical conductivities

A novel method for defining the measured electrical conductivity of CFRTP in induction heating simulations is presented. This method considers the electrical conductivity’s orientation dependence and accurately predicts electromagnetic and transient temperature fields. The study demonstrates that temperature predictions using the present electrical conductivity model are in good agreement with the experiments, and that it yields improved transient temperature field predictions compared to the other electrical conductivity models.

Conjugate Heat Transfer Advancements and Applications in Aerospace Engine Technology

Over the past few decades, conjugate heat transfer (CHT) technology has been instrumental in predicting temperature fields within aerospace engines, guiding engine design with its predictive capabilities. This paper comprehensively surveys the foundational technologies of CHT and their applications in engine design, backed by an extensive literature review. A novel coupling iteration methodology, su-F-TFTB, was proposed. Following this, it introduced grid splicing technology tailored for heat flux conservation, which significantly enhances the adaptability of CHT grids. Ultimately, this study employed the self-developed Aerospace Engine Numerical Simulation (AENS v4.0.1) software to perform CHT analyses on NASA-C3X turbine blades equipped with ten radial cooling systems. A comparative analysis of pressure distributions across various density meshes was undertaken to affirm mesh independence. Furthermore, the impacts of the Spalart–Allmaras (SA) one-equation model and k–ω Shear Stress Transport (SST) two-equation model on the temperature distribution in conjugate heat transfer were investigated. The results indicated that the k–ω SST model exhibited superior performance, aligning closely with NASA experimental data. This validation confirmed the effectiveness of the software.

Graph convolution network-based surrogate model for natural convection in annuli

This work develops a model for natural convection in annuli with internal heat sources based on Graph Convolution Network (GCN), achieving rapid prediction by directly establishing the mapping relationship between the geometric features and the temperature field. The GCN learns node features and connections between nodes in data structures. Compared with traditional machine learning networks, GCN exhibits greater advantages in handling complex geometric shapes and unstructured nodes. In this study, numerous data samples were constructed with various geometric features, e.g., varying sizes, positions, and quantities of internal heat sources. The results indicate that a fully trained GCN was capable to adaptively predict temperature field distributions with arbitrary heat source sizes, positions, and quantities. The mean prediction errors of GCN for the temperature field are around 0.17 %, 0.32 %, and 0.85 %, for the single, double, and triple heat sources cases, respectively. Furthermore, the GCN prediction performance was also compared with surrogate models by the convolutional, and fully connected neural networks (CNN and FNN), which shows an improvement of 88.1 % and 84.8 %, respectively. Therefore, compared with numerical results and other surrogate models, the proposed model presents superior geometric adaptability, high prediction accuracy and a remarkable enhancement in computational efficiency (three orders of magnitude).

Optimization Analysis of a Deep Learning-Based Model for Predicting Temperature Fields in the Solidification Process of Castings

Optimization Analysis of a Deep Learning-Based Model for Predicting Temperature Fields in the Solidification Process of Castings

Prediction and optimization of global temperature field of composite materials under multiple heat sources

The property measurement and structure optimization of composite materials are difficult topics due to the diversity of combinations of composite constituents and complexity of their application environments. The distribution of composite constituents and heat sources can cause heat aggregation phenomenon which may lead to failure of materials. In this paper, we first propose a deep learning-based surrogate model (DLBSM) which can quickly and accurately achieve the end-to-end prediction between the layout of composite materials under multiple heat sources and its temperature field. The prediction result depicts that the coefficient of determination for the maximum and average temperature of all cases exceeds 0.996. Then, the layout optimization is transformed into a combinatorial optimization problem, and the DLBSM is combined with optimization algorithm to optimize the maximum temperature, temperature gradient, and uniformity of the temperature field. The optimized maximum temperature and temperature gradient are significantly reduced, while the temperature uniformity is improved. These enhancements effectively reduce the probability of failure in composites. This approach can significantly improve the efficiency of thermal behavior prediction of composite and its layout optimization compared with finite element method (FEM).

Numerical Study of the Cold Metal Transfer (CMT) Welding of Thin Austenitic Steel Plates with an Equivalent Heat Source Approach

The CMT (cold metal transfer) arc welding process is a valuable joining method for assembling thin sheets, minimizing heat transfers, and reducing subsequent deformations. The study aims to simulate the CMT welding of thin stainless-steel sheets to predict temperature fields and deformations. Both instrumented tests and numerical simulations were conducted for butt-welding of sheets with a thickness of 1 to 1.2 mm. Weld seam samples were observed to identify equivalent heat sources for each configuration. The electric current and voltage were monitored. Temperature measurements were performed using K-type thermocouples, as well as displacement measurements via the DIC (digital image correlation) technique. Thermomechanical simulations, considering phase changes and the actual seam geometry induced by filler material, were conducted using an equivalent heat source approach. A unique heat exchange coefficient accounting for thermal losses was identified. By incorporating these losses into thermal calculations, a good agreement was found between measured and calculated temperatures. Mechanical calculations allowed for the recovery of the horse saddle form after actual welding, with a relative difference of less than 10% in angular distortion between calculated and measured values.

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 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.

Hybrid data-mechanism-driven model of the unsteady soil temperature field for long-buried crude oil pipelines with non-isothermal batch transportation

The thermal simulation of oil pipeline transportation is crucial for ensuring safe transportation of pipelines and optimizing energy consumption. The prediction of the soil temperature field is the key to the thermal calculation for the non-isothermal batch transportation of the buried pipeline, while the standard numerical simulation of the soil temperature field is time-consuming. Coupling with a data-driven Bayesian neural network and mechanism-informed partial differential equation, an efficient and robust prediction model of soil temperature field is proposed to dynamically adapt the spatio-temporal changes of boundary conditions. Based on the soil temperature field predicted by the proposed model, the oil temperature at the outlet of the pipeline is further obtained, which is compared with that from the field data and the standard numerical simulation. It is found that the former is in good agreement with the latter two, verifying the proposed model. However, the calculation of the proposed model only takes 10.59 s, which is 29.53 times faster than the standard numerical simulation. Moreover, the predicted error of the proposed model only changes by 0.12 % (from 3.05 % to 3.17 %) when the training data decreases from 100 % to 2.2 %, which is lower than that of two data-driven surrogate models.

Analysis and experimental research of transient temperature rise characteristics of high-speed cylindrical roller bearing

In this paper, on one hand, the time-varying characteristics of the heat source and thermal boundary conditions of the high-speed spindle system were analyzed considering the thermal-structural coupling effect. And a transient bearing temperature field prediction method combining the thermal network method and finite element method was proposed. Furthermore, the relationships between time step, calculation efficiency and calculation results were analyzed. On the other hand, a online real-time monitoring system of the transient temperature of the cylindrical roller bearing inner ring for maximum speed of 13,000 r/min was designed and implemented using fibre optic sensing technology. Comparing with the conventional static thermal analysis results, it is verified that the simulation method proposed in this paper has higher accuracy. This paper provides a new approach for analysing and testing the thermal characteristics of high-speed spindle system.

Rapid Prediction of Magnetic and Temperature Field Based on Hybrid Subdomain Method and Finite Difference Method for the Interior Permanent Magnet Synchronous Motor

Rapid Prediction of Magnetic and Temperature Field Based on Hybrid Subdomain Method and Finite Difference Method for the Interior Permanent Magnet Synchronous Motor

Mathematical models for determining and analyzing thermal regimes in mining industry mechanism structures

Purpose. To develop linear and nonlinear mathematical models of heat conduction for isotropic heterogeneous media with internal heating. This will allow for an increased accuracy in determining temperature fields, which will subsequently impact the effectiveness of designing mechanisms, devices, and individual components of structures that have a layered structure and are subjected to heat stress. Methodology. For the development of linear and nonlinear mathematical models of the temperature field and the analysis of temperature regimes in layered media with internal thermal heating, the coefficient of thermal conductivity is described as a whole using asymmetric unit functions. This makes it possible to solve a differential equation with singular coefficients in both linear and nonlinear boundary value problems of heat conduction with appropriate boundary conditions. Findings. Quadratic equations are obtained to determine the analytical solutions of linear and nonlinear boundary problems of heat conduction for a layered plate with internal heat load. Originality. The scientific novelty lies in the given method of linearization of the nonlinear mathematical model of heat conduction and obtaining analytical solutions, in a closed form, of the corresponding linear and nonlinear boundary value problems for isotropic layered media subjected to internal heating. Practical value. The developed linear and nonlinear mathematical models for determining the temperature distribution in layered structures with internal heating make it possible to analyze heat exchange processes and ensure the thermal stability of such structures. This also makes it possible to increase the heat resistance of structures and protect them from overheating, which can lead to damage to individual components and elements of mechanisms, as well as to the entire structure as a whole. The resulting analytical solutions can be used to predict temperature fields in mine shafts, underground environments and mechanisms of mining equipment, in particular, in drilling and underground compressor stations, ventilation systems and other equipment, which improves work efficiency and reduces useful energy consumption.

Digital twin temperature field prediction of laser powder bed fusion through proper orthogonal decomposition with radial basis function

Digital Twin framework, integrated with in-situ sensing and physical simulation, can significantly improve process productivity and product quality of Laser Powder Bed Fusion (LPBF). Online simulation is the key step for implement of Digital Twin of LPBF. For constructing an online simulation model of LPBF, a swift and accurate Reducing Order Modeling, Proper Orthogonal Decomposition with Radial Basis Function (POD-RBF) is designed and applied to the online simulation of single-track and single-layer temperature field of LPBF. The results show that the relative error of temperature between the POD-RBF and the FEM is about 2%, the training time is less than 8 min. These results mean the lower relative error, the less training time, when compared with the typical non-intrusive model reduction method, Proper Orthogonal Decomposition with Gaussian Process Regression (POD-GPR). The proposed POD-RBF method can accurately realize the rapid prediction of the powder temperature field under the designed process parameters, which meets the requirements of online simulation of Digital Twin model of LPBF and makes it possible to provide opportune feedback for correcting incipient anomalies.

Real-time temperature field reconstruction using a few measurement points and RPIM-AGQ6 interpolation

In thermal management systems, temperature field monitoring is vital for the structural safety of electronic equipment. However, there are practical challenges when it comes to installing temperature sensors on some monitoring points directly. To solve this problem, a novel temperature field reconstruction method is proposed. This strategy offers the advantage of using only a limited number of discrete values to predict temperature fields across an entire plane. Initially, the approach constructs a composite interpolation function, named RPIM-AGQ6, by coupling the mesh-free radial point interpolation method (RPIM) with the quadrilateral area coordinate method (AGQ6) The temperature field is discretized and represented as the product of the nodal temperatures and the RPIM-AGQ6. Next, ill-posed equations are established and solved using Bayesian Multivariate Linear Regression (BMLR) to obtain the temperature in the key points. Subsequently, a novel optimization strategy is proposed to achieve the cooperative optimization of the number of sensors and reconstruction accuracy. Finally, the simulated calculation and experimental tests are used to demonstrate the effectiveness and accuracy of the proposed approach. The results show that the proposed methodology can accurately reconstruct the plane temperature field of plate structure with one and two hot sources and the relative percentage error is within 2.6 %. Therefore, it can be utilized as an effective tool to real-time monitor structural surface temperature.

Step response model and real-time prediction of temperature fields in laser irradiated biological tissues

Step response model and real-time prediction of temperature fields in laser irradiated biological tissues

Research on temperature field prediction method in an aero-engine combustor with high generalization ability

Using the inlet flow parameters to get the temperature field of the aero-engine combustor can help researchers quickly learn about the combustion state of the combustor, which is essential to aero-engine combustor design and optimization. This study puts forward a fast-predicting scheme on the temperature distribution of aero-engine combustor by deep learning method. Different networks are trained to gain multiple predicting models, and the prediction performance of temperature field models under different dataset processing methods and different network structures are compared. The results show that both temperature field prediction models constructed by fully-connected networks and fusion convolutional networks have good predictive capabilities. However, when the equivalent ratio conditions deviate significantly from the training dataset, the model performance deteriorates seriously. By introducing reference data to process the dataset, the models’ prediction ability for equivalent ratio conditions far from the training dataset is significantly improved. Further research shows that this data processing method has ability to extrapolate to some extent.

Data-driven estimation of scalar quantities from planar velocity measurements by deep learning applied to temperature in thermal convection

The measurement of the transport of scalar quantities within flows is oftentimes laborious, difficult or even unfeasible. On the other hand, velocity measurement techniques are very advanced and give high-resolution, high-fidelity experimental data. Hence, we explore the capabilities of a deep learning model to predict the scalar quantity, in our case temperature, from measured velocity data. Our method is purely data-driven and based on the u-net architecture and, therefore, well-suited for planar experimental data. We demonstrate the applicability of the u-net on experimental temperature and velocity data, measured in large aspect ratio Rayleigh–Bénard convection at Pr=7.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{Pr} =7.1$$\\end{document} and Ra=2×105,4×105,7×105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{Ra} =2\ imes 10^5,4\ imes 10^5,7\ imes 10^5$$\\end{document}. We conduct a hyper-parameter optimization and ablation study to ensure appropriate training convergence and test different architectural variations for the u-net. We test two application scenarios that are of interest to experimentalists. One, in which the u-net is trained with data of the same experimental run and one in which the u-net is trained on data of different Ra\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{Ra}$$\\end{document}. Our analysis shows that the u-net can predict temperature fields similar to the measurement data and preserves typical spatial structure sizes. Moreover, the analysis of the heat transfer associated with the temperature showed good agreement when the u-net is trained with data of the same experimental run. The relative difference between measured and reconstructed local heat transfer of the system characterized by the Nusselt number Nu\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{Nu}$$\\end{document} is between 0.3 and 14.1% depending on Ra\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{Ra}$$\\end{document}. We conclude that deep learning has the potential to supplement measurements and can partially alleviate the expense of additional measurement of the scalar quantity.

Numerical simulation and optimization of In-Vessel primary heat exchangers of NHR200-II

NHR200-II is a multipurpose small integral pressurized water reactor. The primary heat exchangers of NHR200-II are in-vessel single-phase heat exchangers. The primary heat exchangers adopted a tube-in-tube bundle design to improve compactness; however, a significant weakness of the tube-in-tube bundle is the nonuniform thermal expansion between the inner and outer tubes owing to the inhomogeneous tube-temperature distribution. To minimize the thermal stress, the inner and outer tube diameters were optimized using numerical simulations. The nonuniform mass flow rates and temperature fields in each channel and tube were calculated using the optimal tube sizes, and the axially averaged temperatures of the inner and outer tubes were also calculated. The present prediction of nonuniform temperature fields can be used as the input to estimate the magnitude of thermal stress in the assessment of the structural integrity of the primary heat exchangers of NHR200-II.

Reconstruction and fast prediction of 3D heat and mass transfer based on a variational autoencoder

Reconstruction and fast prediction of heat and mass transfer are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN) and variational autoencoder (VAE) composite model is proposed for the reconstruction and prediction of 3D flowfields with high accuracy and efficiency. The VAE model is trained to extract features of the problem and to realize 3D physical field reconstruction. The ANN is employed to achieve the constructability of the extracted features. A dataset of steady temperature/velocity fields is acquired by computational fluid dynamics and heat transfer (CFD/HT) and fed to train the deep learning model. The proposed ANN-VAE model is experimentally proven to achieve promising field prediction accuracy with a significantly reduced computational cost. CFD/HT method is often incapable of occasions that require instant knowledge of heat and mass transfer. The proposed ANN-VAE method convert the time-consuming simulations into the training stage. Driven by large amount of simulation data, the trained ANN-VAE model is capable of field prediction. Compared to the CFD/HT method, the ANN-VAE method speeds up the physical field prediction by approximately 380,000 times, with mean accuracies of 97.3% for temperature field prediction and 97.9% for velocity field prediction, making it feasible for real-time prediction of heat and mass transfer.

Study on Simulation of Mold Filling and Solidification Characteristics of Hypereutectic High-Chromium Cast Iron by Lost Foam Suspension Casting

In this paper, the finite element software ProCAST version 14.5 was used to simulate the temperature field, flow field and defect prediction in the filling and solidification process of hypereutectic high-chromium cast iron. The effects of pouring temperature, negative pressure and the amount of suspension agent added during the technological process were explored. The optimum process parameters were presented. It was found that the suspension agent has a certain hindrance to the filling process, but the filling process remains stable. In the solidification stage, 89.4% of the suspension agent melted, resulting in a relative supercooling degree of 50 °C, which had a certain chilling effect and improved the solidification rate.

Tempnet: A graph convolutional network for temperature field prediction of fire-damaged concrete

Determining the damage level of the fire-damaged concrete structure is critical for the structural assessment and repair of buildings after fire. Existing methods assess the damage levels of concrete by measuring the remaining mechanical performance in a traditional manner, where they either have limited accuracy or efficiency due to the need of heavy machines and experienced laborers. In contrast with these methods, we propose a deep learning based approach called Tempnet to promote the efficiency and effectiveness of damage level assessment for concrete after fire. Tempnet incorporates a graph convolutional layer and a conventional convolutional layer to encode the temperature interdependency between neighboring areas in the images of fire-affected concrete to capture the exposed temperature fields of the concrete. Three closely related application scenarios together with their corresponding datasets have been proposed to evaluate the performance of Tempnet. Extensive comparative experiments and ablation studies have validated the model design, the high efficiency, and the robust performance of Tempnet, with a performance metric F1 value higher than 0.97 in all applications. Case studies were conducted further to provide insightful illustration of Tempnet’s impressive performance. It is envisioned that the Tempnet can contribute to the efficient maintenance of concrete structures following fire accidents for construction applications.