Prediction Of Temperature Field Research Articles

Predicting Structural Temperature of Ancient City Walls: A Case Study Using Ambient Temperature and Deep Learning

ABSTRACT Accurate prediction of the structural temperature field is required for the performance assessment of engineering structures and for maintaining structural safety. However, there are complex time-lag effects between ambient temperature and structural temperature. It is difficult to accurately describe and predict the structural temperature using traditional linear and nonlinear fitting methods. Hence, this study uses the advantages of gated recurrent unit (GRU) neural networks in dealing with complex nonlinear mapping issues, further considering solar radiation and developing a structural temperature prediction framework using ambient temperature and the deep learning model. The framework is applied in structural temperature prediction for an ancient city wall in China. The solar altitude angle at the location of the city wall is calculated, and the input and output structures of the deep learning neural network model are optimized. In the long-term prediction of the structural temperature of the city wall, the root mean square error (RMSE) of the GRU model is less than 0.6°C, which solves the problem of low accuracy caused by time-lag effects.

Prediction of engine combustion chamber outlet temperature field based on deep Learning: Application in aero-engine life extension control

One of the current research hotspots is the prediction of combustion chamber flow field based on deep learning methods, but how to effectively utilize these prediction results is a key issue that needs to be addressed urgently. This paper further proposes a study on the predictive control method for engine life extension considering the dangerous point temperature of turbine guide vane based on the prediction of combustion chamber outlet temperature field. Firstly, a combustion chamber outlet temperature distribution prediction model is established through Computational Fluid Dynamics (CFD) numerical simulation and inception deconvolution network, and integrated into the variable cycle engine component level model to achieve real-time prediction of combustion chamber outlet temperature distribution. Furthermore, based on this, the dangerous point temperature of the high-pressure turbine (HT) guide vanes is introduced as a constraint into the prediction optimization, and a predictive control method for engine life extension considering the thermal mechanical fatigue life (TMFL) of the guide vanes is proposed. The simulation results show that compared to the traditional LEC control method, the temperature at the critical point of the blade is reduced by 12.28 K, and the TMFL of the blade is increased by 29.23 %. The research results of this article provide a good application example for predicting the temperature distribution at the outlet of the combustion chamber, expanding the application scenarios for predicting the flow field parameters inside the engine.

Physics-Informed Online Learning for Temperature Prediction in Metal AM.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN's use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyperparameters-the learning rate and batch size of the online learning phase-highlights their roles in optimizing the learning process and enhancing the framework's overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

Study on heat dissipation of torsional vibration damper for engine crankshaft

Abstract High temperatures in torsional vibration dampers can cause silicone oil to fail, increasing crankshaft vibration and shortening component fatigue life. Accurate prediction of the damper temperature field is key to the manufacture of highly reliable torsional vibration dampers. This study establishes the relationship between temperature, torsional vibration amplitude, silicone oil viscosity, rotational speed, housing and inertia ring clearance, and heat generation power based on Comsol. A bench test was carried out on a 6-cylinder inline diesel engine. The simulated temperature profile matched the tested result in terms of trend and relative value, and the established model could accurately predict the temperature field of the engine crankshaft torsional vibration damper.

Prediction of geothermal temperature field by multi-attribute neural network

Hot dry rock (HDR) resources are gaining increasing attention as a significant renewable resource due to their low carbon footprint and stable nature. When assessing the potential of a conventional geothermal resource, a temperature field distribution is a crucial factor. However, the available geostatistical and numerical simulations methods are often influenced by data coverage and human factors. In this study, the Convolution Block Attention Module (CBAM) and Bottleneck Architecture were integrated into UNet (CBAM-B-UNet) for simulating the geothermal temperature field. The proposed CBAM-B-UNet takes in a geological model containing parameters such as density, thermal conductivity, and specific heat capacity as input, and it simulates the temperature field by dynamically blending these multiple parameters through the neural network. The bottleneck architectures and CBAM can reduce the computational cost while ensuring accuracy in the simulation. The CBAM-B-UNet was trained using thousands of geological models with various real structures and their corresponding temperature fields. The method’s applicability was verified by employing a complex geological model of hot dry rock. In the final analysis, the simulated temperature field results are compared with the theoretical steady-state crustal ground temperature model of Gonghe Basin. The results indicated a small error between them, further validating the method's superiority. During the temperature field simulation, the thermal evolution law of a symmetrical cooling front formed by low thermal conductivity and high specific heat capacity in the center of the fault zone and on both sides of granite was revealed. The temperature gradually decreases from the center towards the edges.

Temperature field analysis and prediction of winter construction warm shed method based on hot air heating

The warm shed method is commonly employed in concrete construction during winter as an effective technique. This study aims to investigate the impact of different sizes and heating methods on the temperature inside the shed and the concrete through scale model testing. The hot air heating method is simulated using finite element software, and a prediction model is developed. The findings reveal an inverse relationship between the size of the warm shed and its heat preservation effect. When utilizing hot air, both the maximum temperature inside the warm shed and at the center of the concrete increase by 15.5 °C and 25.2 °C respectively compared to when employing a point heat source, resulting in earlier stabilization times of 390 min and 1000 min respectively, as well as a more uniform temperature distribution across all parts. Based on finite element simulation results, an optimal wind speed μ is proposed along with a corresponding prediction model.

Modeling spatiotemporal temperature dynamics of large-format power batteries: A multi-source information fusion approach

Uneven temperature distributions can significantly impact battery performance and cycle life. Industrial applications, in particular, where unknown disturbances and limited sensors exist, pose significant challenges for accurately estimating the battery temperature field. This work proposes a multi-source information fusion framework for capturing the spatiotemporal temperature dynamics of pouch-type Lithium-ion batteries. First, we develop a system model of the battery thermal process based on physical insights, considering unknown parameter deviations and unmodeled dynamics. Next, we use the Galerkin-spectral method to expand the spatiotemporal variable and reduce the original infinite-dimensional system to a low-order model containing the most important system modes. The unknown component of the model is then entirely stripped and integrated into a nonlinear term. To close the reality gap of the physics-based model, we subsequently develop an error compensation model that learns the dynamic behavior from sparse observations. Ultimately, our framework design yields a fusion-driven model for reliable temperature field prediction. Simulations and experimental data validate the superior performance and generalization capability of the proposed method.

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.

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.

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.

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.