A study on temperature field prediction of complex electronic cooling systems based on partition-cascaded encoder-decoder

Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.

Radiative energy transfer in induction plasma modelling

A semianalytical method based on the separation of variables is presented to solve the problem of heat transfer in multilayer skin tissues. This method is used to predict the temperature field and thermal damage of skin tissues, which are subjected to the time-varying laser heating and fluid cooling. The effect of convective heat transfer coefficient of the skin surface on temperature field and thermal damage is studied. The temperature field and thermal damage of skin tissue with different laser heating cycles are calculated. The relationship between the laser heating period and the temperature fluctuation is obtained. The effects of the peak time of laser heating on temperature and the thermal damage of skin tissues are investigated. The relationship between the peak time of laser and the temperature fluctuation of skin tissue is drawn. The results calculated in this paper can provide some guidance for hyperthermia of skin tissue. The presented semianalytical method can be applied to solve the general heat transfer problems of multilayer structures.

Full-scale experimental and computational fluid dynamics (CFD) methods were used to investigate the velocity and temperature fields in a mechanically ventilated room. Detailed airflow fields were measured in three cases of ventilation air temperature: an isothermal case, a hot case and a cold case. The ventilation system created a round jet which developed near the ceiling. The experimental data were used to test two turbulence models: a first order k-ε turbulence model and a second order RSM turbulence model. The RSM model predicted the temperature and velocity fields better than the k-ε turbulence model. In particular, experimental global values of velocity and temperature were in good agreement with the RSM turbulence model. This model is recommended for simulation of ventilated enclosures with thermal effects.

Prediction of temperature field and residual stress of oscillation laser welding of 316LN stainless steel

Temperature field prediction for various porous media considering variable boundary conditions using deep learning method

Temporal prediction of three-dimensional spatial fields of ocean temperature, salinity and flow is important for a number of civilian and military applications. For accurate prediction of regional ocean environments, combing an accurate dynamic ocean model with optimized in-situ observations of ocean states provided by a network of marine vehicles (such as unmanned surface vehicles, autonomous underwater vehicles, and underwater gliders) is an important approach. To realize fast and accurate three-dimensional spatial prediction of regional ocean environments via ocean models and fast optimization of ocean observation strategies of marine vehicles guided by ocean models, this paper proposes a data driven dynamic model for temporal prediction of three-dimensional regional ocean environments. The proposed model is based on the deep learning method, and is developed based on the Convolutional-LSTM (long short-term memory) network. The feature of the proposed model is that the correlation of temperature, salinity and flow is considered and implemented in the neural network, with which the joint prediction of three-dimensional ocean temperature, salinity and flow fields is achieved. Data set form a numerical ocean model is used to conduct the training of the model and the results demonstrate that the proposed model could provide more accurate prediction than implementing prediction of single ocean temperature, salinity or flow field. The proposed model could be integrated with marine vehicles to form an accurate, high-resolution three-dimensional regional ocean prediction and fast-response adaptive sensing system.

Reliable NOx modeling depends on the accurate prediction of both velocity and temperature fields. The velocity and temperature fields of a propane diffusion flame combustor, with interior and exterior conjugate heat transfers, were first numerically studied. The results from three combustion models, together with the renormalization group (RNG) k-ε turbulence model and the discrete ordinates radiation model are discussed, and compared with comprehensive experimental measurements. The flow patterns and the recirculation zone length in the combustion chamber are excellently predicted, and the mean axial velocities are in fairly good agreement with the experimental data for all three combustion models. The mean temperature profiles are fairly well captured by the probability density function (PDF) and eddy dissipation (EDS) combustion models. However, the EDS-finite-rate combustion model fails to provide an acceptable temperature field. Based on the acceptable velocity and temperature fields, a number of NO modeling approaches were evaluated in a postprocessing mode. The partial-equilibrium approach of O and OH radical concentrations shows a significant effect on the thermal NO formation rate. In contrast, the prompt NO, the NO reburn mechanism and the third reaction of the extended Zeldovich mechanism have negligible effects on the overall NO formation in the present study. This study indicates that the semiempirical, postprocessing NO model can provide valuable NO simulations as long as the velocity and temperature fields are adequately predicted.

Efficient online prediction and correction of 3D combustion temperature field in coal-fired boilers using GDNN

Formation of dissimilar Al - Ti weld joints is of great interest of manufacturer in the area of automotive, aircraft, aeronautic and other industries. Joining of Al - Ti alloys represents quite difficult problem not only due to the considerable differences in material properties of these alloys but also due to their susceptibility to form brittle intermetallic compounds when conventional fusion welding methods are applied. Today the effort of researchers for the successful joining of aluminum to titanium alloys is connected mainly with the demands on minimizing of the thickness of brittle intermetallic zones by selection of appropriate welding parameters or application of suitable filler materials. The paper deals with numerical simulation of the laser welding-brazing process of titanium Grade 2 and EN AW 5083-H111 aluminum alloy sheets with the thickness of 2.0 mm using the 5087 aluminum filler wire with the diameter of 1.2 mm. Based on the developed simulation model, the influence of technological parameters of laser welding-brazing including the laser power, welding speed and laser beam offset on the transient temperature fields is studied applying the ANSYS finite element code and used for the prediction of temperature fields during the welding-brazing process and the quality of produced joints. The results of numerical simulation are verified by experimental temperature measurements during laser welding-brazing process applying the TruDisk 4002 disk laser.

The temperature of turbine blades is a critical factor influencing their performance and lifespan. However, a high cost is required for the traditional experimental and computational fluid dynamics (CFD) methods to obtain an accurate temperature field of turbine blades. In this paper, an effective temperature field reconstruction method that combines proper orthogonal decomposition (POD) with an artificial neural network (ANN) is proposed. Initially, POD is employed to reduce the dimensionality of the turbine blade temperature field data by extracting the dominant spatial modes and corresponding mode coefficients, thereby significantly reducing data complexity. Subsequently, an ANN with a feedforward neural network as its core is developed to predict the mode coefficients, facilitating rapid reconstruction of the temperature field. Comparative results indicate that the POD-ANN approach not only maintains high prediction accuracy—with a maximum relative error of 2.61% for fluid and solid fields and only 0.10% for the solid domain—but also dramatically reduces computation time, achieving a speedup of 793 223.2 relative to conventional CFD methods. This study, therefore, presents a robust and feasible technical approach for the rapid prediction and optimization of turbine blade temperature fields.

The current study aims to provide an understanding of the heat transfer process in compacted aluminum alloy AA6061 machining chips during the friction consolidation process (FCP) through experimental investigations and mathematical modelling and numerical simulation. Compaction and friction consolidation of machining chips is the first stage of the Friction Extrusion Process (FEP), which is a novel method for recycling machining chips to produce useful products such as wires. In this study, compacted machining chips are modelled as a continuum whose material properties vary with density during friction consolidation. Based on density and temperature dependent thermal properties, the temperature field in the chip material and process chamber caused by frictional heating during the friction consolidation process is predicted. The predicted temperature field is found to compare well with temperature measurements at select points where such measurements can be made using thermocouples.

Subsets of data from spatially sampled temperatures measured in each of nine experimental heatings of normal canine thighs were used to test the feasibility of using a state and parameter estimation (SPE) technique to predict the complete measured data set in each heating. Temperature measurements were made at between seventy-two and ninety-six stationary thermocouple locations within the thigh, and measurements from as few as thirteen of these locations were used as inputs to the estimation algorithm. The remaining (non "input") measurements were compared to the predicted temperatures for the corresponding "unmeasured" locations to judge the ability of the estimation algorithm to accurately reconstruct the complete experimental data set. The results show that the predictions of the "unmeasured" steady-state temperatures are quite accurate in general (average errors usually < 0.5 degrees C; and small variances about those averages) and that this reconstruction procedure can yield improved descriptors of the steady-state temperature distribution. The accuracy of the reconstructed temperature distribution was not strongly affected by either the number of perfusion zones or by the number of input sensors used by the algorithm. One situation extensively considered in this study modeled the thigh with twenty-seven independent regions of perfusion. For this situation, measurements from ninety-six to thirteen sensors were used as input to the estimation algorithm. The average error for all of these cases ranged from -0.55 degrees C to +0.75 degrees C, respectively, and was not strongly related to the number of sensors used as input to the estimation algorithm.(ABSTRACT TRUNCATED AT 250 WORDS)

With the increasing size of lithium-ion batteries (LIBs), more temperature sensing points can be deployed to capture a comprehensive thermal profile, enabling more accurate monitoring of battery states. However, traditional internal short circuit (ISC) recognition methods often exhibit limited sensitivity to early-stage micro short circuits, delayed response, and vulnerability to external thermal disturbances, leading to false alarms or missed detections. To address these issues, this study proposes an early ISC identification method that integrates a thermal resistance network model with a cosine similarity-based diversity analysis, and the model-predicted temperature field is used as a reference baseline. Then, a dissimilarity index based on cosine similarity is introduced to dynamically monitor local deviations in the measured temperature matrix, thereby enabling early identification of micro short circuits. Meanwhile, by analyzing the evolution trend of discrepancies between the measured and predicted temperature fields, the method can effectively identify internal fault symptoms even under weak external thermal disturbances. Furthermore, by simulating the initial thermal behavior of an ISC through local heating experiments using electric heating wire, the results show that the proposed method achieves higher accuracy, robustness, and response speed than conventional temperature difference or gradient-based approaches, particularly for micro short circuits. This method not only improves the sensitivity and reliability of micro short circuit recognition but also provides a promising solution for digital thermal safety monitoring of large-sized LIBs.

A note on the use of Dynamic Mode Decomposition in mechanics