Accurate Temperature Prediction Research Articles

Enhanced Short-Term Temperature Prediction of Seasonally Frozen Soil Subgrades Using the NARX Neural Network

Accurate prediction of subgrade temperatures in seasonally frozen regions is crucial for understanding thermal states, frost heave phenomena, stability, and other critical characteristics. This study employs a nonlinear autoregressive with exogenous input (NARX) network to predict short-term subgrade temperatures in the Golmud-Nagqu section of China’s National Highway 109. The methodology involves preprocessing subgrade monitoring data, including temperature, water content, and frost heave, followed by developing a temperature prediction model. This tailored NARX neural network, compared to the traditional BP neural network, integrates feedback and delay mechanisms for monitoring data, offering superior memory and dynamic response capabilities. The precision of the NARX model is assessed with the backpropagation (BP) network, indicating that the NARX neural network significantly outperforms the BP model in both precision and stability for temperature prediction in seasonally frozen subgrades. These findings suggest that the NARX model is a valuable tool for accurately predicting subgrade temperatures in seasonally frozen regions, offering significant insights for practical engineering applications.

Steady-State Temperature Prediction for Cluster-Laid Tunnel Cables Based on Self-Modeling in Natural Convection

Accurate temperature prediction of the operating tunnel cable is crucial for its safe and efficient function. To achieve a rapid and accurate prediction of the steady-state temperature of the tunnel cable, the self-modeling pattern in natural convection on the cable surface in the rectangular tunnel is investigated, and the self-modeling method for the convective heat transfer coefficient calculation is proposed. A thermal circuit model for single cables is further established to predict the cable core temperature, and the model is extended to predict the cluster-laid cable core temperature based on the combined method. The results show that when the tunnel size is neglected, the maximum relative deviation of the convective heat transfer coefficient between the self-modeling method and the finite element simulation is only 1.78% in the studied cases, indicating that the natural convection on the cable surface approximately satisfies the self-modeling method. Additionally, applying the self-modeling method to the thermal circuit can accurately predict the temperature of the single cable core. Furthermore, for the three-phase four-circuit cable, the maximum deviation between the temperature prediction results and the finite element results is within 2 K in the studied cases, which verifies the predictive accuracy of the combined method for the cluster-laid tunnel cable.

Temperature modeling of wave rotor refrigeration process based on variable time-delay compensation and SDAE-GRU

Abstract Aiming at the problem of noise and time-delay in temperature prediction modeling of large wave rotor refrigeration process, this paper proposes a novel modeling algorithm that combines the advantages of stacked denoising autoencoder (SDAE) network and Gate Recurrent Unit (GRU) algorithm and utilizes the Maximal Information Coefficient (MIC) algorithm to solve the targeting problem. The MIC algorithm is utilized to calculate the delay time of each input variable and output variable, and the input matrix is reconstructed to compensate for the time-delay, thus solving the problem afterwards. The SDAE network is utilized for denoising the input data and feature extraction to solve the problem of presence of noise in the data. The simulation results show that the proposed MIC-SDAE-GRU algorithm achieves a Root Mean Squared Error (RMSE) of 0.6432 and an R-squared (R²) value of 0.9638, outperforming traditional machine learning methods and other deep learning approaches. Specifically, compared to the standard GRU algorithm, it improves the accuracy of temperature prediction for the wave rotor refrigeration process by 24.7% and exhibits strong generalization ability across various operating conditions.

Integrating Thermal Infrared Imaging and Weather Data for Short-Term Prediction of Building Envelope Thermal Appearance

This study presents a novel deep-learning framework for predicting the thermal appearance of building envelopes under varying weather conditions based on a new dataset collected using a thermal infrared camera at 10 min intervals over a one-and-a-half-year period. Unlike existing studies that rely on simulated data or physical models that do not always accurately reflect the complex heat transfer processes in real buildings, we have collected a large dataset showing how a building behaves under different climatic conditions. We propose a novel deep-learning approach that integrates weather data and thermal imagery to predict the temperature distribution on the building façade for the next 24 and 48 h. The model uses a state-of-the-art recurrent neural network architecture, PredRNN V2, with an action conditioning mechanism to incorporate weather forecasting data into the prediction process. We evaluate this approach in terms of average accuracy, prediction accuracy in specific regions, and visual-perceptual performance of the images. The proposed framework achieves a prediction accuracy of 1.5 °C (root mean square error—RMSE) for the 24 h prediction and 2.04 °C (RMSE) for the 48 h prediction, outperforming baseline models in terms of temperature prediction accuracy and structural similarity of the predicted images.

Optimizing neural network models for predicting nuclear reactor channel temperature: A study on hyperparameter tuning and performance analysis

This study emphasizes how important accurate prediction of channel temperatures in nuclear reactors is for safety and operational efficiency. While traditional methods require long and complex processes such as kernel modeling and mathematical simulations, artificial neural networks (ANN) provide more efficient predictions by accelerating this process. The superior ability of ANNs to process large data sets is intended to demonstrate that this study will provide a valuable alternative compared to conventional methods and increase the accuracy of reactor temperature predictions. In this study, the training performances of Artificial Neural Network (ANN) developed to determine the nuclear reactor channel temperature with different hyperparameter combinations were analysed. It was conducted several experimental studies to assess the influence of hyperparameters on our model for nuclear reactor parameter data prediction. The training and validation results indicates that learning rate, hidden layer sizes and number have critical effects for the more precisive prediction. It was observed that models with a learning rate of 0.05 and 0.5 achieved successful learning with less fluctuation in training and validation errors. When looking at hidden layer sizes, networks with 32 and 64 neurons performed better than networks with 16 neurons. For the test phase our model can successfully predict data with slight error margin. As a result, we demonstrated that neural networks are a powerful tool in nuclear reactor channel temperature prediction through our proposed model.

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.

Understanding Heat Dissipation Factors for Fixed‐Tilt and Single‐Axis Tracked Open‐Rack Photovoltaic Modules: Experimental Insights

ABSTRACTThis paper presents the results of long‐term experiments conducted on fixed‐tilt (FT) and single‐axis tracked (SAT) open‐rack photovoltaic (PV) modules in South Africa. Utilising Faiman's heat dissipation model and data filtering method, the study demonstrates favourable comparisons of FT experimental results with literature while yielding novel heat dissipation factors for SAT modules. Enhanced heat dissipation is observed in no/low wind conditions for SAT modules compared to FT modules. Analyses reveal the influence of plane‐of‐array (POA) irradiance, wind speed and direction on module temperature, with SAT modules exhibiting greater heat dissipation stability. An investigation into data filtering methods suggests minor sensitivity for both configurations, with a slightly more pronounced impact on SAT modules. Assessments comparing module temperature predictions using diverse heat dissipation factors for FT modules reveal negligible sensitivity. This suggests that exact heat dissipation factor values may not be crucial for accurate predictions of module temperature in FT open‐rack systems. Annual power output simulations using PVsyst software demonstrate a 2.9% and 3.3% enhancement for FT and SAT configurations, respectively, when employing experimentally determined heat dissipation factors. These findings highlight the importance of realistic, configuration‐specific heat dissipation factors in optimising PV system performance, particularly in the competitive context of modern PV power plant construction and techno‐economic calculations.

Simulation of Friction Stir Welding of AZ31 Mg Alloys.

Friction stir welding has been extensively applied for the high-quality bonding of Mg alloys. The welding temperature caused by friction and plastic deformation is essential for determining the joint characteristics, especially the residual stress and weld microstructure. In this work, a modified moving heat source model was proposed by considering the variations in heat generation caused by friction shear stress at both the side and bottom surfaces of the tool. The application of this model was further extended to the entire welding process, especially in the plunging stage. The relative errors between the experimental and simulated peak temperatures at characteristic points were small, with a maximum of 10%, thereby validating the model for accurate temperature prediction. Furthermore, the influence of welding and rotational speed on temperature fields was systematically investigated. At relatively low welding and rotational speeds, the welding temperature increased significantly with either an increase in rotational speed or a decrease in welding speed. However, this effect gradually diminished at higher welding and rotational speeds. These results provide some valuable guidelines for controlling heat generation to improve the quality of Mg alloy welds.

Relevance-Based Reconstruction Using an Empirical Mode Decomposition Informer for Lithium-Ion Battery Surface-Temperature Prediction

Accurate monitoring of lithium-ion battery temperature is essential to ensure these batteries’ efficient and safe operation. This paper proposes a relevance-based reconstruction-oriented EMD-Informer machine learning model, which combines empirical mode decomposition (EMD) and the Informer framework to estimate the surface temperature of 18,650 lithium-ion batteries during charging and discharging processes under complex operating conditions. Initially, based on 9000 data points from the U.S. NASA Prognostics Center of Excellence’s random battery-usage dataset, where each data point includes three features: temperature, voltage, and current, EMD is used to decompose the temperature data into intrinsic mode functions (IMFs). Subsequently, the IMFs are reconstructed into low-, medium-, and high-correlation components based on their correlation with the original data. These components, along with voltage and current data, are fed into sub-models. Finally, the model captures the long-term dependencies among temperature, voltage, and current. The experimental results show that, in single-step prediction, the mean squared error, mean absolute error, and maximum absolute error of the model’s predictions are 0.00095, 0.02114, and 0.32164 °C; these metrics indicate the accurate prediction of the surface temperature of lithium-ion batteries. In multi-step predictions, when the prediction horizon is set to 12 steps, the model achieves a hit rate of 93.57% where the maximum absolute error is within 0.5 °C; under these conditions, the model combines high predictive accuracy with a broad predictive range, which is conducive to the effective prevention of thermal runaway in lithium-ion batteries.

A Predictive Model for Sintering Ignition Temperature Based on a CNN-LSTM Neural Network with an Attention Mechanism

The sintering ignition process parameters fluctuate frequently and significantly, resulting in large variations in ignition temperature, which in severe cases can exceed 200 °C. This not only increases gas consumption but also affects the quality of the sinter. Because the intelligent control model based on feedback mechanisms struggles to deal with high-frequency fluctuation conditions over time, the prediction of sintering ignition temperature using feedforward regulation is attracting increasing attention. Given the multi-variable, time-sequential and strongly coupled characteristics of the sintering ignition process, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are deeply integrated, with an attention mechanism incorporated to develop the sintering ignition temperature prediction model, enabling the accurate prediction of the ignition temperature. The research demonstrates that the combination of a CNN and the attention mechanism effectively addresses the challenges posed by the multi-variable and strongly coupled nature of sintering ignition data to predictive modeling. The LSTM network resolves the sequential data issues through its gating mechanism. As a result, the coefficient of determination (R2 ) of the CNN_LSTM-Attention model in predicting the sintering ignition temperature can reach 0.97, with a mean absolute error (MAE) as low as 10.23 °C. The predicted values closely match the actual values, achieving a hit rate of 93% within the acceptable error range. These performance metrics are significantly superior to those of the CNN-Attention and LSTM-Attention models, greatly enhancing the control accuracy of the ignition temperature.

Accurate Oil Temperature Prediction Model and Oil Refilling Parameters Optimization for Hydraulic Closed-Circuit System

Oil temperature plays a crucial role in hydraulic closed-circuit systems (HCS), and conventional thermal equilibrium models and coupled simulation models face challenges in terms of accuracy, efficiency, and cost when calculating oil temperature. This study introduces an innovative HCS oil temperature precise prediction model and oil refilling parameter optimization method. The initial sample space was determined through a Sobol sensitivity analysis and improved Latin hypercube sampling, leading to the development of a combinatorial agent model (CAM) suitable for oil temperature prediction with superior accuracy and stability compared to other methods. Based on CAM, the optimal oil refilling flow rates under various operational conditions are computed. To validate the efficacy of the theoretical analysis, an HCS experiment platform was established. The data indicates that the temperature prediction error range of the CAM model falls between 0.30 °C and 1.05 °C, and optimizing the oil refilling flow rate can effectively enhance system efficiency while ensuring that the oil temperature remains within permissible limits. The research methodology and findings are applicable in engineering practice and can be extended to optimize the design of other hydraulic systems.

Deep learning-based rapid prediction of temperature field and intelligent control of molten pool during directed energy deposition process

In this work, deep learning-based approaches are proposed to provide promising solutions to address the challenges in realizing intelligent manufacturing and digital twins for directed energy deposition (DED) process. Firstly, a rapid and accurate prediction of part temperature is realized by innovatively combining graph neural networks (GNNs) and recurrent neural networks (RNNs). Twenty parts with different structures are selected for demonstration. GPU parallel computing technique is adopted to accelerate the thermal finite element analysis, which is used to quickly construct the simulated graph dataset with sufficient samples. By embedding the memory optimization method into the GNN block, deeper GNNs with more trainable parameters are successfully trained with a 79.4% lower GPU memory footprint, which solves the difficulty of deeper GNNs are hard to train on large graph datasets, and the accuracy of temperature prediction on unseen DED parts is significantly improved. Secondly, for intelligent molten pool regulation, a semi-analytic temperature solution method is used to create an efficient DED environment in reinforcement learning (RL) workflows. The intelligent control of molten pool depth under complex deposition strategy is realized based on the environmental state represented by molten pool images. A tailored convolutional neural networks (CNNs) model is employed as the agent to output varying laser power and continuously interact with the dynamic environment. Compared with the traditional artificial neural network agent, the total reward scored by the CNN agent is improved by 9.7% in the zigzag deposition process, mitigating the fluctuations in the controlled molten pool depths. Moreover, CNNs are more compatible with in-situ thermal images. This work can provide theoretical and technical support for realizing real-time and even ahead-of-time temperature prediction and the corresponding feedback control during DED process.

Viscous-Layer Compressibility Correction for Two-Equation Reynolds-Averaged Navier–Stokes Models

The baseline two-equation Reynolds-averaged Navier–Stokes (RANS) models include fluid density but lack calibration for compressible flows, making them inadequate for high Mach numbers. Various compressibility corrections have been proposed to integrate the van Driest transformation or the semilocal transformation, i.e., a given compressible law of the wall, into the RANS formulation. Prior work has focused mainly on the logarithmic layer, but upon evaluating these log-layer compressibility corrections, we find that they do not significantly improve skin friction estimates. To overcome this challenge, we develop viscous-layer compressibility corrections. We do that by altering the dissipation terms. These corrections conform the RANS model to the semilocal scaling, resulting in more accurate predictions of mean velocity and temperature in a posteriori tests. Although not the primary focus of this study, we also find that the baseline one-equation Spalart–Allmaras model, which was proposed before the concept of semilocal scaling, produces results consistent with the semilocal scaling in a posteriori tests without requiring any compressibility correction.

Enhancing Surface Wind Speed and Temperature Prediction Using Surface-Layer Emulator and Transfer Learning

Abstract Accurate prediction of surface wind speed and temperature is crucial for many sectors. Physical schemes in numerical weather prediction (NWP) and data-driven correction approaches have limitations due to uncertainties in parameterization and lack of robustness, respectively. This study introduces a physics-informed data model, PhyCorNet, which combines a deep learning–based physics emulator (PhyNet) and a subsequent correction network (CorNet). PhyNet imitates the revised surface-layer parameterization scheme [default option of the Weather Research and Forecasting (WRF) Model]. CorNet refines predictions by mitigating the difference between PhyNet and observations. PhyCorNet enables gridded forecasts despite the training data being point based. Therefore, PhyCorNet can be regarded as a rediagnostic scheme for surface wind speed at 10 m (WS10) and temperature at 2 m (T2). Compared to WRF, it reduced diagnostic root-mean-square errors in the 24-h forecasts for WS10 and T2 by 40% and 36%, respectively, across China, with unbiased forecasts at almost all sites. PhyCorNet addresses the oversmoothing prediction issue of other deep learning models by providing the ability to represent fine-scale features and perform well in statistically extreme samples. In grid cells without observations for training, PhyCorNet performed much better than WRF, demonstrating the zero-shot learning capability. This study implies that the emulator plus bias correction provided by PhyCorNet could be used as a simple but effective approach to improve the performance of other diagnostic quantities in NWP. Significance Statement We developed a new model called PhyCorNet to improve the surface wind speed and temperature forecasts. Existing weather forecast models have limitations in accurately predicting these variables. PhyCorNet first uses a neural network (PhyNet) to mimic an existing physics-based wind and temperature calculation method. Another neural network [correction network (CorNet)] improves the PhyNet forecasts by comparing them with observations. Across China, PhyCorNet reduced 24-h forecast errors for wind speed by 40% and temperature by 36% compared to a conventional model. It performed well under diverse conditions and overcame the oversmoothing issues faced by other machine learning models. Importantly, PhyCorNet outperformed traditional models in areas without historical observations. We suggest that this new framework can also be used to enhance the forecasts of other atmospheric variables.

Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

A Trombe wall-heating system is used to absorb solar energy to heat buildings. Different parameters affect the system performance for optimal heating. This study evaluated the performance of four machine learning algorithms—linear regression, k-nearest neighbors, random forest, and decision tree—for predicting the room temperature in a Trombe wall system. The accuracy of the algorithms was assessed using R² and RMSE values. The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance, with R² and RMSE values of 1 and 0. In contrast, linear regression and decision tree showed weaker performance. These findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems, enabling informed design decisions to enhance energy efficiency.

Development of a liquid metal subchannel code applied to ocean conditions and study on the effect of core geometry parameters and ocean motions on flow and heat transfer characteristics

The Generation IV International Forum (GIF) proposes a type of liquid metal reactor, due to its thermal properties and efficient heat transfer in a relatively small system. This special heat transfer characteristic allows the liquid metal reactor to be modular in design and flexible in installation on a nuclear-powered ship. Accurate prediction of the coolant temperature and flow distribution between subchannels is important to ensure nuclear safety in the complex ocean environment. In this study, an ocean version of subchannel code is developed by adding the motion terms in the momentum equations for the liquid metal reactor core based on the previously land-based subchannel code. Since the research on the characteristics of flow resistance and heat transfer mechanism under ocean conditions is not mature, the original empirical model is still used in the ocean version code. The constitutive models of the liquid metal are analyzed and incorporated into the ocean code. The code has been verified and validated by comparing with Computational Fluid Dynamics (CFD) simulation results, and experimental data. The effect of geometric parameters such as rod Pitch to Diameter ratio (P/D), Rod to Wall gap (RTW) is studied in the liquid metal lead 7 rod bundles. In addition, the effect of heaving start-up and shutdown motions between liquid metal lead and water is discussed in the 5 × 5 bare rod bundles. In general, the modified subchannel analysis code can well complete the calculation of liquid metal reactor under ocean conditions, as well as provide support for the design and safety analysis of a liquid metal cooled fast reactor.

A Prediction Model for Pressure and Temperature in Geothermal Drilling Based on Physics-Informed Neural Networks

With the global expansion of geothermal energy, accurate prediction of pressure and temperature during drilling has become essential for ensuring the safety and efficiency of geothermal wells. Traditional numerical methods, however, often struggle to handle complex wellbore environments due to their high data demands and limited computational accuracy. To address these challenges, this paper introduces an innovative predictive model based on Physics-Informed Neural Networks (PINNs). By integrating physical laws with deep learning, the model theoretically surpasses the limitations of conventional methods. Trained on pressure and temperature data from a geothermal well in the Xiong’an area, the model demonstrates exceptional accuracy and robustness. Additionally, the model was rigorously tested under extreme wellbore conditions, showcasing its strong generalization capabilities. The findings suggest that PINNs offer a highly advantageous solution for geothermal drilling, with significant potential for practical engineering applications.

Parameter optimization of thermal network model for aerial cameras utilizing Monte-Carlo and genetic algorithm

It is crucial to precisely calculate temperature utilizing thermal models, which require the determination of thermal parameters that optimally align model outcomes with experimental data. In many instances, the refinement of these models is undertaken within space instruments. This paper introduces an optimization methodology for thermal network models, with the objective of enhancing the accuracy of temperature predictions for aerial cameras. The investigation of internal convective heat transfer coefficients for both cylindrical and planar structures provides an estimation of convective thermal parameters. Based on the identification of thermally sensitive parameters and the reliability evaluation of transient temperature data through the Monte-Carlo simulation, the genetic algorithm is employed to search for global optimal parameter values that minimize the root mean square error (RMSE) between calculated and measured node temperatures. As a result, the optimized model shows significantly improved accuracy in temperature prediction, attaining an RMSE of 1.07 ℃ and reducing the maximum relative error between predicted and experimental results from 33.8 to 3.1%. Furthermore, the flight simulation and thermal control experiments validate the robustness of the optimized model, demonstrating that discrepancies between the observed and predicted temperatures are within 2 °C after re-correcting the external convection heat transfer coefficient value.

Research on Temperature Prediction based on BP Neural Network Approach

Weather changes have an important impact on human production and life as well as socio-economic activities. In recent years, people have also gradually paid attention to the study of weather, especially the study of temperature, and it is of great practical significance to find an accurate temperature prediction model. At the same time, the use of machine learning methods to construct a temperature model can predict temperature changes more accurately and quickly, and provide a more reliable basis for weather forecasting. Therefore, in response to the unsatisfactory accuracy of temperature prediction by traditional prediction models, this paper uses the time series of daily average temperature of Hangzhou City, China, from 2013 to 2022 as the sample data, and combines the traditional ARMA model with the BP neural network model, which enables the BP neural network model to better fit and predict the temperature changes. The study shows that the prediction accuracy of the BP neural network model is significantly improved compared with the ARMA model.

Study on microwave ablation temperature prediction model based on grayscale ultrasound texture and machine learning.

Temperature prediction is crucial in the clinical ablation treatment of liver cancer, as it can be used to estimate the coagulation zone of microwave ablation. Experiments were conducted on 83 fresh ex vivo porcine liver tissues at two ablation powers of 15 W and 20 W. Ultrasound grayscale images and temperature data from multiple sampling points were collected. The machine learning method of random forests was used to train the selected texture features, obtaining temperature prediction models for sampling points and the entire ultrasound imaging area. The accuracy of the algorithm was assessed by measuring the area of the hyperechoic area in the porcine liver tissue cross-section and ultrasound grayscale images. The model exhibited a high degree of accuracy in temperature prediction and the identification of coagulation zone. Within the test sets for the 15 W and 20 W power groups, the average absolute error for temperature prediction was 1.14°C and 4.73°C, respectively. Notably, the model's accuracy in measuring the area of coagulation was higher than that of traditional ultrasonic grey-scale imaging, with error ratios of 0.402 and 0.182 for the respective power groups. Additionally, the model can filter out texture features with a high correlation to temperature, providing a certain degree of interpretability. The temperature prediction model proposed in this study can be applied to temperature monitoring and coagulation zone range assessment in microwave ablation.