Temperature Prediction Method Research Articles

Investigation of temperature effects on wide steel box girder of suspension bridge based on long-term monitoring data

The time-varying temperature distributions on bridge structures may remarkably change structural performance, which may result in differential strain/stress responses on structural members compared with the design conditions. Therefore, it is crucial to have a comprehensive understanding of temperature distributions and its effects on bridges. In this study, taking advantage of structural health monitoring technology, 1-year field monitoring data collected from a long-span suspension bridge were used to investigate the temperature distributions and their effects on the steel box girder. Specifically, the distributions and probability statistics of temperatures on the top and bottom plates were firstly analyzed. Based on which, the transverse and vertical temperature differences on the box girder were further examined, moreover, the representative values of temperature differences for various return periods were calculated by exceedance probability method. At end, a temperature prediction method was proposed to simulated the temperature field distributions during bridge life cycle, to provide substantial temperature data for estimating future operation condition. The results of this study were beneficial to structural evaluation of in-service bridges to ensure their serviceability and integrity in the life cycle.

A novel temperature prediction method in complex indoor environments based on parameter estimation

This article proposes a novel temperature prediction technique for complex indoor environments based on parameter estimation. The method utilizes the Boussinesq approximation to establish a thermal model and estimate key parameters including pressure terms, heat transfer coefficients, and thermal diffusivity. An approximate approach is adopted to simulate the thermal dynamics of an indoor space. By comparing simulation results under an initial temperature with actual measurements, accurate parameter values are obtained. The estimated parameters can then be applied to achieve high-accuracy temperature prediction given varying initial conditions in the same environment. Moreover, the method can be formulated into a state-space model, enabling offline optimal temperature control. Through identifying the governing parameters via data-driven modeling, it provides an effective engineering tool for analyzing heat transfer and regulating temperature in intricate indoor settings. Rigorous derivation and sufficient validation demonstrate the reliability of this method. Given its potential in fields like architecture design and thermal engineering, this technique offers valuable impacts and insights for temperature management in enclosed environments.

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

As the core component of a ship’s engine room, the operation of a marine diesel engine (MDE) directly affects the economy and safety of the entire vessel. Predicting the future changes in the status parameters of a MDE helps to understand the operational status, enabling timely warnings to the engine crew, and to ensure the safe navigation of the vessel. Therefore, this paper combines the temporal pattern attention mechanism with the bidirectional long short-term memory (BiLSTM) network to propose a novel trend prediction method for short-term exhaust gas temperature (EGT) forecasting. First, the Pearson correlation analysis (PCA) is conducted to identify input feature variables that are strongly correlated with the EGT. Next, the BiLSTM network models input feature variables such as load, fuel oil pressure, and scavenging air pressure and capture the interrelationships between different vectors from the hidden layer matrix within the BiLSTM network. This allows the selection of valuable information across different time steps. Meanwhile, the temporal pattern attention (TPA) mechanism has the ability to explore complex nonlinear dependencies between different time steps and series. This assigns appropriate weights to the feature variables within different time steps of the BiLSTM hidden layer, thereby influencing the input effect. Finally, the improved slime mold algorithm (ISMA) is utilized to optimize the hyperparameters of the prediction model to achieve the best level of short-term EGT trend prediction performance based on the ISMA-BiLSTM-TPA model. The prediction results show that the mean square error, the mean absolute percentage error, the root mean square error and the coefficient of determination of the model are 0.4284, 0.1076, 0.6545 and 98.2%, respectively. These values are significantly better than those of other prediction methods, thus fully validating the stability and accuracy of the model proposed in this paper.

A new LSTNet-based temperature prediction model for permanent magnet

Permanent magnet synchronous motors (PMSMs) can effectively protect against demagnetization by accurate permanent magnet (PM) temperature prediction; nevertheless, due to the nonlinear properties and intricate internal structure of PMSMs, accurate PM temperature prediction methods still encounter difficulties. This paper proposes a new PM temperature prediction model (LSTNet-Improved) based on long- and short-term time series network (LSTNet) to increase the prediction accuracy of PM temperature. By adding a multi-scale convolutional (MSC) layer to the convolutional neural network (CNN) layer of LSTNet, the short-term detail-dependent information between PMSM variables can be obtained by the model, and adds a CNN skip (CNN-skip) layer in place of the gate recurrent unite skip (GRU-skip) layer to find the long- and short-term local repetitive patterns between the variables, which yields additional feature information. Furthermore, a nonlinear bias term (MNB) is added to the original multi-head attention layer in order to solve the complex nonlinear relationship between multivariate time series. These layers are applied after the CNN-skip layer and gated recurrent unite (GRU) layers, respectively. This allows the model to become more robust and automatically learn to the complex nonlinear relationship between the sequences, focus on the important feature information, and lessen the impact of redundant information on the PMs’ temperature prediction task. The experimental results show that the goodness of fit (R2), root mean square error (RMSE) and mean absolute error (MAE) of the proposed model are 99.93%, 14.55% and 9.45%, respectively, and compared with LSTNet, the R2 is improved by 0.09%, the RMSE and MAE are reduced by 7.03% and 6.51%. These results confirm that the model can correctly predict the PM temperature, efficiently extract both long- and short-term patterns among the variables of the PMSM, and effectively focus on the key feature information in the intricate nonlinear series.

Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Blast furnace high-proportion pellet smelting stands as a pivotal pathway towards achieving low-carbon and environmentally friendly ironmaking. However, challenges in pellet ore production, particularly through the rotary kiln method, have surfaced due to the scarcity of iron ore resources, leading to issues like “ringing” that hamper production efficiency and energy conservation. Effectively predicting and optimally controlling the temperature field within rotary kilns emerges as a crucial strategy to address this pressing challenge. In a pioneering effort, this paper introduces a data-driven machine learning methodology designed to expedite the prediction of temperature field distributions within rotary kilns. Through a detailed analysis of field data from the factory and the insights of experts, the paper has discussed on the key factors influencing the temperature field dynamics in rotary kilns. Leveraging Computational Fluid Dynamics (CFD), a numerical simulation model was established to replicate rotary kiln oxy-coal combustion scenarios. A comprehensive dataset was generated using high-throughput computing technology, encompassing a variety of conditions such as coal injection rates, return air temperature, return airflow volume, fixed carbon content of pulverized coal, and volatile fraction of pulverized coal. This dataset included a total of 625 distinct cases. Subsequently, employing machine learning techniques including random forest (RF) and genetic algorithm-optimized Random forest (GA-RF), predictive models were developed for rotary kiln temperature field dynamics. A rigorous comparative analysis was conducted to explore the impact of different machine learning dataset sizes on the predictive accuracy of the models. The results of this analysis demonstrate that, enhanced by the genetic algorithm of RF, it surpasses the performance of the standard RF approach. This enhancement resulted in a significant improvement in prediction accuracy, as evidenced by the reduction of the Root Mean Square Error (RMSE) from 25.0879 to 17.5330 and the Mean Absolute Error (MAE) from an initial value of 6.5600 to 4.7661. Furthermore, the novel soft-measurement method introduced in this paper significantly reduces simulation time by several orders of magnitude. It has diminished the response time from the traditional industrial methods, which took two to three weeks, to approximately 10 s, as compared to conventional CFD approaches. Remarkably, this approach successfully preserves a high level of accuracy. The implications of these research findings are far-reaching,providing novel insights into the acceleration of CFD simulation calculations and the design of soft measurement sensors customized for black-box reactors in industrial processes.

A novel online prediction method for vehicle cabin temperature and passenger thermal sensation

Vehicle cabin temperature and passenger thermal sensation are essential indicators for the vehicle thermal management system. However, the temperature distribution in the vehicle cabin is nonlinear, and the passenger thermal sensation is subjective. In this study, an online prediction method for advanced control is proposed. The cabin temperature is predicted based on the physics-data hybrid-driven model. Passenger comfort is predicted based on the human–machine interaction technology. The results show that the root mean squared error of the temperature prediction model is less than 0.5 °C, and the prediction accuracy of passenger thermal sensation is more than 91.7 %. The method proposed in this paper has broad applicability, and only a small amount of data is needed to complete the prediction process.

Prediction of Onset Temperature in Standing Wave Thermoacoustic Engine with Mesh Screen Stack

A thermoacoustic engine is a engine that converts thermal energy into acoustic energy, which can be used to generate electricity or cooling. This engine is attractive because it consists only of a stack, heat exchangers, and a resonator. The stack serves as the primary component for the energy conversion process and consists of porous materials like an array of stainless steel mesh screens. To generate the acoustic energy, a minimum temperature difference is necessary between the two sides of the stack, called the onset temperature difference. However, the calculation for prediction of onset temperature on the stack made of mesh screen has not been addressed. Therefore, the objective of this paper is to propose a method that can be used to estimate the onset temperature difference in standing wave thermoacoustic engine with stacks made of mesh screen arrays. The onset temperature difference is predicted numerically using linear stability theory and matrix transfer methods. Experimental verification is carried out by using standing wave thermoacoustic engine from pervious study. The results showed that the lowest onset temperature difference (TH - TC = 140ºC) is obtained when rh = 0.497 mm. Furthermore, the numerical and experimental onset temperature difference comparisons show a qualitative agreement, allowing the onset temperature prediction method to be used in designing standing wave thermoacoustic engines with stacks made of mesh screens.

Development of Paper Temperature Prediction Method in Electrophotographic Processes by Using Machine Learning and Thermal Network Model

Development of Paper Temperature Prediction Method in Electrophotographic Processes by Using Machine Learning and Thermal Network Model

Development of a Temperature Prediction Method Combining Deep Neural Networks and a Kalman Filter

Development of a Temperature Prediction Method Combining Deep Neural Networks and a Kalman Filter

Improved thermal hydraulic network modelling and error analysis in disc‐type transformer windings

AbstractTemperature distribution in transformer windings is a crucial factor that influences the safe operation, and lifespan of an insulating system in an oil‐immersed power transformer. Thermal hydraulic network modelling (THNM) has been the subject of numerous studies because of its lower computation cost as compared to a computational fluid dynamics (CFD) simulation. This study proposes improvements to THNM, with primary focus on non‐uniformity of temperatures in the entry and exit vertical oil ducts, and the feasibility of using a piecewise fitting formula for Nusselt number (Nu) of convection heat transfer coefficient in transformer windings. The deviation of hot spot temperature was 0.3°C, and the location of hot spot for CFD and THNM was 34th disc and 35th disc, respectively. The calculation time of CFD and THNM was about 34 h and 35 s under the same computer hardware situations. This provides an acceptable THNM method for the rapid prediction of transformer hot spot temperature and location at low costs for digital twin.

A knowledge transfer online stochastic configuration network-based prediction model for furnace temperature in a municipal solid waste incineration process

To alleviate the concept drift of an offline prediction model for the furnace temperature in a municipal solid waste incineration (MSWI) process caused by changes in working conditions, a prediction method for the furnace temperature based on a knowledge transfer online stochastic configuration network is proposed in this work. The proposed method includes offline learning and online learning. First, a stochastic configuration network is utilized to construct the offline furnace temperature prediction model, and a knowledge transfer method is employed to update the model under new operating conditions in the offline learning stage. Then, the updated model is used as the initial state of online modeling. Second, the recursive solution of the model output weights is presented to adapt to the dynamic change in incineration conditions, and a direction forgetting mechanism is utilized to enhance the result of the prediction model for the furnace temperature under nonpersistence of excitation in the online stage. Finally, to further verify the proposed online modeling method, the real historical data of an MSWI plant are utilized to finish the comparative experiments. The experimental results with the other methods show that the proposed prediction method for the furnace temperature presents a smaller error. Hence, the proposed method can reduce the influence of working conditions on the accuracy of furnace temperature prediction models.

Experimental investigation of heat transfer and structure optimization for regenerative cooling channels using n-decane

Regenerative cooling technologies are considered one of the most effective and widely used active thermal protection methods for hypersonic aircraft scramjets. The heat transfer performance of hydrocarbon fuel and the geometric optimization of cooling channels are the key factors to be considered in designing regenerative cooling structures. Therefore, the present study experimentally and numerically investigated the heat transfer characteristics of n-decane in regenerative channels under asymmetric heating conditions and presented a new structure optimization method. The experiment was conducted at 3 MPa and 293 K with a mass flow rate of 0∼2.442 g/s per channel and a heat flux range of 0∼150 kW/m2. Experimental data show that the local Nusselt numbers exhibit a higher sensitivity to changes in mass flow rate compared to heat flux. Compared to symmetric heating, asymmetric heating mainly affects the heat conduction in the solid domain rather than the performance of convective heat transfer. The modified Dittus-Boelter correlation could predict the experimental data with a relative deviation of ±25 %. A rapid temperature prediction method based on heat flux distribution was proposed through heat transfer analysis, and it was utilized with a particle swarm optimization algorithm to conduct structure optimization.

A novel PoI temperature prediction method for heat source system based on graph convolutional networks

The ever-increasing functional density and complexity of the heat source system, the harsh chip cooling environment, as well as the cost reduction measures that require less sensor involvement are increasingly driving the need to develop new approaches for temperature monitoring and predicting. Most current researches investigate the temperature field reconstruction of the whole system. However, the reconstruction is not necessary or practical in some cases for computing consumption and unprocurable structure. The temperature prediction of the points of interest (PoIs) like the heat-sensitive area in the electronics is essentially important for function maintenance. Thus, a complete solution is proposed based on the graph convolutional networks (GCN) in this paper including the dimensional alignment, graph modeling and corresponding GCN construction. Moreover, various methods have been explored for edge modeling and node embedding obtained by Node2Vec has been integrated for better graph representation. After model training, the real-time temperature prediction of PoIs can be realized according to the corresponding temperature of monitoring points (MoPs) by the GCN. The results of experiments show that this method approach well prediction that the mean absolute error is less than 0.01K under the condition possessing diverse MoPs and PoIs. Moreover, the comparison experiments with the baseline methods further verify the validity of this GCN-based solution.

Research on prediction model of electric vehicle thermal management system based on particle swarm optimization- Back propagation neural network

The variation of temperature in various components of the Electric Vehicle Thermal Management (EVTM) system is the main factor affecting energy consumption. A good temperature prediction method is a prerequisite for optimizing energy strategies. The temperature variation of each component in the thermal management system is influenced by various factors, such as ambient temperature, vehicle speed, and air conditioning compressor speed. Therefore, this paper proposes a temperature prediction method based on Particle Swarm Optimization (PSO) and Back propagation Neural Network (BP). The PSO-BP method is used to update the weights and thresholds of the neural network. Real-time data collected from road experiments are used to ensure the accuracy of the influencing factors. Simulation results show that compared to the BP neural network, the proposed PSO-BP prediction method reduces the prediction errors by 66%, 75%, and 25% respectively.

Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys

The present study proposes a physics-informed machine learning (PIML) algorithm-based approach aimed at predicting the martensitic transformation temperature (Ms) for the design of “NiTi-like” high entropy shape memory alloys (HESMAs). A previously established HESMAs database is enriched and extended to include binary, ternary, quaternary, quinary and senary alloys containing the most employed alloying elements for HEAs design such as Ni equivalents (Fe, Cu, Co, Pd, Pt and Au), Ti equivalents (Zr and Hf), Nb and Ta. The Extremely Randomized Trees algorithm, based on the concept of multiple random decision tree predictions, is adopted as the regression method for Ms temperature prediction. Two strategies for the algorithm inputs have been adopted, discussed, and compared in terms of reliable predictions. The first relies on the composition of the alloying elements, whereas the second exploits a defined set of intrinsic material descriptors. The latter are based on mixing enthalpy, atomic radius, electronegativity, atomic number and number of elements. A high accuracy of the MS prediction has been reached when considering the material descriptors. In fact, the second strategy induces a mean absolute error that is less than 30 °C for alloys containing up to 4 elements. For more elements there are more discrepancies due to the homogenization state required for HEAs. The validation of the developed approach has been performed using 6 home-made HESMAs prepared specifically for this study. It demonstrated the predictive capabilities of the developed physics-informed machine learning based approach. Finally, a HESMA design tool has been implemented to virtually design new HESMAs with a targeted Ms temperature above 400 °C. It is worth noting that this aspect is one of the most challenging engineering issues for such alloys. An illustrative case applied to the (NiCuPd)50(TiZr)50 family of alloys demonstrates the predictive capabilities of the developed approach to design such alloys to achieve a Ms temperature in the range of 300 °C to 700 °C.

Statistical Method and Deep Learning Model for Sea Surface Temperature Prediction

Statistical Method and Deep Learning Model for Sea Surface Temperature Prediction

Junction temperature prediction method of GaN HEMT power devices based on accurate on-voltage testing

Compared with Si devices, GaN HEMT has lower on-resistance, lower junction capacitance, higher switching frequency, higher switching speed and higher junction temperature capability. It is widely used in automotive, aerospace and other fields. Severe environments may cause the junction temperature of GaN-HEMT to be too high, and excessive temperature is the main reason for power device failure. In order to ensure stable operation of GaN devices under harsh conditions, it is important to extract junction temperature information of semiconductor devices simply and effectively. However, most of the existing junction temperature prediction methods are not applicable to GaN HEMT devices due to its high switching speed and high requirement of prediction accuracy. Thus, we proposed a new method based on an accurate on-voltage clamp circuit to predict the junction temperature of GaN HEMT devices. The interrelation between the on-resistance and the junction temperature is analyzed, and the temperature coefficient is calibrated to obtain the corresponding relationship between the characterization quantity and the junction temperature. The on-resistance testing circuit suitable for GaN device is given and analyzed in detail. Finally, the conduction resistance testing circuit is used to predict the junction temperature of GaN devices, achieving extremely high accuracy. Experiments have verified under 100 ℃, the predicted junction temperature and the actual junction temperature have achieved more than 90% coincidence.

Control methodology for tensioned web considering thermal behavior in roll-to-roll manufacturing systems

The roll-to-roll manufacturing system is utilized for processing flexible webs into functional films, offering high productivity through continuous multi-stage processing. In the converting span, which encompasses coating, printing, and drying processes, precise tension control and appropriate tension settings are crucial to ensure the quality of the final product. Particularly during the drying process, where solvent removal occurs, thermal deformation of the web induces additional elongation, amplifying the impact of tension on coating layer quality and printing patterns. Consequently, the drying process necessitates more accurate tension control compared to other span, necessitating the application of feedforward control that considers web thermal deformation. However, the current tension control logic employs an imprecise temperature distribution within the dry span as an input, resulting in inadequate tension control performance and defects such as uneven coating layers and registration errors. This research presents the development of a feedforward tension controller that employs Finite Element Method (FEM)-based web temperature distribution inputs to enhance tension control performance during the drying span. The tension control performance was compared and analyzed based on the drying span web temperature prediction method, affirming that higher accuracy in predicting the web temperature distribution leads to improved tension control performance. Experimental results from an industrial-scale roll-to-roll system demonstrate that the proposed model enhances tension control accuracy during the drying process by 27.76% compared to the existing control logic. Furthermore, the effectiveness of the tension control logic in enhancing the surface quality of functional layers was confirmed through surface quality analysis using solid oxide fuel cell (SOFC) electrolyte layers.

Improved LPTN-Based Online Temperature Prediction of Permanent Magnet Machines by Global Parameter Identification

This paper presents an improved lumped parameter thermal network (LPTN) based online temperature prediction method of permanent magnet (PM) machines by global parameter identification. The proposed global parameter identification incorporates not only the loss but also the magnetic information into a fifth-order LPTN and identifies the multi-physical magneto-thermal parameters as a whole. In the identification process, the nonlinear characteristics, including the magnetic saturation, the cross-saturation, the strand skin effect, and the speed-dependent convective heat transfer, are all considered. It can eliminate the cumbersome magnetic and loss measurement efforts and improve the overall identification accuracy compared to the traditional step by step identification method. Based on the identified multi-physical magneto-thermal model, the critical temperatures of the PM machines, together with the online losses and torque, can then be predicted in real time. The experimental results show that the proposed online temperature prediction method can track the real temperature variation with errors within 5°C.

Condition Monitoring Method for the Gearboxes of Offshore Wind Turbines Based on Oil Temperature Prediction

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring.