Prediction Of Maximum Temperature Research Articles

Experimental and FEM Studies of Continuous Drive Friction Welding of Ferritic Spheroidal Graphite Cast Iron

Experimental and FEM studies of the friction welding process of spheroidal graphite cast iron (SGCI) are presented. A coupled thermal and mechanical 2.5 D FEM model was used to simulate the continuous drive friction welding (CDFW) process. The FE model predicted the peak temperature of the joint, effective stress, axial shortening, and the weld flash size. Additionally, the friction force on the axial shortening of specimens was studied. The peak temperatures were measured both on the axis and at the surface of the specimen. The predicted maximum temperatures in the axis, ½ radius, and 2 mm from the surface of the sample amounted to 1162 °C, 1177 °C, and 1061 °C, respectively. The maximum temperature of the spheroidal graphite cast iron joint was below the melting temperature of the base material (~1350 °C). The predicted temperature curves, outbursts, and shortening of welded elements indicated a good match with real models.

Limiting Temperature Rise in Cochlear Implantation Bone Drilling: A Novel Approach.

In the process of cochlear implantation surgery, it is crucial to develop a method to control the temperature during the drilling of the implant channel since high temperatures can result in damage to bone and nerve tissue. This paper simplified the traditional point heat source temperature rise model and proposed a novel extreme peck drilling model to quantitatively calculate the maximum temperature rise value. It is also innovatively introduced a new method for calculating the best peck drilling duty cycle to strictly control the maximum temperature rise value. Besides, the neural network is trained with virtual data to identify two important thermal parameters in the temperature rise model. In the experiment of epoxy resin and temporal bone, the difference between predicted maximum temperature and actual maximum temperature was less than 1.5 °C, and the error rate was less than 10%. And the error source was analyzed by variational mode decomposition, along with discussion of potential solutions. In the temperature control experiment, the model successfully controlled the maximum temperature rise within 10 °C.For cochlear implantation surgery, we also divide the implantation channel into different stages based on the bone density in CT images to identify thermal parameters and calculate drilling strategies. This method provides a new strategy for accurate and effective control of borehole heat generation. These achievements provide new ideas and directions for research in cochlear implantation surgery and related fields, and are expected to have extensive application in medical practice.

Study of spatial temperature features of road tunnel fires under multiple factors

AbstractAfter a highway tunnel fire, the rapid change of the internal ambient temperature directly affects the personal safety of pedestrians. Based mainly on the lateral possibility of fire location and considering the coupling effect of multiple factors in a two‐lane highway tunnel, this study systematically carried out 63 full‐size tunnel fire simulation studies relying on Fluent software. The smoke temperature distribution patterns in the transverse and longitudinal spaces of the tunnel after the fire were revealed. This study shows that the smoke temperature below the tunnel vault is susceptible to the effect of sidewall restraint, making the maximum temperature of the tunnel vault of the offset fire source large; considering the lateral location of the fire source, the maximum temperature prediction formula of the tunnel vault is modified to expose the change law of the longitudinal temperature decay of the vault. This paper reveals for the first time the law of temperature distribution changes at the characteristic height of the human eye on the evacuation platform in the tunnel; the systematic study of the fire source cross‐sectional temperature distribution changes at the key lining structure points, and it is found that a low‐temperature space conducive to personnel escape exists in the lower part of the tunnel away from the fire source side. The results of the study are beneficial to the design of safe evacuation paths in the tunnel and the active emergency evacuation of personnel in the near‐fire source area.

Decoding Optimization Algorithms for Convolutional Neural Networks in Time Series Regression Tasks

Optimization algorithms play a vital role in training deep learning models effectively. This research paper presents a comprehensive comparative analysis of various optimization algorithms for Convolutional Neural Networks (CNNs) in the context of time series regression. The study focuses on the specific application of maximum temperature prediction, utilizing a dataset of historical temperature records. The primary objective is to investigate the performance of different optimizers and evaluate their impact on the accuracy and convergence properties of the CNN model. Experiments were conducted using different optimizers, including Stochastic Gradient Descent (SGD), RMSprop, Adagrad, Adadelta, Adam, and Adamax, while keeping other factors constant. Their performance was evaluated and compared based on metrics such as mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared (R²), mean absolute percentage error (MAPE), and explained variance score (EVS) to measure the predictive accuracy and generalization capability of the models. Additionally, learning curves are analyzed to observe the convergence behavior of each optimizer. The experimental results, indicating significant variations in convergence speed, accuracy, and robustness among the optimizers, underscore the research value of this work. By comprehensively evaluating and comparing various optimization algorithms, we aimed to provide valuable insights into their performance characteristics in the context of time series regression using CNN models. This work contributes to the understanding of optimizer selection and its impact on model performance, assisting researchers and practitioners in choosing the most suitable optimization algorithm for time series regression tasks.

Research on Maximum Temperature Prediction Based on ARIMA–LSTM—XGBoost Weighted Combination Model

Accurately predicting the maximum temperature is essential for studying human comfort, ecological environment development and social progress. However, traditional prediction methods are inefficient and inaccurate when dealing with large volumes of meteorological data. To tackle these challenges, this paper introduces an integrated approach, the ARIMA–LSTM–XGBoost model, which combines the strengths of autoregressive integrated moving average (ARIMA), long short-term memory network (LSTM) and eXtreme Gradient Boosting (XGBoost) to predict the maximum temperature. The proposed model enhances the prediction accuracy and convergence rate through techniques like MAPE reciprocal weight (MAPE-RW) and Schedule Sampling. Additionally, the model selects the best performing model using the early stopping method. This paper compares and analyzes the prediction results of the ARIMA, LSTM, XGBoost and ARIMA–LSTM–XGBoost models. The experimental results indicate that the ARIMA–LSTM–XGBoost model proposed in this paper achieves superior prediction accuracy, performance, and confidence. The ARIMA–LSTM–XGBoost model shows a Root-Mean-Squared Error (RMSE) of 1.381, significantly outperforming the ARIMA model (3.828), LSTM model (3.360) and XGBoost model (1.422). The coefficient of determination ([Formula: see text] is 0.977, surpassing the values of 0.905 for the ARIMA model, 0.922 for the LSTM model and 0.910 for the XGBoost model. The ARIMA–LSTM–XGBoost model also exhibits a higher confidence level compared to the individual models.

Prediction model of thermal behavior of lithium battery module under high charge-discharge rate

In order to achieve accurate thermal prediction of lithium battery module at high charge and discharge rates, experimental and numerical simulations of the charge-discharge temperature rise of lithium battery cells at lower rates of 1C, 2C, and 3C have been conducted firstly to verify the accuracy of the NTGK model (Newman, Tiedemann, Gu, and Kim, NTGK) at low rates. The experimental results of temperature rise at a 5C rate has matched the experimental results and prove the accuracy of the model, and the maximum temperature prediction difference of the GPR-NTGK model (Gaussian process regression, GPR) has been reduced by 4.86 K compared with the original model. Further thermal prediction and temperature rise experiments have been conducted for the battery module at 1C, 2C, 3C and 5C rates, and the simulation results are in good agreement with the experimental results, which verified the applicability of the GPR-NTGK model for the battery module. The maximum temperature at 5C rate is 334.32 K, which is beyond the safe working temperature range of Li-ion batteries, and the high temperature area is concentrated in the lower center of the module, with poor thermal uniformity and a great risk of thermal runaway.

Prediction of maximum temperature of fluid-lubricated bearing based on machine learning algorithm

The fluid-lubricated bearing, whose maximum temperature is one of the key indicators to determine whether major equipment operates normally, is widely used as the bearing part of major equipment. For temperature prediction, numerical calculation method is commonly used, but not the most accurate method. In this work, a comprehensive test rig for the sliding bearing is used to study the influences of journal linear speed, bearing specific pressure, lubricant viscosity and inlet temperature on bearing temperature. Three machine learning algorithms with different complexity, artificial neural network (ANN), k nearest neighbors (KNN) and random forest (RF) were applied to the test data to predict the maximum temperature. Furthermore, the optimal algorithm was obtained and validated. The results show that the bearing temperature is nonlinearly positively correlated with the four operating parameters. The three algorithms have satisfactory accuracy in the low-to-medium temperature range (20 °C–72 °C), but the accuracy of ANN and RF is significantly reduced in the high temperature region (72 °C–95 °C). It is found that KNN is the optimal algorithm because of its high precision and insensitivity to abnormal data. This study provides a relatively accurate and effective method for quickly and accurately predicting the maximum temperature of fluid-lubricated bearings.

Fluid–solid coupled simulation of hypervelocity impact and plasma formation

Previous theoretical and computational studies on hypervelocity impact have mainly focused on the dynamic response of the solid materials that constitute the projectile and the target, while the surrounding environment is often assumed to be a vacuum. In this paper, we consider impact events that occur in a fluid (e.g., gas) medium, and present a computational model that includes the dynamics, thermodynamics, and ionization of the surrounding fluid material. The model couples the compressible Navier–Stokes equations with the Saha equations to predict the onset and extent of ionization in the surrounding fluid. An extended level set method with two signed distance functions is employed to track the three material interfaces between the projectile, the target, and the ambient fluid. This method accommodates the large deformation, contact, and separation of the interfaces, while avoiding spurious overlapping of different material subdomains. The advective fluxes across material interfaces are computed by constructing and solving bimaterial Riemann problems, thereby taking into account the discontinuities in both state variables (e.g., density) and thermodynamic relations. The computational model is first verified for an infinite ideal plasma and a one-dimensional three-material impact problem. Next, the model is employed to analyze the impact of a tantalum rod projectile onto a soda lime glass (SLG) target in an argon gas environment. In different analyses, the impact velocity is varied between 3 and 6km/s, and the radius of the projectile is varied between 2.5 and 10mm. Each analysis starts with a steady-state fluid dynamics simulation that generates the shock-dominated hypersonic flow around the projectile. This flow field is then used as an initial condition to start the fluid–solid coupled impact simulation. The predicted maximum temperature and pressure within the SLG target agree reasonably well with published experimental data for a similar material (fused quartz). Within the ambient gas, the impact-generated shock wave is found to be stronger than the initial bow shock in front of the projectile. Behind this shock wave, a region of high pressure and temperature forms in the early stage of the impact, mainly due to the hypersonic compression of the fluid between the projectile and the target. The temperature within this region is significantly higher than the peak temperature in the solid materials. For impact velocities higher than 4km/s, ionization is predicted. This finding indicates that the ambient gas may be a nontrivial contributor to the plasma formed in terrestrial and atmospheric hypervelocity impact events.

Neural-network modeling of solar radiation and temperature variability due to climate change in Ibadan Metropolis

his research focused on studying the variability of solar radiation and temperature under climate change in the Ibadan metropolis. In the study, spatial distribution, temporal variations, annual distribution, estimation and prediction of the solar radiation, minimum and maximum temperature data in the Ibadan Metropolis was collected. A Long Short-Term Memory Neural Network (LSTM-NN) model was developed for the prediction using the time-series data obtained. An ARIMA model was further developed to compare and validate the LSTM-NN model. The performance of the prediction models were determined using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The RMSE values for the minimum, maximum temperature and solar radiation predictions were 1.543, 1.290, 1.967, and 1.611, 1.309, 2.106 for the LSTM-NN and ARIMA models respectively, while the MAPE values for the minimum, maximum temperature and solar radiation predictions were 3.603, 4.351, 8.859, and 3.840, 4.480, 9.502 for the LSTM-NN and ARIMA models respectively. The LSTM-NN model had a better prediction performance in all categories with lower error when compared with ARIMA. From the prediction, it was observed that there will be a reduction in the maximum temperature, minimum temperature and solar radiation values when compared to obtained data. The observed minimum temperature ranged from 22.9032-23.2032(0C), while the predicted minimum temperature ranged from 19.9260- 19.977(0C) also the observed maximum temperature ranged from 32.87096-33.7064(0C), while the predicted maximum temperature ranged from 29.5159-29.5529(0C), the observed solar radiation ranged from 19.203-19.722 (W/m2 ), while the predicted solar radiation ranged from 14.123-14.115 (W/m2 ). The year with the highest solar radiation which constitutes the useful energy is 2024 with an average value of 14.1395 W/m2

Performance of Pavement Temperature Prediction Models

Appropriate asphalt binder selection is dependent on the correct determination of maximum and minimum pavement temperatures. Temperature prediction models have been developed to determine pavement design temperatures. Accordingly, accurate temperature prediction is necessary to ensure the correct design of climate-resilient pavements and for suitable pavement overlay design. Research has shown that the complexity of the model, input variables, geographical location among others affect the accuracy of temperature prediction models. Calibration has also proved to improve the accuracy of the predicted temperature. In this paper, the performance of three pavement temperature prediction models with a sample of materials, including asphalt, was examined. Furthermore, the effect of calibration on model accuracy was evaluated. Temperature data sourced from Pretoria were used to calibrate and test the models. The performance of both the calibrated and uncalibrated models in a different geographical location was also assessed. Asphalt temperature data from two locations in Ghana were used. The determination coefficient (R2), Variance Accounted For (VAF), Maximum Relative Error (MRE) and Root Mean Square Error (RMSE) statistical methods were used in the analysis. It was observed that the models performed better at predicting maximum temperature, while minimum temperature predictions were highly variable. The performance of the models varied for the maximum temperature prediction depending on the material. Calibration improved the accuracy of the models, but test data relevant to each location ought to be used for calibration to be effective. There is also a need for the models to be tested with data sourced from other continents.

ASSESSMENT OF THE WRF MODEL FOR SOUTHERN REGION OF VIET NAM IN DRY AND RAINY SEASONS

This article investigates the capability of the applicability of numerical WRF model to forecast weather at 48 hours for the Southern Region Viet Nam in the rainy and dry seasons in 2021 (12/2020 - 11/2021). The WRF model shows that predicted maximum temperature, minimum temperature and rainfall are lower than measured values. Temperature forecasts are more accurate than rainfall forecasts. Rainfall forecast in the rainy season has a higher error than the rainfall forecast in the dry season, on the contrary, the temperature forecast in the rainy season gives lower error than the temperature forecast in the dry season.

Numerical study of real gas effects during bubble collapse using a disequilibrium multiphase model

An explicit density-based solver of the Euler equations for inviscid and immiscible gas–liquid flow media is coupled with real-fluid thermodynamic equations of state supporting mild dissociation and calibrated with shock tube data up to 5000 K and 28 GPa. The present work expands the original 6-equation disequilibrium method by generalising the numerical approach required for estimating the equilibrium pressure in computational cells where both gas and liquid phases co-exist while enforcing energy conservation for all media. An iterative numerical procedure is suggested for taking into account the properties of the gas content as derived from highly non-linear real gas equations of state and implemented in a tabulated form during the numerical solution. The developed method is subsequently used to investigate gaseous bubble collapse cases considering both spherical and 2D asymmetric arrangements as induced by the presence of a rigid wall. It is demonstrated that the predicted maximum temperatures are strongly influenced by the equations of state used; the real gas model predicts a temperature reduction in the bubble interior up to 41% space-averaged and 50% locally during the collapse phase compared to the predictions obtained with the aid of the widely used ideal gas approximation.

An on-site temperature prediction method for passive thermal management of high-temperature logging apparatus

In the downhole oil and gas industry, temperature prediction is an important means to avoid the hazards brought by the high-temperature environment to electronic devices. An improved adaptive Kalman filter (IAKF) temperature prediction method, used here as a virtual sensor, can predict the instrument temperature in real time. It uses the temperature state transfer matrix as a system adaptive discriminant parameter to improve the prediction accuracy of the model. This approach is a data-driven prediction method for practical, field-deployable application, it does not require modeling of heat transfer mechanisms and does not require data sets. Experiments results show that the IAKF model can effectively predict the changing trend of the apparatus in the next 30 min, and the maximum temperature prediction error is within 6.5°C. Its predictions are more stable and accurate than the extended Kalman filter, and it consumes very little CPU resources to run in embedded devices.

Design of a new optimized U-shaped lightweight liquid-cooled battery thermal management system for electric vehicles: A machine learning approach

Despite significant advances in battery thermal management system (BTMS), current electric vehicles (EVs) still lag behind user expectations in terms of battery performance and safety. A reliable, low-cost, and lightweight system is crucial for an effective BTMS that enables the battery's operation within a reasonable temperature range. In this study, a novel U-shaped lightweight liquid cooling method is introduced to optimize the thermal safety and weight of a prismatic battery cell. Numerical calculations are first carried out to investigate the effects of different fluid flow directions, flow rates, channel dimensions, fluid media, and discharge rates on the battery's temperature, and the results reveal that these factors simultaneously influence both the maximum temperature and temperature uniformity. To save computational costs and avoid the overwhelming complexity of the numerical measurements, a neural network-based regression model was constructed on the basis of 125 sets of simulation data, which comprised of four kernel functions with two outputs: maximum temperature and temperature difference. Each kernel function had a desirable testing accuracy (99.1%, 97.0%, 97.8%, 98.4%) after training, which indicates that the Gaussian Process Regression (GPR) with Matern 5/2 kernel function can better express complex non-linear relations for the prediction of maximum temperature and temperature difference with minimal errors between the simulated and predicted values compared with other kernel functions. Additionally, applying the GPR model to all cases studied, it is observed that Case 1 decreases the maximum temperature of the battery cell by 21% and lowers the weight of the cooling plate by 45% compared to the conventional liquid-cooled BTMS. Hence, it is chosen as the optimal design. The newly introduced BTMS can contribute to the development of lightweight and thermally safe battery cells.

TEM Microstructure, Mechanical Properties and Temperature Estimation in the 5XXX Series Al-Mg-Si Aluminum Alloy with W-Ni-Fe Tungsten Composite Friction-Welded Joints.

The temperature distributions, microstructure, and mechanical properties of tungsten composite with aluminum alloy friction-welded joints are presented in this paper. The effects of welding parameters on flash diameter, shortening, joint efficiency, microhardness, and microstructure were studied. Empirical temperature models for heating and cooling phases are proposed in this study. The predicted maximum temperatures at the periphery and in the axis of aluminum specimens were close to 550 °C and 480 °C at the interface, respectively. Moreover, the peak temperature in the weld zone was studied analytically. A maximum tensile strength of 234 MPa was reached for the following welding parameters: friction time of 3.5 s and friction force of 12.5 kN. The efficiency of the welded samples decreased after reaching the maximum value, with an increase of friction time and force. Maximum hardness at the interface and the half-radius reached 100 HV and 80 HV in the aluminum alloy joints, respectively. Dynamic recrystallisation areas on the aluminum alloy side were observed. Transmission electron microscopy observations of the microstructure in the aluminum alloy revealed the presence of a high dislocation density compared to the parent material.

Evaluation of heat transfer within numerical models of resistance spot welding using high-speed thermography

The automotive industry is demanding more sophisticated resistance spot welding (RSW) models with an emphasis on integrated computational materials engineering (ICME) to better design welded structures. RSW process models are often validated using experimental weld nugget diameter measurements that verify the maximum temperature prediction but not the entire transient temperature response. The accuracy of temperature data on-cooling is essential to reduce error in integrated process-material models during the simulation of microstructural evolution. In this study, two existing 3D finite element (FE) models of RSW (general-purpose and RSW-dedicated) that were previously optimized for 1.5 mm thick Usibor®1500 steel are conventionally validated using experimental nugget diameter and electrode indentation measurements and then modified to represent half-section RSW. The half-section RSW simulation results are directly compared to experimental temperature data captured using high-speed thermography (HST). Analysis of HST showed that the thermal history and t8/5 predicted by RSW-dedicated model were more consistent with the HST data, but the general-purpose model more accurately predicted half-section RSW nugget diameter by 8 % and electrode indentation by 11 % as a function of welding current. The models’ material properties, specifically workpiece thermal conductivity and specific heat as a function of temperature, had a significant effect on the RSW model temperature fields on-cooling. Overall, HST of half-section RSW provided a quantitative comparison of experimental and simulated temperature results and was a viable technique to validate the thermal history outputted by RSW FE models.

Raman Spectral Shifts in Naturally Faulted Rocks

AbstractRaman spectral shifts of carbonaceous materials arising from faulted rocks produce varied and complex results. By analyzing faulted and adjacent non‐faulted samples from three discrete localities in France, Italy, and Morocco, we assess changes in specific Raman parameters, including G‐peak width, G‐peak position, and peak intensity ratios. We consistently observe a shift in Raman parameters between faulted and non‐faulted samples; however, the direction and magnitude of this shift varies. Raman peak intensity ratios are shown to both increase and decrease on fault planes. The majority of samples exhibit decreasing peak width and peak position in the faulted samples, but this is not consistent; in two samples an increase is observed, but the shift tends to be small. These data are compared to published Raman spectral shifts from experimental fault data. Our results suggest that fault zone deformation processes may measurably change the carbon nanostructure in faulted rocks. The inconsistent nature of Raman spectral shifts in the fault rock samples analyzed, and those published suggest that a complex set of factors control carbon nanostructure changes in fault rocks. These factors, although not discriminated here, likely include, background temperature, frictional heating, strain, carbonaceous material type, amongst others. We have shown that Raman Spectral shifts occur in faulted rocks with implications for how Raman data are used to predict maximum temperatures in faulted sedimentary rocks. We recognize the potential for a range of fault zone processes to modify Raman spectral shifts, however, systematic sampling across individual fault zones is critical.

Pitch oil production – An intangible cultural heritage in Central Europe

Pitch oil production from Scots pine (Pinus sylvestris L.) resinous wood is an intangible cultural heritage in the Central European region including the Czech Republic, Northern Austria and South Eastern Germany, and is related to traditions in Finland and Southern France. The heating of wood in small kilns also fueled by wood produces liquid for collection. Our detailed investigations of three pitch oil kilns in Upper Austria led to the discovery of relationships to the production of pine and birch tar in Fennoscandia and black pine tar production in Western Anatolia. Our measurements of temperatures at the bases of the kilns revealed a slow temperature rise. We predicted maximum temperatures based on infrared spectra, and measured overall temperatures in the range of 250 °C–650 °C. With gas chromatographic analyses of pitch oil we detected a dominant proportion of resin components and only a minor proportion of compounds attributable to pyrolytic transformation of solid wood mass. In most pitch oil samples the extractives comprised 90 %. Most samples were similar and only the first samples at the starting outflow were systematically dominated by pyrolysis products. Tar runoff from a traditional circular charcoal kiln for charcoal production – used as a reference – had a strongly different composition, with a high proportion of pyrolysis compounds.

A modified uniformly distributed heat source method for predicting braking temperature of railway brake disc

ABSTRACT The current numerical method can only obtain the average temperature of the railway brake disc, which greatly reduces the predicting accuracy. Based on the uniformly distributed heat source method (UDHS Method), a more accurate and efficient method was proposed to predict the braking temperature by using a cosine function. This numerical method is valid as its results are in good agreement with the measurements from the full-scale rig test. After modification, the errors of the predicted maximum temperature are decreased from 5.73–6.72% to 3.47–5.07% at 40–100 km/h, and the temperature curves oscillate with a cosine form whose frequency is perfectly match with the curves from the thermal mechanical coupling method (TMC Method). The computational time of TMC Method is 74.19–665.61 times as much as that of UDHS Method, while the ratio of the modified method is reduced to 2.76–12.95 times.

Computational based on GUI MATLAB for back propagation method in detecting climate change: case study of mataram city

Climate change is a global phenomenon in which its impact can be perceived locally. In the face of this phenomenon, it is necessary to prepare and overcome this threat by developing various mitigation and adaptation strategies for climate change. To support this strategy, climate data such as temperature (maximum and minimum), rainfall, number of rainy days, as well as necessary information in the future need to be detected to prepare good and directional policies. The study aims to determine the climate parameters in Mataram city using forecasting by constructing the algorithm the Back Propagation method. In using the Matlab GUI application by analyzing climate change in Mataram city by using monthly data for the last 10 years. In the simulation stage, the data taken are the maximum temperature, minimum temperature, rainfall, and the number of rainy days in Mataram city. Based on the results of the training and testing process for climate data in the city of Mataram with a prediction of maximum temperature occurred in November at 32,580C and maximum rainfall occurred in December as much as 399.2931 mm, while for the maximum number of rainy days occurred in January with a total of 21.99 days. These results can be used as an alternative policy in the handling and preparation of extreme climate change.