Prediction Of Temperature Distribution Research Articles

Fast prediction of temperature distributions in oil natural air natural transformers using proper orthogonal decomposition reduced‐order data‐driven modelling

Fast prediction of temperature distributions in oil natural air natural transformers using proper orthogonal decomposition reduced‐order data‐driven modelling

Target temperature-based simulation method for predicting performance and designing aircraft thermal anti-icing systems

The prevention of ice build-up on aircraft surfaces is crucial for safe operations. Thermal ice protection systems, particularly electro-thermal systems, are commonly employed to mitigate ice formation. However, the weight of electric batteries used in these systems significantly impacts aircraft performance, necessitating the minimization of power load during design. Despite this requirement, effective anti-icing operation mandates a sufficient heat flux to maintain surface temperature above the freezing point and evaporate any runback water film. Previous research has utilized numerical simulation analysis to predict the performance of electro-thermal anti-icing systems. The conjugate heat transfer method has been commonly employed to predict temperature distribution on the protected surface. However, non-isothermal surfaces exhibit distinct local convective heat transfer coefficients that influence local thermal balance, unlike isothermal surfaces that adopt the loosely-coupled method for computational efficiency. In this study, we present a novel target temperature-based anti-icing simulation method that predicts the required heat flux for thermal ice protection systems without employing the conjugate heat transfer method. Our objective was to calculate the necessary heat flux to achieve the desired non-isothermal surface temperature for effective anti-icing. By eliminating the excessive iteration process associated with the coupling method for conjugate heat transfer, we have streamlined the simulation procedure. Through validation, target temperature-based anti-icing simulation method, when compared to the tight coupling method, demonstrated the ability to predict the same surface water flow rates while reducing computation time by up to 90 %. Furthermore, when applied to design problems, our method achieved approximately a 10 % reduction in the required heat capacity for anti-icing compared to experimental values. As a result, target temperature-based anti-icing simulation method offers a more efficient alternative to traditional conjugate heat transfer methods, providing valuable insights for both predicting anti-icing system performance and optimizing aircraft design.

Implementation of Numerical Model for Prediction of Temperature Distribution for Metallic-Coated Firefighter Protective Clothing

The aim of this study is to predict the distribution of temperature at various positions on silver-coated firefighter protective clothing when subjected to external radiant heat flux. This will be helpful in the determination of thermal protective performance. Firefighter clothing consists of three layers, i.e., the outer shell, moisture barrier and thermal liner. The outer shell is the exposed surface, which was coated with silver particles through a physical vapor deposition process called magnetron sputtering. Afterwards, these uncoated and silver-coated samples were exposed to radiant heat transmission equipment at 10 kW/m2 as per the ISO 6942 standard. Silver-coated samples displayed better thermal protective performance as the rate of temperature rise in silver-coated samples slowed. Later, a numerical approach was employed, contemplating the impact of metallic coating on the exterior shell. The finite difference method was utilized for solving partial differential equations and the implicit method was employed to discretize the partial differential equations. The numerical model displayed a good prediction of the distribution of temperature at different nodes with respect to time. The comparison of time vs. temperature graphs at different nodes for uncoated and silver-coated samples acquired from numerical solutions showed similar patterns, as witnessed in the experimental results.

Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method

Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predicting in vivo brain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy. Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT. Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations. Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction. Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.

Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection

Diverse and complex fire environment in modern utility tunnels with multiple uncertainties make fire detection difficult to be achieved accurately. This study aims to develop an intelligent fire detection technique to address the difficulty. In the technique, initially, a lightweight image segmentation method is proposed for initial estimation of the fire source location. Then, the multi-source heterogeneous data fusion fire detection is implemented for fire source localization and ceiling temperature distribution prediction based on Gauss model and the improved multi-particle swarm optimization (MPSO) algorithm. Additionally, the results of the case study support the ability of the intelligent fire detection technique through compared with the experiment results and the previous methods, which can be used to achieve precise and stable fire source localization and ceiling temperature prediction in the utility tunnel fire.

Heat transfer coefficient simulation and temperature distribution prediction of wire loops in the Stelmor air‐cooling system

AbstractDuring the Stelmor air‐cooling process, the temperature distribution has a significant impact on the wire loops' final mechanical properties. The temperature distribution during the air‐cooling process is accurately solved by establishing three‐dimensional model and numerical simulations. The heat transfer coefficient at the highly dense region is much smaller than that of wire loops at the low dense region, and changes periodically over time, according to a computational fluid dynamics simulation method. It is also found that the heat transfer coefficient on the cross‐section of the wire loop is very different, with a difference of 70–100 W/m2 K. Finally, the finite difference method is used to calculate the mathematical model of the temperature distribution during the Stelmor air‐cooling process. Comparing the results with the measurement data, the simulation results and measurement data match up well.

Numerical simulation of an industrial radiant tube burner using OpenFOAM

This study presents a numerical investigation into the effects of physical models on the prediction accuracy of the wall temperature distribution in an industrial radiant tube burner. Utilizing a reacting flow solver based on OpenFOAM, we explored the effects of various physical models, including those for chemistry, combustion, heat transfer, and radiation properties. The choice of combustion model significantly influences prediction accuracy, playing a more dominant role than the chemistry mechanism. Moreover, the simulations captured a distinctive triple flame structure inside the burner, representing the coexistence of rich premixed, non-premixed, and lean premixed flame structures. Conditional scatter plots displayed the development of both premixed and non-premixed flame structures, converging on the fuel-lean side. Notably, accurate prediction of wall temperature distribution depends on the incorporation of a precise heat transfer model, coupled with a detailed radiation property model. Regarding the distribution of tube surface temperature in the main radiation zone (a distance from the burner nozzle greater than 1 m), the most accurate prediction exhibits a maximum deviation of less than 56 K and an average deviation of 24 K compared to experimental results. The simulation closely matched experimental data for exhaust concentration of NO within an error margin of 20 ppm. However, discrepancy was observed in the CO concentration, which was attributed to the simplified representations of fuel chemistry and composition, as well as the difficulties in accurately capturing the unsteady flame dynamics near the wall.

Experimental and numerical investigation on optimization of welding parameters and prediction of temperature distribution during shielded metal arc welding of ultra high hard armor steel joints

In this investigation, Ultra-high Hard Armor (UHA) steel plate by Shielded Metal Arc Welding (SMAW) process using the Austenitic Stainless Steel (ASS) electrode. In the first part of this investigation, using the finite element method (FEM), thermal analysis parameters were optimized using bead on plate welds in software code, known as SYSWELD. In the second part, using optimized parameters, a coupled thermo-mechanical analysis on multipass welded fabrication was carried out. Weld thermal cycles were analyzed by simulation and compared with experimentally measured temperature profiles. A double ellipsoidal heat source model was used to simulate the SMAW process. The heating rate and cooling rate were calculated along with residual stresses. This range of heating and cooling rate resulted in untempered martensite in the heat-affected zone (HAZ), confirmed by microstructure analysis and hardness mapping. Good validation was found between the measured and simulated 2D model with a 4–10% variation. This study suggests that numerical simulation is the capable technique in understanding the thermal and mechanical properties that influence the welding of ultra-high hard armor steels joints.

Transport phenomena in pasta drying: a dough-air double domain advanced modeling

In the realm of pasta drying, where precise prediction of temperature and water-content distribution is crucial, the application of modeling and simulation emerges as highly significant. This study endeavors to evaluate operational conditions ensuring the production of a safe, high-quality pasta product. An advanced two-domain model was devised and solved to estimate transport phenomena in both the food and air domains. Unlike conventional models relying on specified interfacial heat and mass transfer coefficients, the proposed model serves as a versatile tool applicable across a broad spectrum of process and fluid-dynamic conditions in real pasta driers. The system of non-linear unsteady-state partial differential equations governing the behavior of a “Rigatone" pasta sample in a drier was solved using the finite elements method to estimate how air characteristics influence drying performance. A pilot-scale drying chamber was used for model validation for inlet air temperature Ta,in=80°C at relative humidity values (RHin) of 50 % and 60 %, with error consistently below 10 %.

Multimodal machine learning for predicting heat transfer characteristics in micro-pin fin heat sinks

As three-dimensional integrated circuit (3D-IC) chip technology advances, thermal management has become increasingly important because of increasing heat flux from thermal stacking. Micro-pin fin-embedded cooling has emerged as a promising solution for 3D-ICs, offering better thermal and hydraulic performance than conventional microchannel heat sinks. It is also easy to integrate into existing 3D-IC structures, such as through-silicon vias between stacks. The utilization of two-phase flow in micro-pin fins further enhances temperature uniformity and improves the heat transfer coefficient by leveraging latent heat. Nevertheless, predicting thermal performance in micro-pin fin heat sinks under boiling conditions remains challenging owing to intricate geometric shapes and diverse operating conditions. The present lack of correlation or theoretical models poses a significant obstacle. To address this problem, our study employed a Multimodal machine-learning (ML) approach, combining image data capturing boiling patterns of two-phase flow and information about geometric shape and operating conditions, to predict heat transfer characteristics in micro-pin fin heat sinks. We utilized experimental data comprising 155 types of boiling heat transfer data with the dielectric fluid FC-72 in two micro-fin shapes directly etched on Si. Four ML algorithms (XGBoost, LightGBM, Multilayer perceptron (MLP), and Multimodal ML) were employed to predict thermal performance. The correlation coefficient analysis before learning revealed the influence of each type of measurement data on the heater surface temperature during two-phase flow. Prediction accuracy was measured using mean absolute percent error (MAPE), and the results were compared in terms of maximum and average temperature depending on the characteristics of each ML algorithm. Overall, the Multimodal approach demonstrated superior capability in predicting temperature distributions with spatial details, surpassing conventional decision-tree algorithms and MLP in performance. When trained with boiling images, the Multimodal ML model achieved remarkable precision, evidenced by a MAPE of 1.81% for the maximum temperature and 0.84% for the average temperature, highlighting its exceptional accuracy in mapping the heated surface temperature profile. By contrast, the traditional MLP model, which lacked training on boiling images, showed diminished accuracy, with a MAPE of 2.54% for the maximum temperature and 1.77% for the average temperature, indicating a comparative shortfall against the Multimodal model results.

Enhancement of skin tumor laser hyperthermia with Ytterbium nanoparticles: numerical simulation

Laser hyperthermia therapy (HT) has emerged as a well-established method for treating cancer, yet it poses unique challenges in comprehending heat transfer dynamics within both healthy and cancerous tissues due to their intricate nature. This study investigates laser HT therapy as a promising avenue for addressing skin cancer. Employing two distinct near-infrared (NIR) laser beams at 980 nm, we analyze temperature variations within tumors, employing Pennes’ bioheat transfer equation as our fundamental investigative framework. Furthermore, our study delves into the influence of Ytterbium nanoparticles (YbNPs) on predicting temperature distributions in healthy and cancerous skin tissues. Our findings reveal that the application of YbNPs using a Gaussian beam shape results in a notable maximum temperature increase of 5 °C within the tumor compared to nanoparticle-free heating. Similarly, utilizing a flat top beam alongside YbNPs induces a temperature rise of 3 °C. While this research provides valuable insights into utilizing YbNPs with a Gaussian laser beam configuration for skin cancer treatment, a more thorough understanding could be attained through additional details on experimental parameters such as setup, exposure duration, and specific implications for skin cancer therapy.

Insight into the influence of frictional heat on the modal characteristics and interface temperature of frictionally damped turbine blades

The reciprocating relative motions of friction pairs introduce nonlinearities into the host structure, while also generating frictional heat, leading to a noticeable temperature rise at the contact interface. Consequently, the contact mechanics properties are not invariant but highly dependent on temperature. To elucidate the thermo-mechanical interaction mechanism in dry friction systems, we propose a novel multi-physical nonlinear modal analysis method called Thermo-Mechanical damped Nonlinear Normal Mode (TM-dNNM). This numerical method enables the determination of nonlinear modal characteristics considering thermo-mechanical coupling and the prediction of detailed temperature distribution on contact interface. The efficiency and convergence of the TM-dNNM are ensured through three key techniques: multi-physics reduced order modeling, analytical Jacobian matrix, and scaling adjustment. We apply the proposed numerical method to a typical turbine blade with a flat underplatform damper for engineering purposes. Results indicate that frictional heat significantly affects the nonlinear modal characteristics, exhibiting complex behavior such as softening/stiffening reversal and resonance amplitude backtracking. Neglecting the thermo-mechanical coupling can lead to a deviation of more than 13 Hz in the predicted modal frequency and an overestimation of the modal damping ratio by a factor of three. Interface temperature rises considerably at higher vibration levels, with the hotspot located near the lower left corner of the contact surface, where the average temperature exceeds 1180 ℃, approaching the melting point. By employing the TM-dNNM, designers can predict critical resonance amplitudes and identify potential local hotspots, thus helping prevent excessive temperature at the interface of frictionally damped turbine blades.

PREDICTION OF TEMPERATURE DISTRTBUTION INFLEXIBLE PAVEMENTS

In the present study, finite difference method is proposed to use for the development of Mathematical model for the prediction of the pavement temperature distribution (PPTD) in flexible pavement.To simpli$ the mathematical calculation, Visual Basic language was selected to perform a progam that has a capability fc.: the Prediction of Pavement Temperature as a function of depth and time. To test its accuracy, the program was applied to predict the pavement temperature distribution on specific location in section (R12) from the Expressway No.l in Iraq. The output of this program appears to be in good agreement with the (R12) observed data. Therefore the program can be recognized for use in the prediction of the asphaltic pavement temperature distribution

Fast prediction of spatial temperature distributions in urban areas with WRF and temporal fusion transformers

Urban Heat Island (UHI) poses a significant challenge to the sustainable development of global cities. It is of great importance to efficiently characterize the spatiotemporal distribution of urban temperatures for UHI mitigation strategies, such as urban ecosystem planning and control. Numerical Weather Prediction (NWP) methods are used to obtain the urban temperature distribution. However, NWP requires significant hardware resources and long computation time. The development of artificial intelligence approaches have been applied in expediting the weather forecasting, yet their forecasting precision remains significantly inferior to that of NWP. Hence, this study aims to propose a hybrid fast prediction model, considering the accuracy of WRF (Weather Research and Forecasting) and efficiency of Temporal Fusion Transformer (TFT) neural networks. By integrating high-precision temperature time series boundaries generated by WRF into TFT, this method (WRF-TFT) is able to realize the rapid predictions of urban temperature distributions (around 15 times faster compare to WRF) while maintaining the physical characteristics of atmospheric dynamics. With this method, we also conducted for future temperature forecast for cities. It is estimated that the temperature can exceed 35 °C more than 12 hours per day in July 2050. This hybrid model facilitates swift acquisition of urban temperature trends, providing a crucial basis for urban risk management and planning.

Numerical simulation of the welding process for the prediction of temperature distribution on Al/steel explosion welded joint

Joining steel and aluminum alloys is a common problem in many engineering structures. One of the alternatives for combining these materials is welding them using explosively welded transition joints. The biggest problem is the difference in welding temperatures of steel and aluminum alloys. The higher welding temperature of steel means there is a high risk of overheating the aluminum alloy in the transition joint. One of the methods of optimizing the dimensions of an explosively welded connector is to conduct experimental tests. These tests can indicate the minimum size of the connector in which the steel welding process does not cause changes in the microstructure and strength of the aluminum alloy. Conducting experimental research is time-consuming and expensive. First, it is necessary to produce explosively welded plates. Secondly, specimens of welded joints must be prepared. In order to minimize costs and speed up the design process, the number of experimental tests can be reduced by conducting numerical analyses. The aim of this work is to describe a method for modeling the distribution of temperature fields during welding of steel and aluminum alloy joints using transition joints. The method of manufacturing joint specimens and the method of experimental measurement of temperature during welding are described. The results compare the temperature distributions in an explosively welded connector during steel welding, determined experimentally and using numerical methods.

Physics-Informed Fully Convolutional Networks for Forward Prediction of Temperature Field and Inverse Estimation of Thermal Diffusivity

Abstract Physics-informed neural networks (PINNs) are a novel approach to solving partial differential equations (PDEs) through deep learning. They offer a unified manner for solving forward and inverse problems, which is beneficial for various engineering problems, including heat transfer analysis. However, traditional PINNs suffer from low accuracy and efficiency due to the fully-connected neural network framework and the method to incorporate physical laws. In this paper, a novel physics-informed learning architecture, named physics-informed fully convolutional networks (PIFCNs), is developed to simultaneously solve forward and inverse problems in thermal conduction. The use of fully convolutional networks significantly reduces the density of connections. Thus, the computational cost is reduced. With the advantage of the nodal-level match between inputs and outputs in FCNs, the output solution can be used directly to formulate discretized PDEs via a finite difference method, which is more accurate and efficient than the traditional approach in PINNs. The results demonstrate that PIFCNs can flexibly implement Dirichlet and Neumann boundary conditions to predict temperature distribution. Remarkably, PIFCNs can also estimate unknown thermal diffusivity with an accuracy exceeding 99%, even with incomplete boundaries and limited sampling data. The results obtained from PIFCNs outperform those obtained from PINNs.

Continuum modeling of high-temperature (>1000 °C) heat extraction from a moving-bed oxidation reactor for thermochemical energy storage

There is a growing need for efficient and reliable energy storage technologies to expand renewable energy adoption and transit to a decarbonized clean energy future. Thermochemical energy storage (TCES) through reversible gas-solid reactions has exhibited considerable potential. Up to now, TCES has primarily been studied at the material level and within lab-scale reactors. As such, accurate numerical models are needed for further reactor design and scale-up. In this work, a transient three-dimensional heat and mass transfer continuum model was developed to simulate the transport phenomena coupled with exothermic oxidation of magnesium‑manganese-oxide particles in a high-temperature moving-bed reactor for TCES. The reactor included direct heat extraction at temperatures >1000 °C. The predicted temperature distributions, oxygen concentration profiles, and steady-state reactor energy extraction efficiency were in good agreement with measured values from a corresponding experimental setup. The performance of the reactor was further investigated through two parametric studies (varied particle flow rates and gas extraction ratios), which demonstrated a strong dependence of reactor efficiency (varied between 15 %–40 %) on both operating parameters. The present heat and mass transfer model will be valuable in further reactor design and scale-up studies, as well as for selection of operating conditions to maximize efficiency.

Linear modeling analysis of the heat balance of the transmission line in high frequency critical ice melting state

Abstract The icing of transmission lines can cause many problems such as increased line load, unbalanced tension, and galloping, posing a serious threat to the reliable operation of the power system. Therefore, linear modeling analysis of the heat balance under a high-frequency critical ice melting state of transmission lines is studied. The thermal conduction states of melting, heating, twisting, rotating melting, and ice layer detachment of ice melting conductors are analyzed. Based on the heat conduction process of thermal melting of ice on transmission lines, a simplified calculation formula for thermal melting ice time is derived to calculate times for heating, ice layer torsion, and air gap increase. At the same time, based on the law of conservation of energy, thermodynamic boundary conditions for iced conductors were established and corresponding physical models were constructed. Based on this model, we conduct a linear modeling of the heat balance in the melting state in order to provide a more accurate prediction of temperature distribution. The experimental results show that there is a negative correlation between environmental temperature and critical melting current, and the melting time decreases with the increase of environmental temperature.

A Semi-Analytic Model for Temperature Distribution Prediction and Hotspot Tracking in SPMSM and Its Application for Improved Multiphysics Multiobjective Optimization Design

A Semi-Analytic Model for Temperature Distribution Prediction and Hotspot Tracking in SPMSM and Its Application for Improved Multiphysics Multiobjective Optimization Design

A Numerical Approach for Predicting Temperature Distribution, Heat Flow, and Material Deformation of Printed Circuit Board during Laser Soldering

This paper presents a numerical simulation approach using the finite element method (FEM) in ANSYS software to overcome the limited accessibility of laser soldering machines which are expensive. The study aims to examine the viability of simulating a laser soldering process using ANSYS steady-state thermal simulation. The simulation aims to produce accurate predictions for temperature distribution, heat flow, and deformation of the material during the process. A Gaussian laser beam equation is used to analyse the interaction between the laser beam and metallic components, particularly on PCB boards. This paper evaluates the properties of a laser beam and the Sn-3.0Ag-0.5Cu (SAC305) lead-free solders which are commonly used in electronic industries. As a result of the thermal analysis, the temperature distribution within the solder spots, as well as the copper as printed circuit board (PCB) can be determined, whereas the mechanical analysis of the materials can be determined by looking at their deformation and stress in the system. The results show that the laser beam effectively contains heat within the solder contacts, reducing the risk of excessive heat transfer. The copper acts as an efficient heat sink, managing thermal energy during the process. However, the solder point experiences substantial deformation and stress, indicating potential limitations in its performance. Nonetheless, the neighbouring components and solder joints exhibit a factor of safety, ensuring the overall safety and reliability of the system.