Estimation Of Temperature Distribution Research Articles

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Precision Calibration in Wire-Arc-Directed Energy Deposition Simulations Using a Machine-Learning-Based Multi-Fidelity Model

Thermal simulation is essential in wire-arc-directed energy deposition (W-DED) to accurately estimate temperature distributions, impacting residual stress and distortion in components. Proper calibration of simulation models minimizes inaccuracies caused by varying material properties, machine settings, and environmental conditions. The lack of standardized calibration methods further complicates thermal predictions. This paper introduces a novel calibration method integrating both machine learning, as the high-fidelity (HF) model, and response surface modeling, as the low-fidelity (LF) model, within a multi-fidelity (MF) framework. The approach utilizes Bayesian optimization to effectively explore the search space for optimal solutions. A two-tiered model employs the LF model to identify feasible regions, followed by the HF model to refine calibration parameters, such as thermal efficiency (η), convection coefficient (h), and emissivity (ε), which are difficult to determine experimentally. A three-factor Box–Behnken design (BBD) is applied to explore the design space, requiring only thirteen parameter configurations, conserving resources and enabling robust model training. The efficacy of this MF model is demonstrated in multi-layer W-DED calibration, showing strong alignment between experimental and simulated temperatures, with a mean absolute error (MAE) of 7.47 °C. This method offers a replicable framework for broader additive manufacturing processes.

Thermal Analysis of Parabolic and Fresnel Linear Solar Collectors Using Compressed Gases as Heat Transfer Fluid in CSP Plants

This study introduces the use of compressed air as a heat transfer fluid in small-scale, concentrated linear solar collector technology, evaluating its possible advantages over traditional fluids. This work assumes the adoption of readily available components for both linear parabolic trough and Fresnel collectors and the coupling of the solar field with Brayton cycles for power generation. The aim is to provide a theoretical analysis of the applicability of this novel solar plant configuration for small-scale electricity generation. Firstly, a lumped thermal model was developed in a MatLab® (v. 2023a) environment to assess the thermal performance of a PT collector with an evacuated receiver tube. This model was then modified to describe the performance of a Fresnel collector. The resulting optical–thermal model was validated through literature data and appears to provide realistic estimates of temperature distribution along the entire collector length, including both the receiver tube surface and the Fresnel collector’s secondary concentrator. The analysis shows a high thermal efficiency for both Fresnel and parabolic collectors, with average values above 0.9 (in different wind conditions). Th5s study also shows that the glass covering of the Fresnel evacuated receiver, under the conditions considered (solar field outlet temperature: 550 °C), reaches significant temperatures (above 300 °C). Furthermore, due to the presence of the secondary reflector, the temperature difference between the upper and the lower part of the glass envelope can be very high, well above 100 °C in the final part of the collector string. Differently, in the case of PTs, this temperature difference is quite limited (below 30 °C).

Occurrence of Wetness on the Fruit Surface Modeled Using Spatio-Temporal Temperature Data from Sweet Cherry Tree Canopies

Typically, fruit cracking in sweet cherry is associated with the occurrence of free water at the fruit surface level due to direct (rain and fog) and indirect (cold exposure and dew) mechanisms. Recent advances in close range remote sensing have enabled the monitoring of the temperature distribution with high spatial resolution based on light detection and ranging (LiDAR) and thermal imaging. The fusion of LiDAR-derived geometric 3D point clouds and merged thermal data provides spatially resolved temperature data at the fruit level as LiDAR 4D point clouds. This paper aimed to investigate the thermal behavior of sweet cherry canopies using this new method with emphasis on the surface temperature of fruit around the dew point. Sweet cherry trees were stored in a cold chamber (6 °C) and subsequently scanned at different time intervals at room temperature. A total of 62 sweet cherry LiDAR 4D point clouds were identified. The estimated temperature distribution was validated by means of manual reference readings (n = 40), where average R2 values of 0.70 and 0.94 were found for ideal and real scenarios, respectively. The canopy density was estimated using the ratio of the number of LiDAR points of fruit related to the canopy. The occurrence of wetness on the surface of sweet cherry was visually assessed and compared to an estimated dew point (Ydew) index. At mean Ydew of 1.17, no wetness was observed on the fruit surface. The canopy density ratio had a marginal impact on the thermal kinetics and the occurrence of wetness on the surface of sweet cherry in the slender spindle tree architecture. The modelling of fruit surface wetness based on estimated fruit temperature distribution can support ecophysiological studies on tree architectures considering resilience against climate change and in studies on physiological disorders of fruit.

An artificial intelligence approach for the estimation of conduction heat transfer using deep neural networks

PurposeThis study aims to use deep neural networks (DNNs) to learn the conduction heat transfer physics and estimate temperature distribution images in a physical domain without using any physical model or mathematical governing equation.Design/methodology/approachTwo novel DNNs capable of learning the conduction heat transfer physics were defined. The first DNN (U-Net autoencoder residual network [UARN]) was designed to extract local and global features simultaneously. In the second DNN, a conditional generative adversarial network (CGAN) was used to enhance the accuracy of UARN, which is referred to as CGUARN. Then, novel loss functions, introduced based on outlier errors, were used to train the DNNs.FindingsA UARN neural network could learn the physics of heat transfer. Within a few epochs, it reached mean and outlier errors that other DNNs could never reach after many epochs. The composite outlier-mean error as a loss function showed excellent performance in training DNNs for physical images. A UARN could excellently capture local and global features of conduction heat transfer, whereas the composite error could accurately guide DNN to extract high-level information by estimating temperature distribution images.Originality/valueThis study offers a unique approach to estimating physical information, moving from traditional mathematical and physical models to machine learning approaches. Developing novel DNNs and loss functions has shown promising results, opening up new avenues in heat transfer physics and potentially other fields.

Estimation of Temperature Distribution in the Brain Using the Temperatures of Blood Inflow and Outflow for Brain Temperature Control

Brain cells can be damaged by higher temperature in the brain, and brain temperature distribution monitoring system is required from the perspective of brain preservation. Brain tissue can be further damaged by direct measurement of temperature using some sensors. Therefore, indirect methods of measuring temperature are required. In this study, we propose a method for estimating the average metabolic rate of the entire brain based on the difference between the observed and estimated blood flow temperatures that can be measured during selective brain hypothermia. Our mathematical simulator can calculate the brain temperature distribution based on the estimated average metabolic rate, taking into account other parameters like heat transfer, metabolic heat of individual tissues, heat‐washout by blood flow, and environmental temperature. The algorithm for estimating the metabolic rate of mathematical master model substituted as a human head is confirmed using the mathematical slave model on the simulator. It is possible to present the temperature distribution by estimating metabolic heat production in the brain, considering the equilibrium between heat wash‐out by the blood flow and heat transfer. In addition, an appropriate initial value of brain temperature enables the accurate temperature management enough for clinical use even in the process of temperature estimation. The development of our proposed method will make it possible to construct a non‐invasive monitoring system that presents the estimated temperature distribution in the brain instead of the conventional invasive method. The new system is expected to be utilized in a various treatments related to some brain disorders. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Factors Affecting Soil Bulk Density: A Conceptual Model

Soil bulk density is an important aspect of soil properties, and it can affect agroecology as in diversity, synergies, efficiency, recycling, and resilience. Diversity as in soil low in bulk density allows greater penetration of plant roots allow better access of water and nutrients. The synergistic effect of adequate supply of soil moisture content in forming moderately low soil bulk density for deeper plant roots for better access to water and nutrients. Agroecology efficiency reduces the need of external inputs to reduce environmental impact related to the resulted excessive pollution and wastage. Ultimately, an improved soil bulk density may increase the overall agroecosystem resilience and output, thus, improving the efficiency. Recycling in agroecology involved mass recycling and heat circulation as in nutrient, water, and waste reduction. A high soil bulk density will impede the recycling process the soil. Resilience as in drought resistance is for soil not too loosely packed that cannot hold water or too compacted until flooding water occurs at the surface and too little water infiltrates the soil. In addition, soil bulk density can affect the estimation of soil moisture content and temperature distribution. The ability to understand the factors that influence changes in soil bulk density would improve the predictive ability of the current model. The current study conducts a review on factors affecting the soil bulk density that is divided into soil physical, chemical, biological, environmental, and management practices. A cumulative of more than 50 factors are discussed in the review on how it affects the change in soil bulk density. The current review illustrates the conceptual relation of these factors on soil bulk density. Also, the result of the current work can be used to support future endeavour by turning the conceptual relation into quantifiable predictive model.

Estimating temperature distribution and heat quantity of spindle shaft for machining centers

Estimating temperature distribution and heat quantity of spindle shaft for machining centers

Brain-morphic wireless sensor network and its application to temperature distribution estimation

Brain-morphic wireless sensor network and its application to temperature distribution estimation

Prediction of the efficient heat flux to get an accurate thermal and clad geometry characteristic during laser cladding

The prediction of the accurate thermal and clad geometry characteristics plays a significant role in obtaining a better metallurgical bond between the cladding material and the substrate. The estimation of temperature distribution is difficult experimentally within the melt pool. A suitable and accurate heat flux is needed to apply during the numerical modeling to get clear and accurate process parameters before conducting the actual experiments. Therefore, different heat flux is used to get the best-suited heat flux which gives an accurate response during the laser cladding. The same processing parameters were considered during the simulation for the different heat sources. In all the cases, the maximum temperature was obtained at the center of the melt pool and decreased when moving away from the center of the melt pool in-depth and across the scanning speed direction. A similar shape of the melt pool is formed when three-dimensional volumetric Gaussian heat flux and conical heat flux are used, which gave best suited and efficient heat flux for the numerical simulation before actually conducting experiments. When egg-shaped heat flux is applied, an insufficient temperature was reached at the interface of the powder and substrate, whereas more dilution appeared for parabolic heat flux.

Monte Carlo simulation of phonon transport from ab-initio data with Nano-κ

Understanding of heat transport in nanodevices is a challenging issue for several new technologies. It requires specific modelling approaches and dedicated simulation tools. Yet the latter are not numerous, and are often adapted to some “classic” materials or restricted to simple geometries (i.e. thin films, nanowires). Looking to address this deficiency, the present work brings Nano-κ, a Python code to simulate the phonon transport in nano and micro scale devices from ab-initio phonon data. The code has personalizing capabilities that allow to simulate several geometries and materials, offering insights of temperature distribution, heat flux, thermal conductivity, and mode contribution to energy transport. In the present work, a detailed description of the physical model implementation is provided and several simulation test cases are discussed. Nano-κ provides reliable predictions of the thermal conductivity in different materials, and is able to correctly predict the relative effect of size, temperature, and surface roughness on thermal transport properties. Program summaryProgram Title: Nano-κCPC Library link to program files:https://doi.org/10.17632/tr29mhkjh8.1Developer's repository link:https://github.com/brunohs1993/NanokappaLicensing provisions: MITProgramming language: PythonNature of problem: The estimation of heat conduction at nanoscales depends on the estimation of phonon transport, since Fourier's law may not be valid in these situations. The phonon transport is described by the Boltzmann Transport Equation (BTE), that needs to be solved for each phonon mode separately, while estimating temperature distributions that depends on all modes at the same time. The phonon data can be retrieved from ab-initio simulations. Some BTE calculations have been already done through the years for simple geometries, but there is no publicly available code that allows enough flexibility to be used by the scientific community.Solution method: Some previous works [1-3] have had success in applying the Monte Carlo method to solve the BTE for phonon transport. In the more recent works, the ab-initio data is retrieved from density functional theory (DFT) simulations [4-5]. Nano-κ is a Python code built on the same theoretical basis of previous developments, but adds more flexibility so that it can be open for public research use.

Fast multilayer temperature distribution estimation for lithium-ion battery pack

Fast and accurate temperature estimation is crucial for ensuring battery packs' safety and operation performance. However, the full-scale online temperature estimation is still challenging. In this work, a novel reduced-order multi-physics model for a lithium-ion battery pack is proposed for real-time temperature distribution estimation, containing the distributed equivalent circuit model, the three-heat-source thermal model, and the flow resistance network model. The proposed model is conducted on a direct contact liquid-cooled battery pack composed of three modules connected in series. An online parameterization methodology with a closed loop observer is designed, and the key parameters can be automatically identified and corrected. The validation results suggest that the multilayer temperature distribution of cell, module, and pack levels can be commendably described under both steady and transient conditions, where the maximum error can be controlled within 2.8 °C. Besides, the temperature variation of the coolant can be estimated during the operation. The proposed model shows excellent potential in onboard temperature estimation with tens of milliseconds for each temperature update.

A High-Resolution Analytical Thermal Modeling Method of Capacitor Bank Considering Thermal Coupling and Different Cooling Modes

Thermal stress is of crucial importance to capacitor reliability. However, for a capacitor bank, on spatial scale, the complex heat transfer modes are not clearly illustrated; and on time scale, the ESR aging is neither fully discussed in current evaluation methods. These could lead to a glaring error in the prediction of temperature distribution and lifetime estimation. Therefore, this paper proposed an analytical thermal modeling method with high-resolution for the capacitor bank, considering the thermal coupling effect between individual capacitors, as well as different cooling conditions and the heat variation caused by ESR aging. Firstly, the improved thermal state-space modeling method is proposed to universally describe a multiple heat source system. Then, based on heat transfer and fluid mechanic theories, the characterization method of thermal coupling effect in different cooling modes is discussed. Furthermore, the ESR aging model is applied in the electro-thermal analysis of capacitor bank, aiming to improve the accuracy of thermal calculation in long time scale. Finally, to alleviate the uneven temperature distribution in an in-line designed capacitor bank, a staggered design method is proposed followed by a case study, where a nine-capacitor bank is presented to validate the corresponding modeling method and optimization.

Estimating the thermal conductivity of plutonic rocks from major oxide composition using machine learning

SUMMARY The accurate estimation of temperature distribution in the earth's crust and modelling of heat-related processes in geodynamics requires knowledge of the thermal conductivity of plutonic rocks. This study compiled an extensive data set of 530 representative plutonic rock samples, including thermal conductivity, major oxide composition and (for two subsets of data) modal mineralogy. For the first time, three machine learning algorithms (ML; i.e. support vector regression, random forest and extreme gradient boosting) were employed to estimate the thermal conductivity of plutonic rocks using the major oxide composition feature as input variables. The performance of these ML-based models was evaluated against a geochemically compositional model and eight mineral-driven physically based empirical mixing models. Results show that the means of predicted thermal conductivity by the ML-based models and the geochemically compositional model are not significantly different from the measured thermal conductivity at a significance level of 5 per cent. However, the ML-based models outperformed the best-performing non-ML model, the geochemically compositional model. The highest prediction accuracy was achieved by extreme gradient boosting, which reduced the mean absolute percentage error and root mean square error by more than 50 per cent. Furthermore, SiO2 is confirmed as the most important independent variable, followed by Al2O3, TiO2, CaO and K2O. The turning point observed in the thermal conductivity trend with SiO2 wt per cent may be primarily attributed to variations in mineral composition within the subgroup of igneous rock types classified based on SiO2 wt per cent. This study explores, for the first time, the use of ML algorithms to estimate the thermal conductivity of plutonic rocks from their major oxide composition.

Online temperature distribution estimation of lithium-ion battery considering non-uniform heat generation characteristics under boundary cooling

Lithium-ion batteries (LIBs) generate a lot of heat in the course of use, which will lead to a highly uneven distribution of the temperature, thus affecting the accuracy of battery modeling, parameter estimation and thermal management. In order to solve this potential threat, an on-line temperature distribution estimation method of LIB considering non-uniform heat generation under boundary cooling is proposed in this paper. Firstly, a thermal model of tab area is constructed by using lumped parameter method based on the analysis of the heat generation and heat transfer mechanism, and this tab thermal model is regarded as the first kind of boundary condition of the cell core. Secondly, a difference equation of the temperature distribution of the core area is constructed by using the heat balance method, and the boundary conditions and convection coefficient model are also deduced. Finally, a disturbance observer is designed to estimate the state and disturbance simultaneously to further improve the accuracy of temperature estimation. Additional stability analysis shows the convergence of the proposed disturbance observer. Experiments and verification demonstrate that the proposed method is effective and has better estimation performance compared with the other traditional methods under four different cycles, and the estimation error is less than 1.5 °C.

Estimation of laser light propagation in biological tissue during laser-tissue bonding using Monte Carlo simulation

Monte Carlo simulations are an elementary approach towards modeling light propagation in tissues. The detection of subsurface temperature in tissues during laser mediated therapies and microsurgeries is crucial for estimating associated thermal damage. MC simulation provides with the possibility to optimize the process. The approach has been used to model light propagation, associated energy deposition, and the temperature distribution inside the tissue. Understanding the extent of laser light transmittance and heat distribution within the tissue is crucial for minimizing damage to the adjacent biological tissues. The total photon weight absorbed estimates the total heat distribution within the volume. A three-layer heterogeneous tissue was specified consisting of only the epidermis, dermis, and the subcutaneous fat tissue. The hop, drop, and spin trail of photons depends on the optical properties of these layers. The energy deposition and temperature distribution estimation are obtained by the MC simulation method. A real-time measurement of the temperature profile was also performed. The experimental results were in close congruence with the simulation result. The simulation results show good reproducibility of the real temperature distribution. Monte Carlo method can, thereby, be used in estimation and optimization laser induced processes.

Temperature-related thermal properties of paving materials: experimental analysis and effect on thermal distribution in semi-rigid pavement

ABSTRACT Thermal properties of paving materials are essential for estimating temperature distribution in semi-rigid pavements. Literature reported that the thermal parameters varied widely and were most independent of temperature. This paper proposed an innovative test setup to determine the thermal conductivity and diffusivity of hot mix asphalt (HMA) and cement-treated base (CTB) materials. The experimental results indicated that the thermal properties of the paving materials linearly increased with surface temperature. Field temperature monitoring in semi-rigid pavement was also conducted at different depths of HMA and CTB. The heating rate of any location inside the pavement was found approximately twice the heat release. Such obtained thermal parameters and the pavement surface temperature from field monitoring served as inputs for numerical simulations based on ANSYS programme. Using temperature-related thermal properties in the modelling exhibited higher accuracy in predicting temperature distribution in the semi-rigid pavement than those independent of temperature from the literature.

Bistatic Radar Tomography of Shear Margins: Simulated Temperature and Basal Material Inversions

A significant portion of the sea level contributions of Antarctica and Greenland comes from ice streams, but the physical processes controlling ice stream width are poorly understood, especially when topographic controls are absent. Recent modeling studies have indicated that ice stream width may be controlled by elevated temperatures inside ice stream shear margins. While radio echo sounders can provide measurements of englacial water storage and subglacial conditions, existing radar-sounding techniques cannot measure temperature profiles at the scale required to test this hypothesis. We propose using a wide-angle radar survey and tomographic inversion to resolve temperature profiles, gradients, and anomalies at the scale required to study the thermophysical controls on shear margins. Recent work produced a bistatic radar system capable of obtaining the long offsets required for well-constrained inversions; however, shear-margin-specific temperature inversion techniques have not been developed for this system. In this article, we develop Newton’s method and alternating direction method of multipliers’ inversions for estimating temperature distribution and basal material across ice stream shear margins. We evaluate the performance of these inversion techniques on simulated bistatic radar-sounding data. Our results suggest that bistatic radar tomography experiments should be able to produce temperature maps on 50 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times50$ </tex-math></inline-formula> m grids with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.83~^{\circ} \text{C}~\pm ~0.084~^{\circ} \text{C}$ </tex-math></inline-formula> mean temperature error, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.58~^{\circ} \text{C}~\pm ~0.20~^{\circ} \text{C}$ </tex-math></inline-formula> maximum temperature error, and an error in relative basal permittivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.63~\pm ~0.08$ </tex-math></inline-formula> for a 4-km transect.

Numerical Estimation of SAR and Temperature Distributions inside Differently Shaped Female Breast Tumors during Radio-Frequency Ablation.

Radio-frequency (RF) ablation is a reliable technique for the treatment of deep-seated malignant tumors, including breast carcinoma, using high ablative temperatures. The paper aims at a comparative analysis of the specific absorption rate and temperature distribution during RF ablation with regard to different female breast tumors. In the study, four tumor models equivalent to an irregular tumor were considered, i.e., an equivalent sphere and ellipsoid with the same surfaces and volumes as the irregular tumor and an equivalent sphere and ellipsoid inscribed in the irregular tumor. An RF applicator with a specific voltage, operating at 100 kHz inserted into the anatomically correct female breast, was applied as a source of electromagnetically induced heat. A conjugated Laplace equation with the modified Pennes equation was used to obtain the appropriate temperature gradient in the treated area. The levels of power dissipation in terms of the specific absorption rate (SAR) inside the naturalistically shaped tumor, together with the temperature profiles of the four simplified tumor models equivalent to the irregular one, were determined. It was suggested that the equivalent tumor models might successfully replace a real, irregularly shaped tumor, and the presented numeric methodology may play an important role in the complex therapeutic RF ablation process of irregularly shaped female breast tumors.

Analytical – numerical estimation of temperature distribution in living tissue by the DPL bio-heat model

Here, we used mathematical modeling to predict and simulate the behavior and effect of temperature on human skin, together with studying the effects of the blood perfusion parameters, the time of tissue exposure to the laser, and the effect of the delay times on temperature distribution on living tissue. The model that has been used is a dual-phase lag DPL bioheat transfer model. The solution of the model has been obtained by using the analytical scheme represented by Laplace transforms, as well as the numerical scheme represented by the implicit finite difference method. The paper involves comparing analytical and numerical solutions, a comparison between the temperature distribution according to various bio-heat models and the resulting thermal damage. The results of the DPL bioheat model have been compared with the previous study and it showed. A favorable convergence results have been presented graphically and discussed in detail.