Prediction Of Temperature Distribution Research Articles

An efficient numerical simulator for geothermal simulation: A benchmark study

Accurate prediction of temperature and pressure distribution is essential for geothermal reservoir exploitation with cold water re-injection. Depending on our knowledge about the heterogeneous structure of the subsurface, the reservoir development scheme can be optimized and the overall lifetime of the geothermal field can be extended. In this study, we present Delft Advanced Research Terra Simulator (DARTS), which provides fast and accurate energy production evaluation for geothermal applications. This simulation framework is suitable for uncertainty analysis with a large ensemble of models. In DARTS, we select the molar formulation with pressure and enthalpy as primary variables. Besides, the fully-coupled fully-implicit two-point flux approximation on unstructured grids is implemented to solve the mass and energy conservation equations. For the nonlinear solution, we employ the recently developed Operator-Based Linearization (OBL) approach. In our work, DARTS is compared with the state-of-the-art simulation frameworks using a set of benchmark tests. We demonstrate that DARTS achieves a good match for both low- and high-enthalpy conditions in comparison to other simulators. At the same time, DARTS provides high performance and flexibility of the code due to the OBL approach, which makes it particularly useful for uncertainty quantification in processes involving complex physics.

Effect of Dehydration on the Rheological Measurement of Surimi Paste in Cone-Plate Rheometry: Heat and Mass Transfer Simulation

Moisture transfer characteristics of Alaska pollock (AP) surimi were investigated at various temperatures. The effective moisture diffusivity increased from 5.50 × 10−11 to 2.07 × 10−9 m2/s as the temperature increased from 30 °C to 90 °C. In order to investigate the mass and heat transfer characteristics of AP surimi, the simulation model was developed and evaluated by root-mean-square error (RMSE) (<2.95%). Rheological properties of AP surimi were investigated at different heating rates (1 °C/min, 5 °C/min, 10 °C/min, 20 °C/min and 30 °C/min). As heating rate increased to 20 °C/min and 30 °C/min, elastic modulus (G’) significantly diminished. The diminished G’ could be explained by impaired gel during temperature sweep supported by the predicted temperature distribution in the simulation model. Changes in moisture content of AP surimi during temperature sweep were also measured and predicted by the simulation model. The results showed the decreased amount of moisture content significantly increased as heating rate increased.

Experimental validation of a thermophysical fluid model for use in a hyperthermia treatment planning system

Accurate hyperthermia treatment planning, monitoring, and evaluation of temperatures in and near fluid volumes in the body requires realistic modelling of heat transport within fluids, which is currently not implemented in available treatment planning packages. Aim of this study is to assess the accuracy of a thermophysical fluid model, developed for treatment planning near fluid volumes.A cubic phantom with inner dimensions of (7 cm)3 was filled with deionised water. The front, back, top and bottom walls of the cube consisted of PVC, the side walls of stainless steel. The left wall was kept at a constant temperature of 25 or 37 °C, the right wall at 1, 2, 5, 10, or 15 °C higher. Thermal probes mapped the temperature profile in the central vertical plane perpendicular to the cold and hot walls with a spatial resolution of 5–10 mm. The temperature distributions were compared to simulations using a finite volume-based thermophysical fluid model implementing the Boussinesq approximation to the Navier-Stokes equations, developed as an extension to our in-house developed hyperthermia treatment planning suite. The simulations were performed using three meshes at different resolutions.The fluid model predicts the temperature distribution accurately (random and systematic error <0.1 °C, at least 95% of absolute errors <0.2 °C) for hyperthermic temperature differences (<5 °C within the fluid volume). When the temperature differences reach 15 °C, the random and systematic errors increase to 0.3 °C and 0.1 °C, respectively, with absolute errors up to 1.1 °C.The thermophysical fluid model predicts temperature distributions in a convective fluid with sufficient accuracy for hyperthermia treatment planning in and near fluid regions. A mesh with a resolution of 0.25 cm combines accurate results with acceptable computation times.

Temperature and Stress Evaluation during Three Different Phases of Friction Stir Welding of AA 7075-T651 Alloy

The current study performs an explicit nonlinear finite element simulation to predict temperature distribution and consequent stresses during the friction stir welding (FSW) of AA 7075-T651 alloy. The ABAQUS® finite element software was used to model and analyze the process steps that involve plunging, dwelling, and traverse stages. Techniques such as Arbitrary Lagrangian–Eulerian (ALE) formulation, adaptive meshing, and computational feature of mass scaling were utilized to simulate sequence events during the friction stir welding process. The contact between the welding tool and workpiece was modelled through applying Coulomb’s friction model with a nonlinear friction coefficient value. Also, the model considered the effect of nonlinear material properties as well as heat transfer conditions such as heat losses due to convection and thermal contact conductance between the workpiece and the backing plate interface on the thermal history. To validate the computational model results, an experimental procedure was carried out to measure temperature history on both sides of the specimen as well as the plunging force throughout the whole process time. The results obtained showed that symmetrical temperature distribution throughout the workpiece width was distinguished, implying that the tool rotation has a minor effect on the final temperature distribution. In addition, asymptotic V shape with high gradient temperature value in the weld nugget region after the full plunging was distinguished. Mechanical stresses and related plastic deformations generated, while achieving the FSW samples were evaluated in addition to the tool reaction force and heat generated to protect against tool failure.

High-Temperature Thermal Properties of Sprayed and Infill-Type Fire-Resistant Materials Used in Steel-Tube Columns

In order to investigate the fire performance of steel tube and concrete-filled steel tube (CFT) columns with fire-resistant materials, high-temperature thermal properties tests were performed to measure the thermal conductivity, specific heat, and mass loss of fire-resistant materials. Conventional sprayed fire-resistant material (SFRM) and a newly developed infill-type fire-resistant material (IFRM) were examined, and stub columns fire tests were also performed to verify the measured thermal properties. Test parameters for stub column fire tests were shape of the steel tube, presence of infill concrete, and thickness and type of fire-resistant materials. A heat transfer analysis was performed to predict temperature distributions in steel tube and CFT column specimens. Comparison with the test results show that the temperature distribution can be predicted with reasonable precision using the measured thermal properties of SFRM and IFRM.

Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults

The goal of this work is to achieve the defect-free production of parts made using Additive Manufacturing (AM) processes. As a step towards this goal, the objective is to detect flaws in AM parts during the process by combining predictions from a physical model (simulation) with in-situ sensor signatures in a machine learning framework. We hypothesize that flaws in AM parts are detected with significantly higher statistical fidelity (F-score) when both in-situ sensor data and theoretical predictions are pooled together in a machine learning model, compared to an approach that is based exclusively on machine learning of sensor data (black-box model) or physics-based predictions (white-box model). We test the hypothesized efficacy of such a gray-box model or digital twin approach in the context of the laser powder bed fusion (LPBF) and directed energy deposition (DED) AM processes. For example, in the DED process, we first predicted the instantaneous spatiotemporal distribution of temperature in a thin-wall titanium alloy part using a computational heat transfer model based on graph theory. Subsequently, we combined the preceding physics-derived thermal trends with in-situ temperature measurements obtained from a pyrometer in a readily implemented supervised machine learning framework (support vector machine). We demonstrate that the integration of temperature predictions from an ab initio heat transfer model and in-situ sensor data is capable of detecting flaws in the DED-produced thin-wall part with F-score approaching 90%. By contrast, the F-score decreases to nearly 80% when either temperature measurements from the in-situ sensor or temperature distribution predictions from the theoretical model are used alone by themselves. This work thus demonstrates an early foray into the digital twin paradigm for real-time process monitoring in AM via seamless integration of physics-based modeling (simulation), in-situ sensing, and data analytics (machine learning).

Prediction of temperature and residual stress distributions in friction stir welding of aluminum alloy

A thermal model considering the heat generated by friction and plastic deformation is developed for friction stir welding. The proportion of heat-generated between friction and plastic deformation depends on the contact states determined by the temperature and velocity of the tool. The model is verified by welding experiment, and the numerical results agree with the experimental data. Then, the effects of process parameters on temperature and residual stress are investigated using the validated model. These process parameters involve the welding speed, rotational speed, and axial force. The results show that the temperature increases with the increase of the rotational speed and axial force, and decreases with the increase of the welding speed. The increase of the axial force and welding speed will lead to larger tensile stress in the weld zone. Furthermore, the relationship between peak temperature, peak residual stress, and process parameters are established though regression method.

Spatial and Sequential Deep Learning Approach for Predicting Temperature Distribution in a Steel-Making Continuous Casting Process

Continuous casting is the procedure of the successive casting for solidification of the steel, which contains several cooling processes along the caster to coagulate the molten steel. It is such a rule of thumb that strand surface quality and casting productivity is highly dependent on temperature control. A finite-difference method is one of estimating temperature distribution, yet it hinders the process control efficiently. Song, et al. suggest a multimodal deep learning approach for prediction of the temperature. However, sequential and transient phenomena of solidifying steel are not considered, which makes it difficult to estimate the sequential heat-transfer characteristics in the whole process of the steel concretion. Herein, a deep learning model is proposed to predict the temperature distribution by taking into account both transient and steady-state characteristics. The proposed model addresses both spatial and sequential information by incorporating a convolutional neural network (CNN) and a recurrent neural network (RNN). Our quantitative and qualitative results show considerable predictive performance improvement against baseline models. Furthermore, the proposed model is applicable in a real-world steel-making industry by providing real-time temperature prediction, whilst retaining a lower computational cost.

Finite Element Simulation of Heat Transfer in Laser Additive Manufacturing of AlSi10Mg Powders

Abstract In the digital world, direct metal laser sintering (DMLS) is known as a laser-based additive manufacturing process, which grabs the attention of manufacturing industries to make the components directly from metal powder. It is a high-temperature process in which the metal particles are fused with laser power to form a full dense homogenous structure. The quality of fabricated parts depends on the heat transfer mechanism, temperature distribution, and molten pool formation in the powder bed, which directly influence by the process parameters such as scan speed, laser power, hatch spacing, powder layer thickness, and laser spot diameter. So it is necessary and important to understand the characteristics and mechanism of these thermal processes. The heat transfer mechanism involves conduction, convection, and radiation, which affects the formation of internal stresses that leads to deformation. In response to this fact, it was indispensable to analyze the thermal behavior in the powder bed instantly during the DMLS process and subsequently monitor the process parameters in order to obtain acceptable manufacture outputs intended for further use. The present research work focused to develop a finite element model for the prediction of temperature distribution and molten pool formation during sintering process of AlSi10Mg composite powders using ANSYS 17.0. The temperature distribution, thermal history, molten pool dimension and sintering depth in direct metal laser sintering process were investigatedat different laser spot diameter and powder layer thickness. From the simulation result, it was observed that as the laser spot diameter increases from 2mm to 6mm, the surface temperature of the molten pool decreases 4626°C to 541°C. So that molten pool length, width and sintering depth of powder bed decrease 3mm to 0 mm. Similarly, as powder bed layer thickness increases from 1mm to 3mm, the field temperature of the molten pool decreases 1200°C to 1075°C and the sintering depth decreases 0.243mm to 0.112mm. This model will be an important tool for the development of optimized process controls and cooling strategies

Experimental and numerical investigations of pipe orbital welding process

In this research work, finite element simulation and experimental investigation of Orbital welding process on stainless steel AISI 316 L with different process parameters are performed. For experimental study three process parameters viz. welding current, welding speed and standoff distance to get 9 dominating experimental sets according to L-9 orthogonal array for each Orbital and TIG welding. Temperature and Tensile strength during the welding process are taken as an output parameter. Weld zone and defect of welding are checked by Boroscope. Tensile tests are performed on all welded pipe to determine the tensile strength and elongation. Optimization of the process parameter is done using TAGUCHI and ANOVA through MINITAB software. The optimum values of input parameters for temperature distribution during orbital welding are 40A current, 0.72 mm/s welding speed and 1.8 mm standoff distance. It is observed that welding current affects the temperature distribution maximum 49.83% followed by a welding speed of 31.53%. Standoff distance has a minimum effect viz. only 8.83%. The optimum values for input parameters for tensile strength during Orbital welding are 40A current, 1.08 mm/s welding speed and 1.6 mm standoff distance and welding speed affect the tensile strength maximum 35.19% followed by welding current 21.23%. Standoff distance has a minimum affect only 7.97%. For numerical study, the 3D model is prepared with orbital welding boundary conditions. The complete orbital welding process is simulated using ANSYS APDL software to predict temperature distribution and residual stress in the butt welded pipe. The average error is of the order of 8.2% hence the simulated environment created for Orbital welding can be used to predict the temperature and other values for any other material with other welding parameters. The paper ends with a comparison of experimental temperature with simulated temperature and tensile strength of orbital and TIG welding process.

Influence of Turbulence Schmidt Number on Exit Temperature Distribution of an Annular Gas Turbine Combustor using Flamelet Generated Manifold

The Reynolds analogy concept has been used in almost all turbulent reacting flow RANS (Reynolds-averaged Navier-Stokes) simulations, where the turbulence scalar transfers in flow fields are calculated based on the modeled turbulence momentum transfer. This concept, applied to a lean premixed combustion system, was assessed in this paper in terms of exit temperature distribution. Because of the isotropic assumption involved in this analogy, the prediction in some flow condition, such as jet cross flow mixing, would be inaccurate. In this study, using Flamelet Generated Manifold as reaction model, some of the numerical results, obtained from an annular combustor configuration with the turbulent Schmidt number varying from 0.85 to 0.2, were presented and compared with a benchmark atmospheric test results. It was found that the Schmidt number σt in mean mass fraction f transport equation had significant effect on dilution air mixing process. The mixing between dilution air and reaction products from the primary zone obviously improved as σt decreased on the combustor exit surface. Meanwhile, the sensitivity of σt in three turbulence models including Realizable k-e, SST (Shear Stress Transport) and RSM (Reynolds Stress Model) has been compared as well. Since the calculation method of eddy viscosity was different within these three models, RSM was proved to be less sensitive than another two models and can guarantee the best prediction of mixing process condition. On the other hand, the results of dilution air mixing were almost independent of Schmidt number Sct in progress variable c transport equation. This study suggested that for accurate prediction of combustor exit temperature distribution in steady state reacting flow simulation, the turbulent Schmidt number in steady state simulation should be modified to cater to dilution air mixing process.

Coupled thermo-electro-mechanical models for thermal ablation of biological tissues and heat relaxation time effects

Thermal ablation is a widely applied electrosurgical process in medical treatment of soft biological tissues. Numerical modeling and simulations play an important role in prediction of temperature distribution and damage volume during the treatment planning stage of associated therapies. In this contribution we report a coupled thermo-electro-mechanical model, accounting for heat relaxation time, for more accurate and precise prediction of the temperature distribution, tissue deformation and damage volume during the thermal ablation of biological tissues. Finite element solutions are obtained for most widely used percutaneous thermal ablative techniques, viz., radiofrequency ablation (RFA) and microwave ablation (MWA). Importantly, both tissue expansion and shrinkage have been considered for modeling the tissue deformation in the coupled model of high temperature thermal ablation. The coupled model takes into account the non-Fourier effects, considering both single-phase-lag (SPL) and dual-phase-lag (DPL) models of bio-heat transfer. The temperature-dependent electrical and thermal parameters, damage-dependent blood perfusion rate and phase change effect accounting for tissue vaporization have been accounted for obtaining more clinically relevant model. The proposed model predictions are found to be in good agreement against the temperature distribution and damage volume reported by previous experimental studies. The numerical simulation results revealed that the non-Fourier effects cause a decrease in the predicted temperature distribution, tissue deformation and damage volume during the high temperature thermal ablative procedures. Furthermore, the effects of different magnitudes of phase lags of the heat flux and temperature gradient on the predicted treatment outcomes of the considered thermal ablative modalities are also quantified and discussed in detail.

Numerical prediction of temperature distribution and residual stresses on plasma arc welded thin titanium sheets

ABSTRACT The residual stresses are responsible for most of the failure of weldments in nuclear industry and the temperature distribution is the key measure to predict these residual stresses. The residual stresses are produced due to the non-uniform heating and cooling of the material and thus generating inhomogeneous plastic deformation in the components during the welding process. The Plasma arc welded 2 mm thick Ti-6Al-4V plate is taken for the study to predict the weld bead geometry, temperature distribution and residual stresses. The temperature distribution is predicted through the transient thermal analysis in ANSYS Parametric Design Language (APDL) using Three-Dimensional Conical Heat Source Model & Dhinakaran’s model and the simulated results are validated against the experimental outcomes. The temperature-dependent material properties with the temperature range varying from the room temperature to the melt pool temperature are considered for transient thermal analysis. The radiation heat transfer becomes predominant at high-temperature regimes; hence the radiation effects are imposed into the convective heat transfer coefficient. The weld bead geometry, temperature distribution and the residual stresses are predicted at various regimes of the plate. It is observed that the theoretical values obtained from the FEM Simulation are good in agreement with the experimental values.

Analytical modeling of in-situ deformation of part and substrate in laser cladding additive manufacturing of Inconel 625

Laser cladding is a widely used additive manufacturing (AM) technique to add new material or replace wear to an existing metallic part, in which thin metal layers are deposited and fused using a powder feedstock and laser power respectively. The repeatedly rapid heat and solidification during the laser cladding process lead to deformation of the build and substrate such as geometrical deviation and delamination, which are detrimental for the part. This paper presents an analytical model for the fast prediction of the deformation of the build and substrate in the laser cladding process for the first time. Moving heat source solution is employed and modified to predict temperature distribution with the assumption of uniform temperature at the same in-depth locations. Die-substrate assembly model is employed to predict the deformation of the build and substrate, in which the build was mathematically discretized into two sections. The presented model provides a closed-form solution to the prediction of deformation with high computational efficiency, compared to finite element models. Inconel 625 is chosen to demonstrate the capability of the presented model. Close agreements are observed based upon validation to experimental measurements in the literature.

Analysis and design of nanofluid-filled power transformers

To alleviate the adverse impact due to the deterioration of electrical insulation, this paper presents an electromagnetic-thermal-fluid analysis for the prediction of temperature distribution in transformers with oil-based nanofluids or pure oil. The core loss and copper loss are taken as the heat sources for the temperature analysis using computational fluid dynamics. To strive for computing the temperature distribution accurately in the nano-oil, an effective numerical method using finite volume method and improved physical parameter model are employed. Numerical simulation of the thermal performance of the nanofluid with different volumetric fractions with Al2O3 nanoparticles is compared with those using pure transformer oil in a 500 VA single-phase transformer. From the comparisons of the simulation results, it is found that the volumetric fraction 0.01% is an optimum concentration in reducing the transformer size for the same power rating. The observation is served as useful guidelines and detailed process for the design of oil-based power transformers.

Flow and temperature distributions in a disc type winding-Part II: Natural cooling modes

The liquid flow and temperature distributions in oil natural (ON) cooled power transformer windings are of great importance for thermal design, thermal rating and life estimation of ON power transformers. In this paper, both experimental and numerical investigations of liquid flow and temperature distributions in a disc-type winding model are conducted. The influences of representative operating conditions on liquid flow and temperature distributions are studied using Particle Image Velocimetry (PIV) and temperature measuring systems. Reverse liquid flows and consequential localised overheating at the top part of winding passes are recorded in the experiments. 2D and 3D CFD simulations are implemented and compared with the measurements. 3D CFD simulations are required for reliable predictions of temperature distribution when reverse flows occur. Dimensional analysis is adopted to interpret the measurement and CFD simulation results in order to provide simplicity, insight and universally applicable conclusions. The liquid flow and temperature distributions are found to be mainly controlled by the Richardson number (Ri). The higher Ri the more uneven the liquid flow and temperature distributions would be. At low Ri region flow and temperature distributions are relatively uniform with a low hotspot factor. Above the region, flow and temperature distributions become rapidly uneven and the hotspot factor increases steeply with the increase of Ri. Further increase of Ri results in the occurrence of reverse flows and fluctuation of the hotspot factor.

Coupled physics solvers for analysing the magneto‐thermal behaviour of a permanent magnet synchronous machines

AbstractThe aim of this study is to present a thermal analysis of a permanent magnet synchronous machine based on the finite element method. The developed model can be used to predict temperature distribution inside the studied motor during the rated operation. Electromagnetic computation (EM) is carried out with the aid of two 2D finite‐element (FE) simulations on the cross‐section of the PM motor. To analyse the process of heat transfer (HT) in an electrical machine, empirical correlations are used to describe the convective (HT) from the different surfaces of the PM motor. The (HT) coefficient is determined using dimensionless numbers. The proposed numerical method to solve the coupled problem is based on independently and successively solving the (EM) and thermal problems and then transferring the results between the two sub‐problems. The results obtained by the model are compared with experimental results from testing the prototype electric motor.

Experimental validation of a steady periodic analytical model for Breathing Walls

The Breathing Wall behaviour under variable boundary conditions is described by an analytical model based on a one-dimensional porous domain crossed by air and subject to third type steady periodic boundary conditions. To the best of the authors’ knowledge, its experimental validation is not provided in literature.In this work, a new model is derived considering Dirichlet steady periodic boundary conditions. The model is experimentally validated testing a 1 m2 no-fines concrete sample in the Dual Air Vented Thermal Box apparatus, specially improved to replicate dynamic thermal conditions. The experiments show that increasing the air flow velocity across the Breathing Wall from 0 to 12 mm/s enhances thermal coupling between the two environments, namely reduces the wall thermal capacity, with a decrease in the penetration time from 4.3 h to 3 h.The model shows a very good agreement with experimental data when predicting temperature distribution across the domain, with error averages and standard deviations within the thermocouple accuracy after calibration, assumed to be 0.15 ∘C. The lesser yet good agreement concerning conduction heat flux density is explained in terms of accuracy in the measurement of the boundary conditions and critical issues in the heat flow measure itself (i.e. probe thermal resistance, thermal contact, emissivity mismatch).

A Coupled 1D–3D Numerical Method for Buoyancy-Driven Heat Transfer in a Generic Engine Bay

Energy efficient vehicles are essential for a sustainable society and all car manufacturers are working on improved energy efficiency in their fleets. In this process, an optimization of aerodynamics and thermal management is most essential. The objective of this work is to improve the energy efficiency using encapsulated heat generating units by focusing on predicting temperature distribution inside an engine bay. The overall objective is to make an estimate of the generated heat inside an encapsulation and consecutively use this heat for climatization purposes. The study presents a detailed numerical procedure for predicting buoyancy-driven flow and resulting natural convection inside a simplified vehicle underhood during thermal soak and cool-down events. The procedure employs a direct coupling of one-dimensional and three-dimensional methods to carry out transient one-dimensional thermal analysis in the engine solids synchronized with sequences of steady-state three-dimensional simulations of the fluid flow. The boundary heat transfer coefficients and averaged fluid temperatures in the boundary cells, computed in the three-dimensional fluid flow model, are provided as input data to the one-dimensional analysis to compute the resulting surface temperatures which are then fed back as updated boundary conditions in the flow simulation. The computed temperatures of the simplified engine and the exhaust manifolds during the thermal soak and cool-down period are in favorable agreement with experimental measurements. The present study illustrates the capabilities of the coupled thermal-flow methodology to conduct accurate and fast computations of buoyancy-driven heat transfer. The methodology can be potentially applied to design and analysis of multiple demand vehicle thermal management systems in hybrid and electrical vehicles.

Efficient and robust prediction of internal temperature distribution and boundary heat flux in participating media by using the Kalman smoothing technique

The Kalman smoothing (KS) technique, which is consisted of the Kalman filtering and Rauch-Tung-Striebel smoothing technique, is introduced to resolve the inverse radiation-conduction heat transfer problem by using the future temperature measurement information. For the forward problem, the discrete ordinate method is employed to solve the radiative transfer equation and the energy equation is resolved by using the finite volume method. The boundary time-dependent heat flux and internal temperature filed in participating media are retrieved in near real time from measurement temperature on the right surface. Different forms of time-dependent heat flux are imposed on the left surface to examine the performance of the proposed algorithm. All the reconstruction results indicate that the KS technique is effective and robust for resolving the retrieval of the boundary time-dependent heat flux and internal temperature fields in near real time. The effect of different parameters on the accuracy and stability of the reconstruction results, including the future temperature information, sampling interval, measurement noise covariance, initial state error, and initial state error covariance, are analyzed in detail. Compared with the KF technique, the reconstruction accuracy of KS technique can be improved obviously. Meanwhile, the time delay and oscillation of reconstructed heat flux are reduced significantly and the deviation of the retrieval temperature is also decreased greatly.