Prediction Of Temperature Field Research Articles

Evaluation of flamelet-based partially premixed combustion models for simulating the gas phase combustion of a grate firing biomass furnace

Depending on the mass flow rates of fuel, the primary air (injected usually from underneath of the fuel bed) as well as the composition of volatile gases extracted from the bed, it is believed that the volatile gases taking part in the gas-phase combustion undergo a partially premixed process close to the bed section. Therefore, the aim of this study is to evaluate the prediction capability of flamelet-based partially premixed combustion models in simulating the gas-phase combustion process of a grate firing biomass furnace. Additionally, the effects of the adopted premixed models (i.e., C Equation based model and extended coherent flame model) and non-premixed models (i.e., steady diffusion flamelet (SFM) and unsteady diffusion flamelet (UFM)) on the overall prediction of partially premixed model are assessed. The predicted temperature field and species concentrations are compared with published experimental measurements and also with numerical simulations which use other combustion models (i.e., EDC/Flamelet hybrid model, SFM and UFM). The results of this study revealed that except for slow forming and chemically dominated species, partially premixed combustion models (both extended coherent flame model/SFM and C-equation/SFM based partially premixed model) are capable of reproducing the experimental temperature and major species with reasonable accuracy. For instance, the predicted temperature deviates from the experimental counterpart by a maximum of 9.01%, and the major species by 15.44% (CO2) and 21.60% (O2). In addition, C-equation/UFM-based partially premixed model is found to be the most optimum combination of the examined partially premixed models for overcoming the deficiency faced while predicting the slow-forming species.

Multi-scale modeling for prediction of residual stress and distortion in Ti–6Al–4V semi-circular thin-walled parts additively manufactured by laser powder bed fusion (LPBF)

Despite many advantages of metallic additive manufacturing technology (AM), the difficulties in fully observing and modeling the complex nature of the process prevent its wide implementation in different industry sectors. There have been significant efforts during the last decade to develop reliable models to predict temperature field, melting stage, residual stresses and distortion during a material deposition in AM processes, in order to understand the complex process–structure–property relations in different scales, from powder particle to the whole part. In this study, a mechanical and thermo-mechanical computational methods are implemented to trade off the assessment between accuracy and computational cost of the process simulation. By taking advantage of a multiscale approach to reduce the computational cost and time while achieving promising accuracy, two scaled-models and non-scaled models with various levels of fidelity’s discretization were developed. The fidelity level is defined in space by allocating different numbers of physical layers of powder deposition to the finite element and in time by the time increment definition in numerical simulations. The commercial finite element software ABAQUS-implicit associating AM-plugin is utilized for modeling the printing process. The high-fidelity level for scaled part modeling duplicates the process and guides the medium-fidelity level reducing the computational resources needed. Then, by achieving the adequate configuration from the aforementioned level, the non-scaled model with the real dimensions of the experiments is simulated. Subsequently, the low-fidelity level is implemented for the real scale part utilizing the inherent strain method, taking into account the residual strains. The different approaches are compared, showing that the inherent strain method is more efficient in terms of accuracy and computational cost than the multiscale thermo-mechanical method.

A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain

In the whole aircraft structural optimization loop, thermal analysis plays a very important role. But it faces a severe computational burden when directly applying traditional numerical analysis tools, especially when each optimization involves repetitive parameter modification and thermal analysis. Recently, with the fast development of deep learning, several Convolutional Neural Network (CNN) surrogate models have been introduced to overcome this obstacle. However, for temperature field prediction on irregular geometric domains (TFP-IGD), CNN can hardly be competent since most of them stem from processing for regular images. To alleviate this difficulty, we propose a novel physics and data co-driven surrogate modeling method. First, after adapting the Bezier curve in geometric parameterization, a body-fitted coordinate mapping is introduced to generate coordinate transforms between the irregular physical plane and regular computational plane. Second, a physics-driven CNN surrogate with partial differential equation (PDE) residuals as a loss function is utilized for fast meshing (meshing surrogate); then, we present a data-driven surrogate model based on the multi-level reduced-order method, aiming to learn solutions of temperature field in the above regular computational plane (thermal surrogate). Finally, combining the grid position information provided by the meshing surrogate with the scalar temperature field information provided by the thermal surrogate (combined model), we reach an end-to-end surrogate model from geometric parameters to temperature field prediction on an irregular geometric domain. Numerical results demonstrate that our method can significantly improve accuracy prediction on a smaller dataset while reducing the training time when compared with other CNN methods.

Three-Dimensional Numerical Simulation of Grain Growth during Selective Laser Melting of 316L Stainless Steel.

The grain structure of the selective laser melting additive manufactured parts has been shown to be heterogeneous and spatially non-uniform compared to the traditional manufacturing process. However, the complex formation mechanism of these unique grain structures is hard to reveal using the experimental method alone. In this study, we presented a high-fidelity 3D numerical model to address the grain growth mechanisms during the selective laser melting of 316 stainless steel, including two heating modes, i.e., conduction mode and keyhole mode melting. In the numerical model, the powder-scale thermo-fluid dynamics are simulated using the finite volume method with the volume of fluid method. At the same time, the grain structure evolution is sequentially predicted by the cellular automaton method with the predicted temperature field and the as-melted powder bed configuration as input. The simulation results agree well with the experimental data available in the literature. The influence of the process parameters and the keyhole and keyhole-induced void on grain structure formation are addressed in detail. The findings of this study are helpful to the optimization of process parameters for tailoring the microstructure of fabricated parts with expected mechanical properties.

Thermal error prediction of ball screw feed system based on inverse heat transfer analysis

The thermal error of the ball screw feed system will reduce the positioning accuracy of the machine tool, and the main reason for the thermal error is the friction heat generated by bearings and screw nut in the feed system. Therefore, a time-varying thermal error prediction method of the ball screw feed system based on the inverse method is proposed in this paper. Based on data-driven and dynamic thermal network model, the heat source estimation method of ball screw feed system under unsteady thermal effect is established, and the thermodynamic equilibrium equation is deduced into explicit form for heat source load identification. Aiming at the common matrix ill-conditioned problem of load identification, the regularization algorithm is used to identify the heat source load, and the optimal selection method of regularization parameters is proposed based on Monte Carlo algorithm. Using the collected temperature experiment data and the position data of the moving nut, the dynamic heat source load, temperature field, and thermal error of the feed system under the actual working condition are predicted and analyzed by using the inverse method. Finally, the accuracy and effectiveness of the prediction method are verified by experiment. The inverse method proposed in this paper has great application potential for the prediction and estimation of heat source and temperature field of machine tools and various mechanical structures.

Predictions of flow and temperature fields in a T-junction based on dynamic mode decomposition and deep learning

Accurate flow field prediction methods are needed for the analysis of complex flows in energy and power field. Flow field and temperature field prediction methods combining Dynamic Mode Decomposition (DMD) and deep learning are proposed. A Convolutional Long Short-Term Memory (ConvLSTM) neural network model is built by adjusting the network structure reasonably. The DMD method, the ConvLSTM method and the method combining DMD and ConvLSTM are compared by the flow field and temperature field prediction results in a T-junction, which is widely used in energy industry. The time series dataset of the velocity, pressure and temperature field of a wall jet in a T-junction are obtained through large eddy simulation (LES). The overall relative errors in the predictions of velocity, pressure and temperature fields remained about 4%, 60% and 0.13% for the DMD method, 3%, 10% and 0.08% for the ConvLSTM method, and 2%, 10% and 0.06% for the method combining DMD and ConvLSTM, respectively. The combining method is the most accurate and stable prediction method. Its information loss rates of the velocity, pressure and temperature fields are the smallest and 2.21%, 13.38% and 0.11%, respectively, and will not increase significantly with the increase of the prediction duration.

Three-Dimensional CFD Modeling on the Thermal Characteristics of Buried Oil Pipeline Involving the Heat Transfer of Wax Layer

It is common to have a wax layer on the inner wall of waxy crude oil pipeline. However, the study of the wax layer on the heat transfer of buried pipeline systems is inadequate due to its instability of composition and properties; it may lead to the inaccurate prediction of the pipeline temperature field. Based on the finite element simulation technology, a three-dimensional heat transfer model of buried crude oil pipeline involving wax layer was proposed and solved. The thermal effect of the wax layer on pipeline system was analyzed quantitatively. Numerical results show that the average deviation of soil temperature near the pipeline reach 1.42 K when there is a 4 mm wax layer. Among different thermal conductivity models of heterogeneous materials, the EMT model plays best in predicting the conductivity of a waxy layer. By setting different working conditions, the influence mechanisms of several thermal influencing factors are discussed. The results show that the thermal influence range of heated pipe is positively associated with oil temperature and velocity. The core thermal response zone is about 12 m along the X-axis. Beyond 8 m depth from ground surface, the temperature fluctuation of soil is almost unaffected by the oil pipeline.

Investigation of interfacial heat transfer characterization for TC4 alloy in triple-layer sheet hot stamping process

During the hot stamping process, heat exchange always occurs between the hot sheet and cold tools. The interfacial heat transfer in the whole-forming process will not only affects the forming quality of the parts but also partially determines their post-form mechanical properties and microstructure distribution. As an essential parameter, the interface heat transfer coefficient (IHTC) is of great significance for the prediction of temperature fields in the finite element simulations, especially for the novel-forming process named triple-layer sheet hot stamping. In this study, the triple-layer sheet heat exchange experiment is carried out to investigate the interfacial heat transfer behavior of the titanium alloy sheet in the triple-layer sheet hot stamping process. The effects of contact pressure, cladding steel sheet thickness, and contact gap on the interfacial heat transfer behavior and post-form mechanical properties of titanium alloy sheet are analyzed, and the interface heat transfer coefficient between the titanium alloy and high-strength steel sheets is calculated. The finite element model of triple-layer sheet heat transfer is established to verify the accuracy of the calculated interfacial heat transfer coefficient. The results show that the upper and lower steel sheets maintain the temperature of the titanium alloy sheets at a higher level in the triple-layer sheet hot stamping compared with the single-layer one. The temperature of the titanium alloy sheet under the single-layer hot stamping decreases from 900 to 781.6 °C after transferring, a decrease of 118.4 °C. The temperature of the titanium alloy sheet only declines by 57.5 °C during this period in the triple-layer sheet hot stamping process. In addition, the titanium alloy parts obtained by the triple-layer sheet hot stamping process have better mechanical properties. The IHTC increases with the increase of contact pressure and decreases with the increase of steel sheet thickness and contact gap. The accuracy of the calculated IHTC between the titanium alloy and cladding steel sheets is validated by comparing the results of the experiment and simulation. The maximum error between the simulation results and the experimental measurements is 5.5%.

Real-Time Construction of Thermal Model Based on Multimodal Scene Data

In commercial buildings, the total consumption of central air conditioning accounts for about 40%–50%. However, at present, the initial design value of building Heating Ventilation and Air Conditioning (HVAC) is usually far greater than the actual refrigeration value of refrigeration demand, which will lead to great energy consumption waste. Moreover, the operation of HVAC affects the thermal comfort of users, so it is necessary to establish a thermal model for the scene to control. The thermal model describes the temperature of the scene in different environments. So it is very important to design a thermal model to calculate the scene in real time. Because the flow of people, the opening of windows, the ventilation of the scene and other parameters influence the change of thermal state in the scene environment, these parameters are complicated to model. Human disturbance will lead to the instability of the state of the scene environment. The inconsistency of its thermal model will lead to energy allocation tracking strategies in different regions. To solve this problem, We propose a thermal model for building thermal comfort using a multimodal analysis framework. This paper analyzes multiple temperature and humidity sensors and area image by multimodal combination and processes the image and sensor data by combining CNN and LSTM. Our results show that when the thermal model analyzed by this method is deployed in a building in the south of China, the MSE accuracy of the local effect of temperature field prediction reaches 99%, and its AMAX reaches 94%, so the running stability of the model in the scene is high. In addition, the research shows that the thermal model analysis framework can make the Internet of Things (IoT) in buildings more intelligent, and it can be combined with this thermal model to improve human comfort, make it easier to deploy in each hot zone, and have a better overall energy-saving effect.

Experimental and numerical investigation of wall temperature in a heated pipe filled with beads

Placing granular materials inside empty channels with coolant or heat transfer fluids flowing through is an effective way to increase the heat transfer rate to the wall. Although this approach has been used in different engineering fields, a particular goal is seldom considered in some applications, namely the temperature uniformity of the bounding wall. As temperature varies along the flow direction, a non-uniform temperature field appears on the wall. This bears consequences in some applications such as in electronics, battery cooling or composite manufacturing. In the present study, the uniformity of the wall temperature is investigated numerically for a typical cylindrical pipe filled with monodisperse ceramic beads and heated by air flow. Numerical simulations based on Reynolds-averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) are performed to analyze the heat transfer to the bounding wall. Because of the uncertainty mentioned in the literature, the applicability of an empirical model reflecting additional sources of production and dissipation of turbulent energy due to beads is firstly evaluated. By comparing the numerical predictions of temperature fields with experimental data, a damping function is needed to adjust these additional source terms to better fit experimental data for bulk Reynolds numbers between 100 and 700. A parametric study is then conducted to assess the effect of various parameters on the wall temperature uniformity. Results shows that wall materials of low volumetric heat capacity and a reduced wall thickness greatly shorten the duration to reach thermal uniformity. A less pronounced enhancement also exists for larger thermal conductivity of the wall material. However, using beads of lower volumetric heat capacity does not have a major influence on the uniformity of the wall temperature.

Prediction model of temperature field in dual-mode combustors based on wall pressure

Effective acquisition of the complex flow fields in dual-mode combustors is valuable in improving dual-mode combustor performance. Introducing deep learning methods for fast flow field prediction is an effective way to provide high-quality temperature fields in a dual-mode combustor. A data-driven model to obtain temperature fields based on wall pressure of a dual-mode combustor is proposed in this paper. The temperature fields in dual-mode combustors depend on the combustor geometry, incoming flow condition, and equivalent ratio. The prediction model takes convolutional neural network as the main framework and includes multiple branches. The trained temperature field prediction model can effectively output the temperature distribution of the combustor with the input of wall pressure. The temperature field prediction accuracy has been discussed in depth. From the analysis results, the effectiveness and accuracy of the approach have been demonstrated under the current dataset.

Flameless combustion of low calorific value gases, experiments, and simulations with advanced radiative heat transfer modeling

Thermal radiation is the dominant mode of heat transfer in many combustion systems, and in typical flameless furnaces, it can represent up to 80% of the total heat transfer. Accurate modeling of radiative heat transfer is, thus, crucial in the design of these large-scale combustion systems. Thermal radiation impacts the thermochemistry, thereby the energy efficiency and the temperature sensitive species prediction, such as NOx and soot. The requirement to accurately describe the spectral dependence of gaseous radiative properties of combustion products interacts with the modeling of finite rate chemistry effects and conjugates heat transfer and turbulence. Additionally, because of the multiple injection of fuels and/or oxidizers of various compositions, case-specific radiative properties' expressions are required. Along these lines, a comprehensive modeling to couple radiation and combustion in reacting flows is attempted and applied to the simulation of flameless combustion. Radiation is modeled using the spectral line-based weighted-sum-of-gray-gases approach to calculate gaseous radiative properties of combustion products using the correlation of the line-by-line spectra of H2O and CO2. The emissivity weights and absorption coefficients were optimized for a range of optical thicknesses and temperatures encountered in the considered furnace. Efforts were also made on the development of a reliable and detailed experimental dataset for validation. Measurements are performed in a low calorific value syngas furnace operating under flameless combustion. This test rig features a thermal charge which can extract about 60% of combustion heat release via 80% of radiative heat transfer, making it of special interest for modeling validation. The comparison between the simulation and the experiment demonstrated a fair prediction of heat transfer, energy balance, temperature, and chemical species fields.

Amalgamation of Geometry Structure, Meteorological and Thermophysical Parameters for Intelligent Prediction of Temperature Fields in 3D Scenes.

Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.

Temperature field prediction for various porous media considering variable boundary conditions using deep learning method

The prediction of temperature fields in porous media is challenging owing to the variable boundary conditions attributed to the working condition and topological structure. In this study, a supervised convolutional neural network (CNN) is built to predict the temperature field and effective thermal conductivity of sphere-packed and irregular porous media under various boundary conditions. Datasets of temperature fields, obtained using lattice Boltzmann method (LBM) simulations based on three-dimensional sphere-packed porous media, are employed to a CNN for training. The CNN achieves an accurate and fast prediction of the temperature field and effective thermal conductivity with different boundary conditions (the temperature differences between the inlet and outlet are 25, 50, 100, 125, 150, 175 °C). The relative errors for the effective thermal conductivity between the CNN and LBM are 0.7–22.8% for the sphere-packed porous media and 3.1–16.0% for the irregular porous media. For a typical case of sphere-packed porous medium with a porosity of 0.6, the computation time using CNN is 1.53 × 10−2 h, while that of LBM is approximately 720 h. These findings mean that the CNN is promising for the prediction of the heat transport properties of porous media with different morphologies and variable boundary conditions.

A note on the use of Dynamic Mode Decomposition in mechanics

We advocate for the use of Dynamic Mode Decomposition (DMD) in mechanics. Snapshots of the temperature or displacement fields are obtained; and the DMD algorithm, which, remarkably, requires a few lines of code, is applied to matrices obtained by time shifting the snapshots. Only the higher-order modes are retained to obtain a lower-rank matrix and used for analysis. We show that DMD can be used to: predict temperature fields during transient heat conduction, estimate natural frequency of a linear elastic structure; identify the onset of plasticity in an elastic–plastic plate with a hole; and obtain a reduced-order model that describes the large deformation of a non-linear elastic structure.

3D temperature field prediction in direct energy deposition of metals using physics informed neural network

3D temperature field prediction in direct energy deposition of metals using physics informed neural network

Dataset of A Thermal Model For The Prediction of Temperature Fields During The Creation of Austenite/martensite Mesostructured Materials By Localized Laser Treatments In A Fe-Ni-C Alloy

Dataset of A Thermal Model For The Prediction of Temperature Fields During The Creation of Austenite/martensite Mesostructured Materials By Localized Laser Treatments In A Fe-Ni-C Alloy

Data-Driven Varying State-Space Model Based on Thermal Network for Transient Temperature Field Prediction of Motorized Spindles

Data-Driven Varying State-Space Model Based on Thermal Network for Transient Temperature Field Prediction of Motorized Spindles

An Analytical Model for Predicting Temperature Fields during Fluid Circulation in a Deep-Sea Well

Summary Accurate prediction of temperatures along a well during deep-sea drilling (DSD) is significant for wellbore stability analysis. In this paper, an analytical model is developed to study the thermal behavior around wellbore during DSD. The analytical solutions for temperatures in the tubing, annulus, and formation are obtained in Laplace space, and their values in time domains are obtained by the numerical Stehfest method. A sensitivity study of temperature distribution under different injection temperature and rate, seawater depth, and wellbore length is carried out, and a comparison is made for the thermal behavior between onshore drilling and DSD. It is found that injection rate plays a dominate role in the bottomhole temperature (BHT), which decreases by more than 40°C after 6 months when it varies from 2 to 20 kg/s. Injection temperature only affects the temperature along wellbore at a depth less than 2000 m. There is large difference in the temperatures along the wellbore between DSD and onshore drilling. The difference in the temperature at the depth of seabed and bottomhole between the two cases reaches 80 and 70°C, respectively, after 1 day. In addition, the analytical model can work as a benchmark for other models predicting the thermal behaviors during DSD.

Scale resolving simulations of a high-temperature turbulent jet in a cold crossflow: Comparison of two approaches

A high-temperature turbulent jet in a cold crossflow is investigated with the help of two scale-resolving simulation approaches. This work aims at improving the methodologies used to predict the thermal footprint of exhaust gases issuing from helicopter engines onto the fuselage. Specific attention is brought to the capability of scale resolving simulations to correctly reproduce flow dynamics and turbulent mixing. Mean flow features, turbulent quantities and temperature fields are compared and validated against wind tunnel test measurements. In addition, the present work highlights the importance of synthetic turbulence injection at pipe inlet to obtain a fair prediction of both flow dynamics and temperature field.