Prediction Of Temperature Distribution Research Articles

Numerical prediction of rotary-kiln foundation temperature at an early age

The hydration heat confined to the core of the mass concrete during the hydration reaction causes a temperature rise and irregular temperature distribution in the concrete. High temperatures in concrete cause Delay Ettringite Formation (DEF) that cause damage several years after pouring, especially if the concrete is in an acidic environment. The uneven temperature distribution causes thermal stresses that can initiate cracks in the concrete surface. This article discusses a prediction of temperature distribution inside a mass concrete used as a rotary kiln foundation. We measure the heat of hydration of the concrete sample using an adiabatic calorie meter and derive the heat of hydration equation from the measurement data. The hydration heat was used in numerical calculations to obtain the temperature distribution, maximal temperature and temperature differential. The numerical calculation shows that the maximum foundation temperature was 64.01 0C. This temperature is still below the limit temperature for the occurrence of Delayed Ettringite Formation (DEF). The core region has the highest temperature, while the surfaces have the lowest temperature. The difference between the highest and lowest temperatures is 37.40 0C. However, the temperature differential exceeds the safe limit, 20 0C, so heat treatment to prevent cracking needs to be done.

Temperature distribution prediction in control cooling process with recurrent neural network for variable-velocity hot rolling strips

Control cooling is an essential method for microstructure and mechanical property control in hot rolling strip making. Therefore, it is vital to realize high-precision temperature distribution prediction and control in cooling process to ensure the industrial production. In this paper, a traditional mechanism model based on finite-difference method combined with online cycle velocity calculation strategy was introduced as one of the baseline methods estimating temperature distribution. However, considering calculation time, variable-velocity rolling makes it difficult to rapidly realize temperature and modifying water distribution of all segments in cooling zone. Herein, a temperature distribution prediction method based on recurrent neural network was proposed, by fully considering the variable-velocity rolling dynamic characteristics. And the temperature distribution prediction performance of the model with different recurrent cell and time steps was evaluated. The results indicated that the proposed model could realize temperature distribution prediction, and the model based on bi-LSTM and 48 timesteps has the highest determination coefficient value of 0.976, the lowest root mean square error of 8.03, and a mean absolute error of 5.7. Furthermore, compared with baseline model, the proposed model retained lower computational cost, making it applicable in industrial application by providing real-time temperature distribution prediction.

Large-Scale Robot-Based Polymer and Composite Additive Manufacturing: Failure Modes and Thermal Simulation.

Additive manufacturing (AM) of large-scale polymer and composite parts using robotic arms integrated with extruders has received significant attention in recent years. Despite the contributions of great technical progress and material development towards optimizing this manufacturing method, different failure modes observed in the final printed products have hindered its application in producing large engineering structures used in aerospace and automotive industries. We report failure modes in a variety of printed polymer and composite parts, including fuel tanks and car bumpers. Delamination and warpage observed in these parts originate mostly from thermal gradients and residual stresses accumulated during material deposition and cooling. Because printing large structures requires expensive resources, process simulation to recognize the possible failure modes can significantly lower the manufacturing cost. In this regard, accurate prediction of temperature distribution using thermal simulations is the first step. Finite element analysis (FEA) was used for process simulation of large-scale robotic AM. The important steps of the simulation are presented, and the challenges related to the modeling are recognized and discussed in detail. The numerical results showed reasonable agreement with the temperature data measured by an infrared camera. While in small-scale extrusion AM, the cooling time to the glassy state is less than 1 s, in large-scale AM, the cooling time is around two orders of magnitudes longer.

Welding parameters optimization during plunging and dwelling phase of FSW 2219 aluminum alloy thick plate

The influence of welding parameters on temperature distribution during plunging and dwelling phase of friction stir welding (FSW) 2219 aluminum alloy thick plate has not been studied. Improper selection of welding parameters will result in uneven temperature distribution along with the thickness of the weldment, leading to welding defects and ultimately affecting the mechanical properties of the weldment. To achieve the prediction of temperature distribution and the optimization of welding parameters, a simulation process model of FSW 18-mm-thick 2219 aluminum alloy is established based on DEFORM. The validity of the simulation is verified by experiments. With the minimum temperature difference in the core area of the weldment as the target value and weldable temperature range of 2219 aluminum alloy as the constraint conditions, orthogonal experiments are conducted considering the rotational speed, the press amount, the tool tilt angle, the plunging traverse speed and the dwelling time. The results of variance analysis show that the rotational speed and the dwelling time are significant factors affecting the temperature field during the plunging and dwelling phase. Through single factor simulation, the welding parameters during the plunging and dwelling phase are optimized. This study guides the selection of welding parameters of the FSW 2219 aluminum alloy thick plate.

Local Thermal Nonequilibrium (LTNE) Modeling of a Partially Porous Channel With Spatial Variation in Biot Number

Abstract The local thermal nonequilibrium (LTNE) model has been used widely for analyzing heat transfer during internal flow through porous media, including when a channel is only partially filled with a porous medium. In such problems, the Biot number describes the rate of convective heat transfer between solid and fluid phases. While uniform Biot number models are commonly available, recent advances in functionally graded materials necessitate the analysis of spatially varying Biot number in such geometries. This paper presents LTNE-based heat transfer analysis for fully developed flow in a channel partially filled with porous medium and with spatially varying Biot number to describe solid-fluid interactions in the porous medium. Fully uncoupled ordinary differential equations for solid and fluid temperature distributions are derived under three different boundary condition models. Solid and fluid temperature fields are presented for a variety of Biot number distributions, including quadratically and periodically varying functions. An explanation of the nature of temperature distribution predictions for such problems is provided. For special cases, the results presented here are shown to reduce to past work on constant Biot number. This work improves the theoretical understanding of porous media heat transfer and facilitates the use of such theoretical models for functionally graded materials.

Developing a Framework for Using Molecular Dynamics in Additive Manufacturing Process Modelling

Additive Manufacturing (AM), or else Smart Manufacturing, has been an intrinsic concept in Industry 4.0, offering flexibility and material efficiency. Certain limitations prevent AM from being used in the industrial setting extensively, despite its advantages. Therefore, a literature review on the process modelling approaches, their advantages and limitations was performed. The most frequently used process modelling approaches were reviewed and summarized with respect to the process modelling approach, scale and limitations. The different categories of process modelling approaches were compared, with molecular dynamics being a promising modelling technique that can be used in software applications. A new framework for modelling additive manufacturing processes based on molecular dynamics was proposed in this work, combining previously published manufacturing methodologies for the AM process, such as manufacturability, design and planning of the AM. A validation plan followed, with the main parameters and details highlighted. The proposed framework is offering a unique approach for modelling the AM process, based on parameters from the manufacturing design, planning and process. This framework will be used in software platforms for predicting temperature distributions and for optimizing shape and AM process.

Prediction of OLED temperature distribution based on a neural network model

Prediction of OLED temperature distribution based on a neural network model

Prediction of Temperature Distribution in Concrete under Variable Environmental Factors through a Three-Dimensional Heat Transfer Model.

Temperature distribution in concrete is significant to the concrete structure’s macro properties and different factors affect the heat transfer in concrete, and therefore influence the temperature distribution. This work established a three-dimensional transient heat transfer model coupled with various environmental factors, using the finite element method for calculating the results and real-measured data for testing accuracy. In addition, a sensitivity evaluation of various factors was conducted. Due to various environmental factors, the results revealed that the prediction of temperature distribution in concrete by the three-dimensional model had great accuracy with an error of less than 4%. A particular hysteresis effect of temperature response in the concrete existed. Considering heat transfer in different spatial directions, the model can predict the temperature change of each spatial point instead of the spatial surface in different depths, proving the shortcomings of a one-dimensional heat transfer model. A greater solar radiation intensity caused a more significant temperature difference on the concrete surface: the surface temperature difference in July was twice as significant as that in December. Wind speed had a cooling effect on the concrete surface, and stronger wind speed accompanied with a stronger cooling effect made the surface temperature closer to the ambient temperature. Material properties had different effects on the temperature distribution of the surface part and internal part: the specific heat capacity determined the speed of the outer layer temperature change while the thermal conductivity determined the speed of the inner layer temperature change.

Experimental and theoretical investigation of a novel system for progressive freeze-concentration based desalination process

This paper develops a novel vertical freezing system for an energy-efficient progressive freeze-concentration (PFC) based desalination process. Unlike the stirrer-based PFC process, we develop and provide proof-of-concept for a PFC system wherein the liquid precooling removes the latent heat of freezing gained by the liquid during its crystallization. In addition, the liquid circulation substitutes the mechanical stirrer. It was found that the required coolant temperature in the proposed system is increased by 55% compared to conventional vertical freezing apparatus, and thus saving energy consumed and cost by 30.1%. A parametric study was carried out to study the effect of liquid circulation flow rate and initial salinity of the liquid on the salinity of thawed ice and the salinity of the liquid. A mathematical model was developed to study the PFC-based desalination process. The solute redistribution during the process was predicted by using Scheil equation. The predicted temperature distribution in the ice, thickness of the ice, and salinity of thawed ice agree well with experimental values. The proposed heat and mass transfer model can predict the maximum duration of the process to tap ice as potable or as irrigation water, thus enabling batch process design.

Study on temperature distribution prediction of horizontal wells during fracturing treatment

In recent years, fiber-optic distributed temperature sensors (DTS) have been widely used for the dynamic monitoring of horizontal well during fracturing treatments. It is proposed to address the frequent issues of unknown artificial fracture initiation location, unclear crack extension pattern, unknown fracturing fluid injection success, and difficult fracturing effect evaluation in the hydraulic fracturing of horizontal wells. The temperature prediction model is the basis for fracturing diagnosis based on DTS monitoring, however, the quantitative prediction of temperature distribution throughout the horizontal well fracturing process remains a challenge. In this paper, a set of temperature-distribution prediction model for multi-stage fracturing in horizontal wells is established, with various micro-heat effects taken into account, and the models are solved through coupling. The temperature distribution during multi-cluster fracturing, and the temperature distribution characteristics during staged fracturing of horizontal wells have been analyzed. Moreover, the influence of fracturing fluid displacement, formation filtration coefficient, fracture width, and height on temperature distribution has also been clarified. The findings of this study provide theoretical support for fracturing performance diagnosis, fracture initiation identification, and analysis of the whereabouts of fracturing fluids using DTS monitoring. It has a great significance on fracturing effect evaluation and fracturing design optimization for horizontal wells.

Development of a numerical multi-layer model of skin subjected to pulsed laser irradiation to optimise thermal stimulation in photorejuvenation procedure

Background and Objective: This paper presents the development of a 3D physics-based numerical model of skin capable of representing the laser-skin photo-thermal interactions occurring in skin photorejuvenation treatment procedures. The aim of this model was to provide a rational and quantitative basis to control and predict temperature distribution within the layered structure of skin. Ultimately, this mathematical and numerical modelling platform will guide the design of an automatic robotic controller to precisely regulate skin temperature at desired depths and for specific durations. Methods: The Pennes bioheat equation was used to account for heat transfer in a 3D multi-layer model of skin. The effects of blood perfusion, skin pigmentation and various convection conditions are also incorporated in the proposed model. The photo-thermal effect due to pulsed laser light on skin is computed using light diffusion theory. The physics-based constitutive model was numerically implemented using a combination of finite volume and finite difference techniques. Direct sensitivity routines were also implemented to assess the influence of constitutive parameters on temperature. A stability analysis of the numerical model was conducted. Results: Finally, the numerical model was exploited to assess its ability to predict temperature distribution and thermal damage via a multi-parametric study which accounted for a wide array of biophysical parameters such as light coefficients of absorption for individual skin layers and melanin levels (correlated with ethnicity). It was shown how critical is the link between melanin content, laser light characteristics and potential thermal damage to skin. Conclusions: The developed photo-thermal model of skin-laser interactions paves the way for the design of an automated simulation-driven photorejuvenation robot, thus alleviating the need for inconsistent and error-prone human operators.

Performance-Based Analysis in Evaluation of Safety in Car Parks under Electric Vehicle Fire Conditions

Even though electric vehicles (EV) were invented over a century ago, their popularity has grown significantly within the last 10 years due to the development of Li-ion battery technology. This evolution created an increase in the fire risk and hazards associated with this type of high-energy battery. This review focuses on lessons learned from electric vehicle fires and fire risk mitigation measures for passenger road vehicles partially or fully powered by Li-ion batteries. The paper presents EV fire risks, as well as historical car fires, published large-scale fire tests, and some proposed fire protection strategies in the aspect of electromobility safety for the future. Technical solutions for EV fire hazard mitigation are discussed, and methods of performance-based analysis and simulations for fire safety in car park evaluation are demonstrated. The Fire Dynamic Simulator (FDS) was used for the CFD simulations for the prediction of smoke dispersion and temperature distribution during an EV fire. The presented case study demonstrates how fire simulations could predict conditions for the safe evacuation of people and Fire Brigade intervention conditions in the case of an EV fire in a car park.

Optimal swirl number and equivalence ratio for minimizing the soot and NO emissions in turbulent methane combustion

The simultaneous effect of the swirl number (SN) and equivalence ratio on NO and soot emissions in turbulent methane–air combustion was numerically investigated. The realizable k-ε model has been applied for modeling turbulence and eddy dissipation (ED) and probability density function (PDF) models were adopted for chemical reaction. The general gradient diffusion hypothesis (GGDH), high order algebraic model (HOGGDH), and simple eddy diffusivity model have been applied for modeling the turbulent heat flux (THF) vector. The Brooke–Moss model has been employed to predict the soot particle quantities. Comparing the results of the different numerical models (PDF and EDM with algebraic THF) with available experimental data indicated that employing the second-order models significantly leads to the modification of predicting temperature distribution in EDM. However, due to the effect of the PDF model on soot modeling, the PDF model provides more accurate results. The numerical simulations have been performed for various SNs (SN = 0–1.33) and equivalence ratios (0.125–0.75). In all of equivalence ratios the flame temperature and pollutants emission (NO and soot) were strongly affected by the SN. Also, the SN has different effects on NO and soot emission. Finally, for each of NO and soot emission, a correlation relative to effective combustion factors such as SN and equivalence ratio was presented. It is found that by increasing the equivalence ratio, the NO emission increases with a power of 2.3, and soot emission decreases with a power of 8.7.

THE BEHAVIOR OF HEAT SINK-IMPINGEMENT COOLING WITH FLAT PLATE AND ARCED FINS MODELS

In this paper, a statistical analysis was applied to the numerical predictions of temperature distribution for the heat sinks. There are two types of heat sink with an array of impingement. The first type is a flat plate heat sink, and the second type is arcs-fins heat sinks. The second type category considers five models (A, B, C, D, and E). The shapes of fins were changed, but the thickness, distance between fins, and radius were held fixed for comparing and analyzing them depending upon the improvement of the fin geometry of heat sink. The heat sinks of the two types are subjected to multi impinging flow at different Reynolds numbers (7000-11000). Thermodynamic and hydraulic results were collected. The best model was calculated through a statistical analysis. The efficiency of an arcs-fin heat sink was superior to that of the flat plate heat sink. The findings of Model D were more appropriate than those of the other models. The concave arc near the heat sink's exit (model D) created better effect than the convex arc(model E), despite the fact that the (model D) shape fins being identical to (model E) shape fins (only rotated 180° at the same location). However, Descriptive Statistics manifested that in all situations, the mean temperature for (model D) is better than (model E). The results of comparison between the flat plate heat sink and models (D and E) evinced that the average heat sink temperaturein the suggested design reduced via 12%, 8%, while the (model E) decreased by 12%, 7% for Re (7000, 9000), respectively. In addition, the two models maintained the same percentage of (8% and 7%) improvement at Re (11000). The correlation coefficient between the flat plate and the arcs-fins heat sink for model B has the highest value (0.809), while model A has the lowest value of correlation (0.673).

An investigation on heat transfer analysis of Micro-Plasma arc welds using finite element method

Predicting temperature distribution and controlling cooling rate to mitigate welding defects and residual stresses using a high-density arc welding process is quite significant. Therefore, choosing the right process parameters is very vital. However, performing experimental trials and deciding process parameters is quite expensive and time-consuming. Hence, a numerical investigation has been put forth using two different heat source models using the Finite element-based method. The three-dimensional heat source model has been solved using COMSOL Multiphysics. The study unfolds the most suitable heat source model for calculating temperature distribution around weld bead for the micro-plasma arc welding process. This study also lays the foundation for further analysis of residual stress.

Prediction of Room Temperature and Airflow Distribution in Air Conditioning Guidance Systems

In this paper, we propose a new air conditioning system that predicts the indoor comfort distribution by computational fluid dynamics (CFD) and the Internet of Things (IoT), and incorporates human behavior patterns to adjust to the appropriate indoor comfort level. In this air conditioning system, the temperature as well as the airflow distribution in the room needs to be predicted and then a solution is proposed based on the prediction results. In this paper, we present the experimental model we used and the related experiments for the analysis.

Approach on prediction of indoor temperature distribution by combining POD, CRI and mobile sensors

In our previous study, a prediction method of indoor temperature distribution based on the Contribution Ratio of Indoor Climate (CRI) and finite air temperature collected by one mobile sensor has been proposed. However, the CRI fixed value hypothesis limits its application in practical situations. In this regard, this study proposes to introduce interpolation POD (proper orthogonal decomposition) method to obtain the dynamic distribution of CRI, so as to improve the prediction accuracy. As a case study, in a simplified CFD model with identified heat source conditions, based on the interpolation POD method, the reconstruction of each sub temperature field dominated by only one heat source under any air supply parameters was realized, and thus its CRI distribution could be calculated. Then, by combining the finite air temperature collected by the mobile sensor, the indoor temperature distribution was predicted. The results showed that the introduction of interpolation POD method is effective, which breaks the application limitations of CRI fixed value hypothesis on the proposed method, and further promotes its development potential in the direction of "real-time prediction and dynamic regulation" in practical application.

Air flow analysis of 20 hp three phase induction motor

The present work deals with steady state air flow analysis of electric motor having 20 hp rating running at 1450 rpm. The motor is being used to run the belt pull system to drive the exhaust fan in the industry. Air flow analysis of electric motor is carried out to predict temperature distribution over the motor. The modeling of the complete motor is done in CATIA. Meshing of whole geometry is done in ICEM CFD14.5. Results are obtained using FLUENT. In this research only copper losses and iron losses are taken into consideration based on the output obtained from electrical simulation software. The copper and iron losses are found out from electromagnetic analysis using Motorsolve software. Losses are treated as heat source or input to find out temperature distribution. To improve the accuracy, the computational fluid dynamics (CFD) analysis is performed by considering the air flow around casing and fins and thermal generation due to the losses. It is observed that there is a significant rise in temperature on casing and on fins for 30 °C ambient temperature.

Experimental studies on friction stir welding of aluminium alloy 5083 and prediction of temperature distribution using arbitrary Lagrangian–Eulerian-based finite element method

The present work focused on welding aluminium alloy 5083 using the friction stir welding process. Suitable welding process parameters were identified to fabricate a defect-free butt joint with a tool rotational speed of 1600 rpm, traverse speed of 20 mm/min and tilt angle of 3°. The microstructure at the nugget zone, thermo mechanically affected zone, heat-affected zone and base metal zone are examined. Mechanical properties of the weldment exhibited promising results with an average joint efficiency and hardness of 75.70% and 94.0 ± 5.0 vickers hardness, respectively. Fractography revealed ductile mode of failure in base and weld metal tensile samples. Furthermore, a 3D thermomechanical finite element model was utilized to simulate the friction stir welding process using the selected process parameters. Arbitrary Lagrangian–Eulerian-based model aided in predicting residual stress distributions and thermal history during the friction stir welding process.

Detailed modeling of aluminum particle combustion – From single particles to cloud combustion in Bunsen flames

A numerical model for aluminum cloud combustion which includes the effects of interphase heat transfer, phase change, heterogeneous surface reactions, homogeneous combustion, oxide cap growth and radiation within the Euler–Lagrange framework is proposed. The model is validated in single particle configurations with varying particle diameters. The combustion process of a single aluminum particle is analyzed in detail and the particle consumption rates as well as the heat release rates due to the various physical/chemical sub-models are presented. The combustion time of single aluminum particles predicted by the model are in very good agreement with empirical correlations for particles with diameters larger than 10 μm. The prediction error for smaller particles is noticeably reduced when using a heat transfer model that is capable of capturing the transition regime between continuum mechanics and molecular dynamics. The predictive capabilities of the proposed model framework are further evaluated by simulating the aluminum/air Bunsen flames of McGill University for the first time. Results show that the predicted temperature distribution of the flame is consistent with the experimental data and the double-front structure of the Bunsen flame is reproduced well. The burning rates of aluminum in both single particle and particle cloud configurations are calculated and compared with empirical correlations. Results show that the burning rates obtained from the present model are more reasonable, while the correlations, when embedded in the Euler–Lagrange context, tend to underestimate the burning rate in the combustion stage, particularly for the considered fuel-rich flames.