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This paper aims to discuss the inverse problems that arise in various practical heat transfer processes. The purpose of this paper is to provide an identification method for predicting the internal boundary conditions for thermal analysis of mechanical structure. A few examples of heat transfer systems are given to illustrate the applicability of the method and the challenges that must be addressed in solving the inverse problem.

In this paper, the thermal network method and the finite difference method are used to model the two-dimensional heat conduction inverse problem of the tube structure, and the heat balance equation is arranged into an explicit form for heat load prediction. To solve the matrix ill-conditioned problem in the process of solving the inverse problem, a Tikhonov regularization parameter selection method based on the inverse computation-contrast-adjustment-approach was proposed.

The applicability of the proposed method is illustrated by numerical examples for different dynamically varying heat source functions. It is proved that the method can predict dynamic heat source with different complexity.

The modeling calculation method described in this paper can be used to predict the boundary conditions for the inner wall of the heat transfer tube, where the temperature sensor cannot be placed.

This paper presents a general method for the direct prediction of heat sources or boundary conditions in mechanical structure. It can directly obtain the time-varying heat flux load and thtemperature field of the machine structure.

Accurate prediction of temperature distribution in an electrical machine at the design stage is becoming increasingly important. It is essential to know the locations and magnitudes of hot spot temperatures for optimum design of electrical machines. A methodology based on axi‐symmetric finite element formulation has been developed to solve the conduction‐convection problem in radial cooled machines using a new eight noded solid‐fluid coupled element. The axi‐symmetric model adopted is formulated purely from dimensional data, property data and published convective correlations. Steady state temperatures have been determined for 102 kW radial cooled motor at 100 percent and 75 percent loads and are validated with experimental results obtained from heat run tests. Parametric studies have been carried out to study the effect of critical parameters on temperature distribution and for optimising the design.

The purpose of this paper is to optimize the configuration sizes of micro‐channel cooling heat sink using the thermal resistance network model. The optimized micro‐channel heat sink is simulated by computational fluid dynamics method, and the total thermal resistance is calculated to compare with that of thermal resistance network model.

Taking the thermal resistance and the pressure drop as goal functions, a multi‐objective optimization model was proposed for the micro‐channel cooling heat sink based on the thermal resistance net work model. The Sequential Quadratic Programming procedure was used to do the optimization design of the structure size of the micro‐channel. The optimized micro‐channel heat sink was numerically simulated by computational fluid dynamics (CFD) software.

For the heat sink to cool a chip with the sizes of L × W = 2.5 mm × 2.5 mm and the power of 8 W, the optimized width and height of the micro‐channel are 154 μm and 1,000 μm, respectively, and its corresponding total thermal resistance is 8.255 K/W. According to the simulation results, the total thermal resistance of whole micro‐channel heat sink R_total is 7.596 K/W, which agrees well with the analysis result of thermal resistance network model.

The convection heat transfer coefficient is calculated approximatively here for convenience, and that may induce some errors. Originality/value –The maximum difference in temperature of the optimized micro‐channel cooling heat sink is 59.064 K, which may satisfy the requirement for removal of high heat flux in new‐generation chips.

To determine the optimal dimensions for a stacked micro‐channel using the genetic algorithms (GAs) under different flow constraints.

GA is used as an optimization tool for optimizing the thermal resistance of a stacked micro‐channel under different flow constraints obtained by using the one dimensional (1D) and two dimensional (2D) finite element methods (FEM) and by thermal resistance network model as well (proposed by earlier researcher). The 2D FEM is used to study the effect of two dimensional heat conduction in the micro‐channel material. Some parametric studies are carried out to determine the resulting performance of the stacked micro‐channel. Different number of layers of the stacked micro‐channel is also investigated to study its effect on the minimum thermal resistance.

The results obtained from the 1D FEM analysis compare well with those obtained from the thermal resistance network model. However, the 2D FEM analysis results in lower thermal resistance and, therefore, the importance of considering the conduction in two dimensions in the micro‐channel is highlighted.

The analysis is valid for constant properties fluid and for steady‐state conditions. The top‐most surfaces as well as the side surfaces of the micro‐channel are considered adiabatic.

The method is very useful for practical design of micro‐channel heat‐sinks.

FEM analyses of stacked micro‐channel can be easily implemented in the optimization procedure for obtaining the dimensions of the stacked micro‐channel heat‐sinks for minimum thermal resistance.

The purpose of this paper is to show how, with a view to the shortcomings of traditional optimization methods, a multi‐objective optimization concerning the structure sizes of micro‐channel heat sink is performed by adaptive genetic algorithm. The optimized micro‐channel heat sink is simulated by computational fluid dynamics (CFD) method, and the total thermal resistance is calculated to compare with that of thermal resistance network model.

Taking the thermal resistance and the pressure drop as goal functions, a multi‐objective optimization model was proposed for the micro‐channel cooling heat sink based on the thermal resistance network model. The coupled solution of the flow and heat transfer is considered in the optimization process, and the aim of the procedure is to find the geometry most favorable to simultaneously maximize heat transfer while obtaining a minimum pressure drop. The optimized micro‐channel heat sink was numerically simulated by CFD software.

The results of optimization show that the base convection thermal resistance contributes to maximum the total thermal resistance, and base conduction thermal resistance contributes to least. The width of optimized micro‐channel and fin are 197 and 50 μm, respectively, and the corresponding total thermal resistance of the whole micro‐channel heat sink is 0.838 K/W, which agrees well with the analysis result of thermal resistance network model.

The convection heat transfer coefficient is calculated approximately here for convenience, and that may induce some errors.

The maximum difference in temperature of the optimized micro‐channel cooling heat sink is 84.706 K, which may satisfy the requirement for removal of high heat flux in new‐generation chips. The numerical simulation results are also presented, and the results of numerical simulation show that the optimized micro‐channel heat sink can enhance thermal transfer performance.

This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance.

A total of 2,160 computational fluid dynamics simulation cases over up to 2,000 W/mK are conducted to regress big data and predict a wider range of effective thermal conductivity up to 10,000 W/mK. The deep neural networking is trained with reinforcement learning from 10–12 steps minimizing errors in each step. Another 8,640 CFD cases are used to validate.

Experimental, simulational and theoretical approaches are used to validate the DNN estimation for the same independent variables. The results from the two approaches show a good agreement with each other. In addition, the DNN method required less time when compared to the CFD.

The DNN method opens a new way to secure data while predicting in a wide range without experiments or simulations. If these technologies can be applied to thermal and materials engineering, they will be the key to solve thermal obstacles that many longing to overcome.

This paper aims to develop the thermal resistance network model based on the heat dissipation paths from the multi-die stack to the ambient and takes into account the composite effects of the thermal spreading resistance and one-dimensional (1D) thermal resistance. The thermal spreading resistance comprises majority of the thermal resistance when heat flows in the horizontal direction of a large plate. The present study investigates the role of determining the temperature increase compared to the thermal resistances intrinsic to the 3D technology, including the thermal resistances of bonding layers and through silicon vias (TSVs).

This paper presents an effective method that can be applied to predict the thermal failure of the heat source of silicon chips. An analytical model of the 3D integrated circuit (IC) package, including the full structure, is developed to estimate the temperature of stacked chips. Two fundamental theories are used in this paper – Laplace’s equation and the thermal resistance network – to calculate 1D thermal resistance and thermal spreading resistance on the 3D IC package.

This paper provides a comprehensive model of the 3D IC package, thus improving the existing analytical approach for predicting the temperature of the heat source on the chip for the 3D IC package.

Based on the aforementioned shortcomings, the present study aims to determine if the use of an analytical resistance model would improve the handling of a temperature increase on the silicon chips in a 3D IC package. To achieve this aim, a simple rectangular plate is utilized to analyze the temperature of the heat source when applying the heat flux on the area of the heat source. Next, the analytical model of a pure plate is applied to the 3D IC package, and the temperature increase is analyzed and discussed.

The main contribution of this paper is the use of a simple concept and a theoretical resistance network model to improve the current understanding of thermal failure by redesigning the parameters or materials of a printed circuit board.

In this paper, an analytical model of a 3D IC package was proposed based on the calculation of the thermal resistance and the analysis of the network model.

The aim of this work was to estimate the mean temperature of the silicon chips and understand the heat convection paths in the 3D IC package. The results reveal these phenomena of the complete structure, including TSV and bump, and highlight the different thermal conductivities of the materials used in creating the 3D IC packages.

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This paper aims to use a scaling approach to scale the solutions of a beforehand-simulated finite element (FE) solution of an induction machine (IM). The scaling procedure is coupled to an analytic three-node-lumped parameter thermal network (LPTN) model enabling the possibility to adjust the machine losses in the simulation to the actual calculated temperature.

The proposed scaling procedure of IMs allows the possibility to scale the solutions, particularly the losses, of a beforehand-performed FE simulation owing to temperature changes and therefore enables the possibility of a very general multiphysics approach by coupling the FE simulation results of the IM to a thermal model in a very fast and efficient way. The thermal capacities and resistances of the three-node thermal network model are parameterized by analytical formulations and an optimization procedure. For the parameterization of the model, temperature measurements of the IM operated in the 30-min short-time mode are used.

This approach allows an efficient calculation of the machine temperature under consideration of temperature-dependent losses. Using the proposed scaling procedure, the time to simulate the thermal behavior of an IM in a continuous operation mode is less than 5 s. The scaling procedure of IMs enables a rapid calculation of the thermal behavior using FE simulation data.

The approach uses a scaling procedure for the FE solutions of IMs, which results in the possibility to weakly couple a finite element method model and a LPTN model in a very efficient way.

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