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Inverse problems deal with determining the causes on the basis of knowing their effects. The object of the inverse parameter estimation problem is to fix the thermal material parameters (the cause) on the strength of a given observation of the temperature history at one or more interior points (the effect). This paper demonstrates two novel approaches to the inverse problems. These approaches use two artificial intelligence mechanisms: neural network and genetic algorithm. Examples shown in this paper give a comparison of results obtained by both of these methods. The numerical technique of neural networks evolved from the effort to model the function of the human brain and the genetic algorithms model the evolutional process of nature. Both of the presented approaches can lead to a solution without having problems with the stability of the inverse task. Both methods are suitable for parallel processing and are advantageous for a multiprocessor computer architecture.

A method of estimating a heat transfer coefficient at the surface of a solid body is described. Knowing the ambient temperature and the temperature history at an inner point (points) of the body, the heat transfer coefficient is computed. The inverse algorithm can respect the non‐linear nature of the task. The inverse algorithm is based on the computation of the temperature fields. Any method for unsteady state heat conduction can be used. The influence of the random errors of the input experimental data is described.

The purpose of this study is to describe the combustion of a magnesium particle falling into a hot oxidizer medium.

The governing equations, including mass, momentum and energy conservation equations, are numerically solved. Afterward, the influences of effective parameters on the temperature distribution and burning time are investigated. Artificial neural network (ANN) is applied to approximate the particle temperature as a function of time, diameter and porosity factor. To obtain the best arrangement of the ANN structure, an optimization process is conducted.

The results show that by considering variations of the particle size, the maximum temperature increases compared to the case in which the particle diameter is constant. Also, the ignition and burning times and the maximum temperature of the moving particle are lower than those of the motionless particle. Optimum network has the best values of regression coefficient and mean relative error whose values are found to be 0.99991 and 1.58 per cent, respectively.

In this study, particle size varies over the combustion process that leads to calculation of particle burning time. In addition, the effects of the motion and porosity of the particle are examined.

Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures.

A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO.

The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms.

Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS.

Thermal conduction anisotropy, which is defined by the dependency of thermal conductivity on direction, is an important parameter in many engineering and research studies such as the design of nuclear waste depositional sites. In this context, the authors aim to investigate the effect of grain shape in thermal conduction anisotropy using pore scale modeling that utilizes real shapes of grains, pores and throats to characterize petrophysical properties of a porous medium.

The authors generalize the swelling circle approach to generate porous media composed of randomly arranged but regularly oriented elliptical grains at various grain ratios and porosities. Unlike previous studies that use fitting parameters to capture the effect of grain–grain thermal contact resistance, the authors apply roughness to grains’ surface. The authors utilize Lattice Boltzmann method to solve steady state heat conduction through medium.

Based on the results, when the temperature field is not parallel to either major or minor axes of grains, the overall heat flux vector makes a “deviation angle” with the temperature field. Deviation angle increases by augmenting the ratio of thermal conductivities of solid to fluid and the aspect ratios of grains. In addition, the authors show that porosity and surface roughness can considerably change the anisotropic properties of a porous medium whose grains are elliptical in shape.

The authors developed an algorithm for generation of non-circular-based porous medium with a novel approach to include grain surface roughness. In previous studies, the effect of grain contacts has been simulated using fitting parameters, whereas in this work, the authors impose the roughness based on the its fractal geometry.

The application of laser and optical technologies in the industry is wide and extensive; the development and application of laser and optical technologies have become a promising research domain. However, most existing studies have focused on the technical aspects or the application aspects; these studies have not highlighted the technology distribution and application development of laser and optical technologies from the big picture. Additionally, the manner in which the research and development (R&D) results of universities correspond to the needs of enterprises and industry has become a topic of concern for the public. Therefore, this study aims to adopt the academic patents as the basis for analysis and to construct a laser and optical technology network.

Therefore, in the current study, the researchers have analyzed relevant academic patent technology networks, using academic patents of laser and optical technologies as a basis of analysis.

The study results indicated that the key technologies mainly lie in nanostructures, metal-working, material analysis and semiconductor devices. Additionally, these technologies are mainly applied in industries, such as optics, medical technology, pharmaceuticals, biotechnology and organic fine chemistry; this indicated that a large proportion of academia’s R&D outcomes are applied in these industries.

In this study, the researchers have constructed a technology network model to explore the technical development direction of laser and optical technologies; the results of the current study could serve as a reference for universities and industry for allocation of R&D resources.

This study investigates the role of “soft” factors of total quality management – in terms of empowerment and engagement of employees – in facilitating or hindering organizational performance of the university technology transfer offices.

The authors developed an Ordinary Least Squares (OLS), multiple regression model to test if empowerment and engagement affect organizational performance of the university technology transfer offices.

The authors found that “soft” factors of total quality management – in terms of empowerment and engagement – facilitate the improvement of organizational performance in university technology transfer offices.

The authors’ analysis shows that soft total quality management practices create the conditions for improving organizational performance. This study provides practical implications by showing that, in the evaluation of the technology transfer office, not only the “hard” variables (e.g. number of employees and employee experience) but also the “soft” one (e.g. empowerment and engagement) matter. Therefore, university technology transfer managers or university technology transfer delegates should take actions to promote not only empowering employees but also create a climate conducive to employees' engagement in the university technology transfer offices.

With regards to the differences in organizational performances of university technology transfer offices, several studies have focused their attention on technology transfer professionals in technology transfer offices, but only a few of them have examined the “soft side” of total quality management. Thus, this study examines the organizational goals of technology transfer offices through “soft” factors of total quality management in terms of empowerment and engagement employees.

This paper aims to considered the problem of identification of temperature-dependent thermal conductivity in the nonstationary, nonlinear heat equation. To describe the heat transfer in the furnace charge occupied by a homogeneous porous material, the heat equation is formulated. The inverse problem consists in finding the heat conductivity parameter, which depends on the temperature, from the measurements of the temperature in fixed points of the material.

A numerical method based on the finite-difference scheme and the least squares approach for numerical solution of the direct and inverse problems has been recently developed.

The influence of different numerical scheme parameters on the accuracy of the identified conductivity coefficient is studied. The results of the experiment carried out on real measurements are presented. Their results confirm the ones obtained earlier by using other methods.

Novelty is in a new, easy way to identify thermal conductivity by known temperature measurements. This method is based on special finite-difference scheme, which gives a resolvable system of algebraic equations. The results sensitivity on changes in the method parameters was studies. The algorithms of identification in the case of a purely mathematical experiment and in the case of real measurements, their differences and the practical details are presented.

The main aim of this paper is to utilize the different forms of functions for the numerical solution of the two‐dimensional (2‐D) inverse heat conduction problem with temperature‐dependent thermo‐physical properties (TDTPs).

The proposed numerical technique is based on the modified elitist genetic algorithm (MEGA) combined with finite different method (FDM) to simultaneously estimate temperature‐dependent thermal conductivity and heat capacity. In this paper, simulated (noisy and filtered) temperatures are used instead of experimental data. The estimated temperatures are obtained from the direct numerical solution (FDM) of the 2‐D conductive model by using an estimate for the unknown TDTPs and MEGA is used to minimize a least squares objective function containing estimated and simulated (noisy and filtered) temperatures.

The accuracy of the MEGA is assessed by comparing the estimated and the pre‐selected TDTPs. The results show that the measurement errors do not considerably affect the accuracy of the estimates. In other words, the proposed method provides a practical and confident prediction in simultaneously estimating the temperature‐dependent heat capacity and thermal conductivity. From the results, it is found that the RMS error between estimated and simulated temperatures is smaller for linear simulation and also we found this form convenient for parameters estimations.

Future approaches should find the optimal design of case study and then apply the proposed method to achieve the best results.

Applications of the results presented in this paper can be of value in practical applications in parameter estimation even with one sensor temperature history.