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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.

This paper aims to study a disturbance rejection controller to improve the anti-interference capability and the position tracking performance of airborne radar stabilized platform that ensures the stability and clarity of synthetic aperture radar imaging.

This study proposes a disturbance rejection control scheme for an airborne radar stabilized platform based on the active disturbance rejection control (ADRC) inverse estimation algorithm. Exploiting the extended state observer (ESO) characteristic, an inversely ESO is developed to inverse estimate the unmodeled state and extended state of the platform system known as total disturbances, which greatly improves the estimation performance of the disturbance. Then, based on the inverse ESO result, feedback the difference between the output of the tracking differentiator and the inverse ESO result to the nonlinear state error feedback controller (NLSEF) to eliminate the effects of total disturbance and ensure the stability of the airborne radar stabilized platform.

Simulation experiments are adopted to compare the performance of the ADRC inverse estimation algorithm with that of the proportional integral derivative controller which is one of the mostly applied control schemes in platform systems. In addition, classical ADRC is compared as well. The results have shown that the ADRC inverse estimation algorithm has a better disturbance rejection performance when disturbance acts in airborne radar stabilized platform, especially disturbed by continuous airflow under some harsh air conditions.

The originality of this paper is exploiting the ESO characteristic to develop an inverse ESO, which greatly improves the estimation performance of the disturbance. And the ADRC inverse estimation algorithm is applied to ameliorate the anti-interference ability of the airborne radar stabilization platform, especially the ability to suppress continuous interference under complex air conditions.

This study aims to promote the anti-disturbance and tracking accuracy of optoelectronic stabilized platforms, which ensure that optical detectors accurately track targets and acquire high-quality images.

An improved active disturbance rejection control (ADRC) strategy based on model-assisted double extended state observers (MDESOs) is proposed in this paper. First, by establishing an auxiliary model, the total disturbances are separated into two parts: inner and external disturbances. Then, MDESOs are designed to estimate the two parts by separately using two parallel ESOs, by which the controlled plant is adjusted to the ideal pure integral series. Simultaneously, combined with the nonlinear state error feedback, an overall control strategy is established.

Compared with the conventional ADRC and proportional derivative, the improved ADRC (IADRC) has stronger robustness and adaptability and effectively reduces the requirements for model accuracy and the gain of the ESO. The error of the auxiliary model is tolerated to exceed 50%, and the parameter values of the MDESOs are reduced by 90%.

The total disturbance rejection rate of the proposed strategy is only 3.11% under multiple disturbances, which indicates that the IADRC strategy significantly promotes anti-disturbance performance.

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.

The purpose of this paper is to present an efficient algorithm based on a multi‐level adaptive mesh refinement strategy for the solution of ill‐posed inverse heat conduction problems arising in pool boiling using few temperature observations.

The stable solution of the inverse problem is obtained by applying the conjugate gradient method for the normal equation method together with a discrepancy stopping rule. The resulting three‐dimensional direct, adjoin and sensitivity problems are solved numerically by a space‐time finite element method. A multi‐level computational approach, which uses an a posteriori error estimator to adaptively refine the meshes on different levels, is proposed to speed up the entire inverse solution procedure.

This systematic approach can efficiently solve the large‐scale inverse problem considered without losing necessary detail in the estimated quantities. It is shown that the choice of different termination parameters in the discrepancy stopping conditions for each level is crucial for obtaining a good overall estimation quality. The proposed algorithm has also been applied to real experimental data in pool boiling. It shows high computational efficiency and good estimation quality.

The high efficiency of the approach presented in the paper allows the fast processing of experimental data at many operating conditions along the entire boiling curve, which has been considered previously as computationally intractable. The present study is the authors' first step towards a systematic approach to consider an adaptive mesh refinement for the solution of large‐scale inverse boiling problems.

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 paper is to present research in the area of the applications of the generalized inverse matrix in IP traffic matrix.

Traffic matrices are important for many network design, engineering, and management functions. However, they are often difficult to measure directly. Because networks are dynamic, analysis tools must be adaptive and computationally lightweight. In order to manage the whole network, a novel calculating model is proposed based on the generalized inverse matrix. In this model, a generalized inverse matrix is introduced to resolve the traffic matrix equation. But if so, the error is raised. In order to improve the method, an original traffic matrix is estimated according to the prior, for example, Poisson model. To acquire the optimized solutions, linear programming is introduced. Through both theoretical analysis and simulating results, it is shown that the proposed algorithm achieves better performance than the existing representative methods.

This paper illustrates the useful information that can be obtained using generalized inverse matrix for incomplete data estimation.

The use of generalized inverse matrix was a very effective method to calculate IP traffic matrix.

The algorithms discussed in the paper can be used to estimate solutions of an ill‐posed linear inverse equation.

The paper is of value in proposing an estimation method for IP traffic matrix using generalized inverse matrix.

To provide a new and simple inverse rotor time constant identification method which can be used to update an indirect rotor field oriented controlled (IRFOC) induction motor algorithm.

Two different equations are used to estimate the rotor flux in the stator reference frame. One of the equations is a function of the rotor time constant, rotor angular velocity and the stator currents. The other equation is a function of measured stator currents and voltages. The equation that uses the voltage and the current signals of the stator serves as reference model, however, the other equation works as an adjustable model with respect to the variation of the rotor time constant. Voltage signals used in the reference model equation are obtained from the measured DC bus voltage and the inverter gating signals. The proposed scheme is verified using a MATLAB/SIMULINK model for two different motors and experimentally using a DSP development tool (MCK 243) supplied by Technosoft S.A.

The proposed estimator was able to successfully track the actual value of the inverse rotor time constant for different load torque and speed operating conditions. Increased oscillations in the estimated inverse rotor time constant appeared at lower speeds (below 10 per cent of rated speed) due to drift in a PI regulator (used at the estimator side), which was tuned under rated operating conditions and using parameters nominal values.

This estimation scheme is limited when near zero speed operation is demanded; otherwise it gives a simple and practical solution. A suggested way out of this, is to provide a self‐tuning controller that can automatically adjust even for zero speed operation, or to automatically disconnect the estimator and take the most updated value as long as the operating speed is below a predetermined value.

This paper presented a new inverse rotor time constant estimator for an IRFOC induction motor application and in conjunction rotor flux was estimated without voltage phase sensors.

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.

In principle, it is possible to apply genetic algorithms (GAs) to the solution of inverse problems in the simulation of manufacturing processes. In this context, an inverse problem can be stated as ‘knowing the desired output of a process, what combination of process parameters are required for its achievement?’. Since the simulation of many processes requires the simulation of thermal, solids and/or fluids problems, the application of GAs to inverse process modelling depends on their ability to solve a wide range of inverse field problems. This paper has two major objectives: (a) to demonstrate the application of a GA to a simple inverse thermal field problem, and (b) to compare its performance against a relatively mature technique for the solution of such problems. The results of this study indicate that, despite the relatively large computational cost of GAs, their accuracy and robustness warrants further investigation of their performance in more demanding applications.