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The optimal control of the steady‐state temperature distribution in radiating panels using control heat sources is considered. The problem has important applications in the thermal control of space structures. A mathematical model leads to an elliptic nonlinear optimal control problem. A numerical optimal control method, based on finite element (FE) discretization and sequential quadratic programming (SQP), is employed. Results are presented for some specific examples.

On-orbit assembly technology is a promising research topic in spaceflight field. For purposes of studying the dynamic performance and reducing weight of an on-orbit assembly satellite structure frame, this paper aims to propose a structural optimization design method based on natural frequency.

The dynamic stability of the satellite under working condition depends on the mechanical properties of the structure matrix. A global structural optimization model is established, with the objective of mass minimization and the constraints of given natural frequencies and given structure requirements. The structural optimization and improvement design method is proposed using sequential quadratic programming calculation.

The optimal result of objective function is effectively obtained, and the best combination of structural geometric parameters is configurated. By analyzing the relationship between the structural variables and optimization parameters, the primary and secondary factors to the mass optimization process of the microsatellite satisfying the dynamic performance requirements are obtained, which improves the effectiveness and accuracy of the system optimization design.

This method can coordinate the relation between satellite vibration stability and weight reduction, which provides an effective way for the optimization design of on-orbit assembly microsatellite. It has reference significance for the similar spacecraft framework structure design.

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.

The purpose of this paper is to present numerical experimentation of three constraint detection methods to explore their main features and drawbacks in infeasibility detection during the design process.

Three detection methods (deletion filter, additive method and elasticity method) are used to find the minimum intractable subsystem of constraints in conflict. These methods are tested with four enhanced NLP solvers (sequential quadratic program, multi-start sequential quadratic programing, global optimization solver and genetic algorithm method).

The additive filtering method with both the multistart sequential quadratic programming and the genetic algorithm solvers is the most efficient method in terms of computation time and accuracy of detecting infeasibility. Meanwhile, the elasticity method has the worst performance.

The research has been carried out for only inequality constraints and continuous design variables. This research work could be extended to develop computer-aided graphical user interface with the capability of including equality constraints and discrete variables.

These proposed methods have great potential for finding and guiding the designer to detect the infeasibility for ill-posed complex design problems.

The application of the proposed infeasibility detection methods with their four enhanced solvers on several mechanical design problems reduces the number of constraints to be checked from full set to a much smaller subset.

Static converters generate current harmonics in grids. Numerous studies on analytical frequency models of converters are often required to carry out their harmonic modeling in the context of sizing by optimization. Some formulations are proposed to solve such models. Each formulation has its own advantages and drawbacks. The paper mainly focuses on two formulations: the first to be solved by Sequential Quadratic Programming (SQP) and the second to be solved by Newton-Raphson (NR). In this way, the paper presents the performances of each formulation and compares the results of both formulations for the modeling of a single-phase diode rectifier.

The paper aims to compare SQP formulation and NR formulation, and to propose the ways to improve their convergence. In the modeling, by using an explicit formulation of the state variables combined to a numerical method, equations are defined to reduce, as far as possible, the number of unknowns.

The difficulty is to find the good operating mode of the static converter. So, outside the equations and the constraints, the paper proposes to use the eigenvalues of the state space matrixes to initialize the duration of every configuration and to consider the operating symmetries of the static converter that allow to reduce the research area and also the variables calculated.

The number of the conducting phase per half period is a priori, as the operating mode.

The modeling is based on the use of linear components, ideal switches and the static converter operates in steady-state. The main difficulties are to formulate the equations representing the non-controlled switching of semiconductors, and to solve them.

A new method of designing electromagnetic devices especially high‐voltage ones, is presented. As a result of applying a sequential quadratic programing algorithm and a very effective algorithm for non‐linear minimax optimization, a flexible method for computer aided design of high‐voltage and semiconductor devices has been obtained. The minimax algorithm is based on a successive linear approximation of the functions defining the problem. In each iteration step these functions are computed with the aid of the finite element method. The resulting linear subproblems are solved in the minimax sense subject to the linear equality and inequality constraints. Applications of these two methods for the designing some parts of the high‐voltage devices are presented.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

The purpose of this paper is to optimize the key dimensions parameters of the missile suspension structure to ensure the structural fatigue life (>10000 cycles) with the reliability of 0.995.

The design objective is the fatigue life reliability of the structure, while the design variables are the four fatigue‐sensitive dimensions. The nominal stress approach is introduced to predict the fatigue life, and it was verified by comparing with experimental data. The second respond surface method is applied to solve the reliability in a finite element‐supported analysis using software MSC Patran/Nastran. A Sequential quadratic programming (SQP) algorithm is used for structure optimization.

The fillet radius r is the most important factor that influences the fatigue life reliability of the structure. The four optimal dimensions parameters are obtained by a reliability‐based design optimization process with the fatigue life and reliability fulfilling the demands.

The optimal result can be used as the design values for missile suspension structure. The feasibility of the reliability‐based design optimization method is validated for the design of missile suspension structure.

In this work, the design problem of hydrodynamic plain journal bearings is formulated as a multi-objective optimization problem to improve bearing performance under different operating conditions.

The problem is solved using a hybrid approach combining genetic algorithm and sequential quadratic programming. The selected state variables are oil leakage flow rate, power loss and minimum oil film thickness. The selected design variables are the radial clearance, length-to-diameter ratio, oil viscosity, oil supply pressure and oil supply groove angular position. A validated empirical model is adopted to provide relatively accurate estimation of the bearing state variables with reduced computations. Pareto optimal solution sets are obtained for different operating conditions, and secondary selection criteria are proposed to choose a final optimum design.

The adopted hybrid optimization approach is a random search algorithm that generates a different solution set for each run, thus a different bearing design. For a number of runs, it is found that the key design variables that significantly affect the optimum state variables are the bearing radial clearance, oil viscosity and oil supply pressure. Additionally, oil viscosity is found to represent the significant factor that distinguishes the optimum designs obtained using the implemented secondary selection criteria. Finally, the results of the proposed optimum design framework at different operating conditions are presented and compared.

The proposed multi-objective formulation of the bearing design problem can provide engineers with a systematic approach and an important degree of flexibility to choose the optimum design that best fits the application requirements.

This investigation applied a hybrid method combining a trained artificial neural network (ANN) and the sequential quadratic programming (SQP) method to determine an optimal parameter setting for a reflow soldering process of ball grid array packages in printed circuit boards.

Nine experiments based on an orthogonal array table with three‐controlled inputs and average shear forces of solder spheres as a quality target were utilized to train the ANN and then the SQP method was implemented to search for an optimal setting of parameters.

The ANN can be utilized successfully to predict the shear force under different reflow soldering conditions after being properly trained and the identified optimal parameter setting are capable of striking the balance between the average shear forces and the manufacturing cycle time.

The reflow time and the peak temperature were found to be the most significant factors for the reflow process via analysis of variance.

This study provided an algorithm integrating a black‐box modeling approach (i.e. the ANN predictive model) with the SQP method to resolve an optimization problem. This algorithm offered an effective and systematic way to identify an optimal setting of the reflow soldering process. Hence, the efficiency of designing the optimal parameters was greatly improved.