Topology optimization for minimum compliance using a control strategy

A robust topological derivative-based multi-material optimization approach: Optimality condition and computational algorithm

Variational approach to relaxed topological optimization: Closed form solutions for structural problems in a sequential pseudo-time framework

SummaryIn this work, an asymptotically concentrated topology optimization method based on the solid isotropic material with logistic function interpolation is proposed. The asymptotically concentrated method is introduced into the process of optimization cycle after updating the design variables. At the same time, with the use of the solid isotropic material with logistic function interpolation, all the candidate densities are reasonably polarized, relying on the characteristic of the interpolation curve itself. The asymptotically concentrated method can effectively suppress the generation of intermediate density and speed up the process of updating the design variables, hence improving the optimization efficiency. Moreover, the above polarization can weaken the influence of low‐related‐density elements and enhance the influence of high‐related‐density elements. For the single‐material topology optimization problem, gray‐scale elements can be effectively eliminated, and clear boundary and smaller compliance can be obtained by this method. For the multimaterial topology optimization problem, minimum compliance with high efficiency can be achieved by this method. The proposed method mainly includes the following advantages: concentrated density variables, reasonable interpolation, high computational efficiency, and good topological results.

An approach for the concurrent homogenization-based microstructure type and topology optimization problem

This paper presents an efficient and comprehensive MATLAB code to solve two-dimensional structural topology optimization problems, including minimum mean compliance, compliant mechanism synthesis and multi-load compliance problems. The Unsmooth Variational Topology Optimization (UNVARTOP) method, developed by the authors in a previous work, is used in the topology optimization code, based on the finite element method (FEM), to compute the sensitivity and update the topology. The paper also includes instructions to improve the bisection algorithm, modify the computation of the Lagrangian multiplier by using an Augmented Lagrangian to impose the constraint, implement heat conduction problems and extend the code to three-dimensional topology optimization problems. The code, intended for students and newcomers in topology optimization, is included as an appendix (Appendix A) and it can be downloaded from https://github.com/DanielYago together with supplementary material.

In this study, the binary bat algorithm (BBA) for structural topology optimization is implemented. The problem is to find the stiffest structure using a certain amount of material and some constraints using the bit-array representation method. A new filtering algorithm is proposed to make BBA find designs with no separated objects, no checkerboard patterns, less unusable material, and higher structural performance. A volition penalty function for topology optimization is also proposed to accelerate the convergence toward the optimal design. The main effect of using the BBA lies in the fact that the BBA is able to handle a large number of design variables in comparison with other well-known metaheuristic algorithms. Based on the numerical results of four benchmark problems in structural topology optimization for minimum compliance, the following conclusions are made: (1) The BBA with the proposed filtering algorithm and penalty function are effective in solving large-scale numerical topology optimization problems (fine finite elements mesh). (2) The proposed algorithm produces solid-void designs without gray areas, which makes them practical solutions that are applicable in manufacturing.

The worldwide direction moving towards sustainable energy solutions requires more advanced control mechanisms within power systems, especially in DC microgrids that combine renewable energy sources like solar panels and wind turbines with conventional power setups. This paper addresses the necessary challenge of maintaining steady and efficient DC voltage control—important for the dependability and performance of power plants. Specifically, it looks at the efficiency of four different control strategies: classic PID (Proportional-Integral-Derivative) controllers, Fuzzy Logic Controllers (FLC), Fractional Order PID (FOPID) controllers, and a new mixed system that merges Fuzzy Logic with PID controllers in a Fuzzy-PI layout. Our approach utilizes a strict comparative analysis using MATLAB Simulink simulations, assessing each controller’s capacity to manage dynamic load shifts, adjust to the unpredictability of renewable energy inputs, and preserve strong voltage stability under various operational cases. The simulations are crafted to single out the most effective control strategy to boost efficiency and toughness in microgrid operations. This study adds substantially to the progress of control strategies in hybrid energy systems, offering important insights into the creation of more complex and reliable power management solutions. By evaluating the performance of these varied controllers, we aim to underline potential upgrades in control systems that could support better energy management in intricate grid setups, thereby encouraging the wider inclusion of renewable energy technologies into power grids.

The high-quality operation of a superheated steam temperature (SST) system is a core fact of the safety, economy, and stability of thermal power units. How to improve the control performance of an SST system under large-varying operating conditions is becoming a research hotspot. To solve this challenge, this paper proposes a proportional integral derivative (PID) control strategy based on integral gain scheduling. Based on the introduction of the SST system and classical model under typical operating conditions, the control difficulties of the SST system are analyzed theoretically. Then, a PID control strategy, based on integral gain scheduling, is introduced for the cascade control structure, and the stability of the proposed control strategy is analyzed by calculating the PID stability region. Finally, the effectiveness of the proposed method is verified under nominal and uncertain conditions, where the proposed method could obtain satisfactory tracking and disturbance rejection control performance. Simulation results show the valuable application prospects of the proposed method.

In this work, topology optimization is used to optimize the compliance or eigenvalues of prestressed plates. The prestress is accounted for by including the force equivalent to the prestressing and adding the initial stress stiffness matrix to the original stiffness matrix. The calculation of the sensitivities is complicated because of the initial stress stiffness matrix, but the computational cost can be kept low by using the adjoint method. The topology optimization problem is solved using the solid isotropic material with penalization (SIMP) method in combination with method of moving asymptotes (MMA). Numerical examples of minimizing compliance or maximizing the first eigenvalue of plates are given. One example is a micro electro mechanical systems (MEMS) application involving a backplate used in a microphone in a hearing aid. Copyright © 2001 John Wiley & Sons, Ltd.

In this work, topology optimization is used to optimize the compliance or eigenvalues of prestressed plates. The prestress is accounted for by including the force equivalent to the prestressing and adding the initial stress stiffness matrix to the original stiffness matrix. The calculation of the sensitivities is complicated because of the initial stress stiffness matrix, but the computational cost can be kept low by using the adjoint method. The topology optimization problem is solved using the solid isotropic material with penalization (SIMP) method in combination with method of moving asymptotes (MMA). Numerical examples of minimizing compliance or maximizing the first eigenvalue of plates are given. One example is a micro electro mechanical systems (MEMS) application involving a backplate used in a microphone in a hearing aid. Copyright © 2001 John Wiley & Sons, Ltd.

— The objective of this paper is to investigate the Model Predictive Control (MPC) strategy, analyze and compare the control effects with Proportional-Integral-Derivative (PID) control strategy in maintaining a level of spherical tank system using VFD. An advanced control method, MPC has been widely used and well received in a wide variety of applications in process control, it utilizes an explicit process model to predict the future response of a process and solve an optimal control problem with a finite horizon at each sampling instant. In this paper, an algorithm is developed for controlling the speed of the pump using VFD to maintain the level of the spherical tank system. Next, the system is modeled and linearized for analysis and design. Model is implemented in a simulation environment based on LabVIEW. Both MPC and PID control methods are tried to design the controller for the water level system in a spherical tank and compared the results in terms of peak time, settling time, overshoot, and steady-state error under various operational conditions including time delays. The results showed the advantage of MPC for dealing with the system dynamic over PID and could be designed for more complex and fast system dynamics even in presence of constraints and Reduction of power consumption by the pump due to usage of VFD for controlling level by varying the speed of the pump based on the level of the tank. Keywords: Non Linear - Spherical Tank System – Level Control – Variable Frequency Drive – PID Controller – MPC Controller – Modeling - LabVIEW. Cite this Article R. Dhanasekar, S. Vijayachitra, M. Pravin Savaridass. Controlling of Level Process in Nonlinear System Using Variable Frequency Drive. Current Trends in Information Technology. 2019; 9(1): 24–33p.

A globally convergent method to accelerate topology optimization using on-the-fly model reduction

The most widely used control strategy in industry is proportional integral derivative (PID) controller. The popularity of PID controllers can be attributed partly to their robust performance in a wide range of operating conditions and partly to their functional simplicity. One of the application is to control arm robot manipulator model by using DC servo motor as actuator. This paper presents design of PID controller of DC servo motor using automated PID tuning by sisotool for higher order system and implement to the Arduino Mega 2560 via potentiometer by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink. A better design of controller using fuzzy logic controller (FLC) is proposed. Simulation results are demonstrated. Performance analysis shows the effectiveness of the proposed Fuzzy logic controller as compared to the PID controller.

With the rapid advancement of computing technology, mathematical modeling and computer simulations have become common and standard methods for design of complex systems. To exercise these models intelligently and eliminate the burden of manual iteration, manipulating inputs and reviewing outputs, optimization strategies are applied in a simulation-based design environment. In reality, the knowledge that engineers have about the design problem is imperfect and incomplete, uncertainty always exists in simulation-based design. The conventional deterministic optimization techniques do not consider the impact of such uncertainties. If the system responses driving the design decisions are very sensitive to these uncertainties, serious problems might confront the designer. Performance of the actual system may differ greatly from expected optimum performance. Again, if the design solution is near one or more constraint boundaries, then even slight uncertainties or changes in the operating environment could produce failed, unsafe designs, and/or result in substantial performance degradation. In recent years, the concept of reliability and robust design has become very popular. Probabilistic design analysis and statistical modeling allow the identification of designs that qualify as not only feasible, but as consistently feasible in the face of uncertainty. Reliability based topology optimization (RBTO) extends the reliability notion to the area of structural topology optimization. Although reliability based design optimization (RBDO) is a well established research area, very few work has been presented in the area of RBTO. In this study, we give an overview of the various works done in the field of RBTO. Ever since Bendsoe and Kikuchi introduced the topology optimization using a homogenization method, many topology optimization methods have been developed for the both linear and nonlinear structures. Traditional topology optimization methods drive the topology of a structure to an optimum design based on the constraint on mass. Kharmanda et al. introduced reliability constraints in deterministic topology optimization problem. They proposed a heuristic strategy that aims to reduce mass while improving the reliability level of the structure without greatly increasing its weight. But the limit state function used by them was not based on failure criteria for the structure. Their formulation considered uncertainty with respect to ∗Design Automation Laboratory †Advisor geometrical dimension and applied load only. Also their reliability analysis seems to be independent of the boundary and loading condition, so their results showed similar values for the uncertain variables for different structures. Tovar et al. have developed the Hybrid Cellular Automaton (HCA) method for structural synthesis of continuum material where the state of each cell is defined by both density and strain energy. In Agarwal, a decoupled RBDO approach is employed such that the topology optimization is separate from the reliability analysis. Patel et al. showed the use of RBTO using the gradient free Hybrid Cellular Automata (HCA) method. Their formulation incorporates uncertainty with respect to material property also. They considered limit state function based on failure modes on the output displacements. This study aims at giving a comparative study on these methods. It points out the advantages and disadvantages of the methods, and their significance in the field of RBTO. As large numbers of design variables are associated with continuum topology optimization problems, RBTO methods are inherently computationally expensive because of additional required system analysis associate with RBDO. Based on the observations, this study presents an efficient method for incorporating reliability analysis in topology optimization.

Robust topology and discrete fiber orientation optimization under principal material uncertainty