Reliable Optimization Method Research Articles

Determination of strut-and-tie models for structural concrete under dynamic loads

This proposed study aims to develop reliable and efficient numerical optimization methods for generating optimal strut-and-tie models (STMs) in structural concrete members under dynamic loads. The numerical models are developed based on the bidirectional evolutionary structural optimization (BESO) method for the stiffness maximization problems. In this method, a controlling index based on the minimum weight and maximum stiffness is defined as the optimization criterion function and the element virtual strain energy is taken as the element removal and addition criterion. By the dynamical analysis, optimal strut-and-tie models are established based on the BESO method. Several examples are presented to show the efficiency of the proposed approach in finding optimal STMs under dynamic loads. It is shown that optimal STMs and reinforcement layouts under dynamic loads generally differ from those obtained under static loads. The developed numerical models based on dynamic responses can be used by practicing design engineers for the analysis and design of STMs in concrete structures.

Development of High Cell Density Cultivation Strategies for Improved Medium Chain Length Polyhydroxyalkanoate Productivity Using Pseudomonas putida LS46.

High cell density (HCD) fed-batch cultures are widely perceived as a requisite for high-productivity polyhydroxyalkanoate (PHA) cultivation processes. In this work, a reactive pulse feed strategy (based on real-time CO2 or dissolved oxygen (DO) measurements as feedback variables) was used to control an oxygen-limited fed-batch process for improved productivity of medium chain length (mcl-) PHAs synthesized by Pseudomonas putida LS46. Despite the onset of oxygen limitation half-way through the process (14 h post inoculation), 28.8 ± 3.9 g L−1 total biomass (with PHA content up to 61 ± 8% cell dry mass) was reliably achieved within 27 h using octanoic acid as the carbon source in a bench-scale (7 L) bioreactor operated under atmospheric conditions. This resulted in a final volumetric productivity of 0.66 ± 0.14 g L−1 h−1. Delivering carbon to the bioreactor as a continuous drip feed process (a proactive feeding strategy compared to pulse feeding) made little difference on the final volumetric productivity of 0.60 ± 0.04 g L−1 h−1. However, the drip feed strategy favored production of non-PHA residual biomass during the growth phase, while pulse feeding favored a higher rate of mcl-PHA synthesis and yield during the storage phase. Overall, it was shown that the inherent O2-limitation brought about by HCD cultures can be used as a simple and effective control strategy for mcl-PHA synthesis from fatty acids. Furthermore, the pulse feed strategy appears to be a relatively easy and reliable method for rapid optimization of fed-batch processes, particularly when using toxic substrates like octanoic acid.

A Novel Method for Comprehensive Quality and Reliability Optimization of High-Power DC Actuators for Renewable Energy Systems

To better qualify various uncertainties in design and manufacturing, as well as to understand the time-varying degradation process, a novel method of quality and reliable design and optimization for high-power DC actuators was developed in this study that considered relevant uncertainties in design, manufacturing parameters, and the degradation process. Orthogonal transformation was used to normalize heterogeneous uncertainties and the results were quantitatively described by the hyperellipsoid set model. On the basis of the uncertainty quantitative relationship, a fast substitution model was developed for high-power DC actuators with permanent magnet output characteristics of strong non-linearity and insufficient accuracy. The response surface method was used to derive the basis function, and the error between the practical measured values and the calculation values was modified by the radial basis function model. Afterwards, a life cycle global sensitivity analysis method was put forward to determine the design parameters when parameter degradation existed during the life cycle of high-power DC actuators. Then, an optimization model was established considering parameter uncertainties and reliability constraints, and the particle swarm algorithm was used to obtain the solution. Finally, the effectiveness of the proposed method was verified by a case study of high-power DC actuators in electric vehicles.

Aerodynamic Optimal Design for Horizontal Axis Wind Turbine Airfoil Using Integrated Optimization Method

In this work, a new approach for aerodynamic optimization of horizontal axis wind turbine (HAWT) airfoil is presented. This technique combines commercial computational fluid dynamics (CFD) codes with differential evolution (DE), a reliable gradient-free global optimization method. During the optimization process, commercial CFD codes are used to evaluate aerodynamic characteristics of HAWT airfoil and an improved DE algorithm is utilized to find the optimal airfoil design. The objective of this research is to maximize the aerodynamic coefficients of HAWT airfoil at the design angle of attack (AOA) with specific ambient environment. The airfoil shape is modeled by control points which their coordinates are design variables. The reliability of CFD codes is validated by comparing the analytical results of a typical HAWT airfoil with its experimental data. Finally, the optimal design of wind turbine airfoil is evaluated about aerodynamic performance in comparison with existing airfoils and some discussions are performed.

Integrated method for performance analysis of reliability-based topologically optimized components

The available robust and reliable topology optimization methods provide quick and efficient design output in an uncertain environment. However, the whole domain of performance function remains hidden during this design process. In the interest of the designer, it is required to know the overall behavior of performance functions in deterministic as well as uncertain/realistic environment. The current work achieves this by proposing an integrated methodology, which combines the design of experiments approach and reliability-based topology optimization. The proposed method enables the designer to simulate performance functions in a desired design-factors space, including uncertainties, via reliability value. For this analysis, compliance, maximum deflection, mechanical advantage, and von Mises stress values are selected as performance functions. Volume fraction, applied force, and dimensions or aspect ratio are chosen as design/control factors. The uncertainties of these design factors are captured using reliability-based topology optimization. The uncertainties due to noncontrollable factors such as material property, load direction, and magnitude are incorporated using the design of experiments approach. Under these uncertainties, the performance of topologically optimized problem is simulated for different experimental combinations of the design factors. The experimental combinations for uncertainties and design factors are generated using Taguchi's orthogonal array. Simulated results are analyzed using techniques such as analysis of mean and variance, signal-to-noise ratio, and response surface method. These analyses help in identifying statistical significance of factors and uncertainties, performance variations, and equivalence relation of performance vs. factor. The proposed methodology is illustrated by selecting monolithic structures such as, on MBB, cantilever beam, and force inverter mechanism.

Research on Application of Electric Vehicle Collision Based on Reliability Optimization Design Method

When mentioning multidisciplinary design optimization methods, the deterministic optimum design is frequently applied to set the constraint boundary. Furthermore, only a small amount of space tolerances (or uncertainty) is available in the process of design, manufacture and operation. Therefore, deterministic optimum design lacking uncertainty cannot meet the needs of reliability-based design optimization. In this paper, reliability optimization design method, finite element (FE) analysis, optimal Latin hypercube test design and response surface approximation model are combined to optimize the side structure of electric vehicles and improve its crashworthiness. Firstly, a side impact FE model of the electric vehicle is established and verified in this paper. Then, the dimensions and the material yield strength of the force-bearing structure in the vehicle are selected as design variables, and the impact speed in the actual collision is selected as a random variable to optimize the car crashworthiness in the side impact using the 95% reliability optimization method. The results show that the 95% reliability optimization design increases the total energy absorption of the side components by 9.45%, the intrusion of the B-pillar and the vehicle door inner panel decreased by 10.42% and 14.75%, respectively. The intrusion speed of the B-pillar and the inner panel of the vehicle door decreases by 10.35% and 17.78%, respectively. By comparing the results of traditional deterministic optimization and reliability optimization methods, the latter can better satisfy the crash safety objectives, and improve the reliability of vehicle body design.

An adaptive particle swarm optimization method for multi-objective system reliability optimization

Multi-objective system reliability optimization has attracted the attention of several researchers, due to its importance in industry. In practice, the optimization regards multiple objectives, for example, maximize the reliability, minimize the cost, weight, and volume. In this article, an adaptive particle swarm optimization is presented for multi-objective system reliability optimization. The approach uses a Lévy flight for some particles of the swarm, for avoiding local optima and insuring diversity in the exploration of the search space. The multi-objective problem is converted to a single-objective problem by resorting to the weighted-sum method and a penalty function is implemented to handle the constraints. Nine numerical case studies are presented as benchmark problems for comparison; the results show that the proposed approach has superior performance than a standard particle swarm optimization.

Selective Flip-Flop Optimization for Reliable Digital Circuit Design

Runtime variability sources, such as bias temperature instability (BTI) and supply voltage fluctuation affect both timing and functionality of the flip-flops inside a VLSI circuit. In this paper, we propose a method to improve the timing and reliability of the VLSI circuits by optimizing the flip-flops for resiliency against aging and supply voltage fluctuation. In the proposed selective reliability optimization method, we first extend the standard cell libraries by adding optimized versions of the flip-flops designed for better resiliency against severe BTI impact and/or supply voltage fluctuation. Then, we optimize the VLSI circuit by replacing the aging-critical and voltage-drop-critical flip-flops (VC) of the circuit with the reliability-optimized versions to improve the timing and the reliability of the entire circuit in a cost-effective way. The simulation results show that incorporating the optimized flip-flops in a processor can prolong the lifetime of the processor by 36.9% compared to the original design, which translates into better reliability. This is achieved with negligible leakage overhead (less than 0.1% on the processor) and no area overhead which facilitates the integration of the proposed method in the standard VLSI design flow.

Verification оf Non-Stationary Mathematical Model оf Concrete Hardening in Thermal Technological Installations

Thermo-technical installations consuming significant amounts of thermal energy are used in order to intensify precast and reinforced concrete production processes under industrial conditions. Despite significant progress in the study of concrete hardening in accelerated hydration devices, a prominent lack of reliable and cost-effective research and optimization methods of their operation is observed. The methods used in real production processes are mainly based on empirical dependences obtained for specific technological conditions. These methods can not always be applied for other modes and technologies. The present paper develops calculation methods based on fundamental laws that make it possible to obtain functions for evolution of concrete product hydration process. Methods of mathematical modeling permit to develop new ways directed on improvement of modes for heat treatment of concrete products and accelerated hydration technologies. The paper describes a mathematical model for calculating a hardening process of a concrete product that includes a transient three-dimensional heat conductivity equation, a function of internal heat release due to behavior of exothermic reactions of cement hydration and also a system of initial and boundary conditions. A numerical simulation for temperature and hydration coefficient of a concrete product having shape of a 0.1´0.1´0.1 m cube has been performed in the paper. Verification of the non-stationary mathematical model for calculating temperature fields and hydration degree while using experimental data on concrete product strength obtained under industrial conditions. Investigations on hydration degree function of time have shown that experimentally obtained values of compressive strength correlate with hydration coefficient and hydration rate functions of heat treatment time which are calculated on the basis of the proposed non-stationary mathematical model of concrete product hardening. Satisfactory agreement of experimental and calculated data confirms adequacy of the proposed non-stationary mathematical model for calculating temperature fields and hydration degree with accelerated heat treatment of concrete products.

Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search

Artificial Neural networks (ANNs) are often applied to data classification problems. However, training ANNs remains a challenging task due to the large and high dimensional nature of search space particularly in the process of fine-tuning the best set of control parameters in terms of weight and bias. Evolutionary algorithms are proved to be a reliable optimization method for training the parameters. While a number of conventional training algorithms have been proposed and applied to various applications, most of them share the common disadvantages of local optima stagnation and slow convergence. In this paper, we propose a new evolutionary training algorithm referred to as LPSONS, which combines the velocity operators in Particle Swarm Optimization (PSO) with Mantegna Lévy distribution to produce more diverse solutions by dividing the population and generation between different sections of the algorithm. It further combines Neighborhood Search with Mantegna Lévy distribution to mitigate premature convergence and avoid local minima. The proposed algorithm can find optimal results and at the same time avoid stagnation in local optimum solutions as well as prevent premature convergence in training Feedforward Multi-Layer Perceptron (MLP) ANNs. Experiments with fourteen standard datasets from UCI machine learning repository confirm that the LPSONS algorithm significantly outperforms a gradient-based approach as well as some well-known evolutionary algorithms that are also based on enhancing PSO.

Optimal energy saving of doubly fed induction motor based on scalar rotor voltage control and water cycle algorithm

PurposeThis paper aims to present an optimal variable speed drive of a doubly fed induction motor (DFIM) with minimum losses and reduced inverter capacity. The operation with minimum losses ensures that the DFIM develops the required load torque at desired speed with maximum energy saving. Moreover, the control of rotor voltage ensures the reduced inverter capacity. The water cycle algorithm (WCA) as one of meta-heuristic optimization techniques is used to estimate the optimal rotor voltages to drive the DFIM with minimum losses. The results of WCA are confirmed with other well-known and reliable optimization method such as particle swarm optimization along with classical method.Design/methodology/approachThe DFIM is an efficient alternative solution of synchronous motor (SM) because of its speed is synchronized with both stator and rotor frequencies regardless the load torque. As a result, the speed of variable speed drive associated with DFIM can be controlled through a rotor inverter with reduced capacity rather than SM. The output voltage of rotor inverter is controlled to develop the demanded output power with minimum motor losses.FindingsA complete DFIM drive model is developed under MATLAB/SIMULINK environment using d-q dynamic model to verify the strength and significance of the proposed controller. An experimental setup using a 300 W three-phase wound rotor induction motor is established to validate the mathematical models and theoretical results. The motor performances with proposed rotor voltage control (minimum losses) are compared with conventional method of constant voltage to frequency ratio (V/f constant). It is found that the proposed WCA based on controller achieves significant reductions in motor losses, input power and rotor inverter power.Originality/valueThe paper presents an efficient method to maximize the energy saving of DFIM with a reduced inverter capacity using WCA.

Aerothermodynamic Design Optimization of Hypersonic Vehicles

The objective of this study is to develop a reliable and efficient design optimization method for hypersonic vehicles focused on aerothermodynamic environments. Considering the nature of hypersonic flight, a high-fidelity aerothermodynamic analysis code is used for the simulation of weakly ionized hypersonic flows in thermochemical nonequilibrium. A gradient-based method is implemented for optimization. Bezier or nonuniform rational basis spline curves are used to parametrize the geometry or the geometry change. Linear elasticity theory is implemented for mesh deformation. Penalty functions are utilized to prevent undesired geometrical changes. The design objective is to minimize drag without increasing the total heat transfer rate and the maximum values of the surface heat flux, temperature, and pressure. Design optimizations are performed at different trajectory points of the IRV-2 vehicle. The effects of parametrizations, the number of design variables, and freestream conditions on design performance are studied.

An importance learning method for non-probabilistic reliability analysis and optimization

With the time-consuming computations incurred by nested double-loop strategy and multiple performance functions, the enhancement of computational efficiency for the non-probabilistic reliability estimation and optimization is a challenging problem in the assessment of structural safety. In this study, a novel importance learning method (ILM) is proposed on the basis of active learning technique using Kriging metamodel, which builds the Kriging model accurately and efficiently by considering the influence of the most concerned point. To further accelerate the convergence rate of non-probabilistic reliability analysis, a new stopping criterion is constructed to ensure accuracy of the Kriging model. For solving the non-probabilistic reliability-based design optimization (NRBDO) problems with multiple non-probabilistic constraints, a new active learning function is further developed based upon the ILM for dealing with this problem efficiently. The proposed ILM is verified by two non-probabilistic reliability estimation examples and three NRBDO examples. Comparing with the existing active learning methods, the optimal results calculated by the proposed ILM show high performance in terms of efficiency and accuracy.

Performance optimization methods for switched-capacitor biquadratic filters

Abstract A class of computer-aided optimization methods based on Differential Evolution (DE), Particle Swarm Optimization (PSO) and Nelder Mead algorithms applied to a switched-capacitor (SC) filter circuit design are investigated. Comparisons of these algorithms applied to a 4th order biquadratic two-channel filter bank CMOS design on 0.35 µm technology are made. The frequency responses of the biquadratic filters must match ideal responses in a finite number of iterations with a limited number of “particles”. The original and derived methods are evaluated on the base of their convergence progress and their reliability over different starting populations. An optimal design approach based on combining algorithms is derived as a more suitable and more reliable method for SC circuit optimization.

Topology optimization of transmission system of flapping-wing micro aerial vehicle via 3D printing

3D printing makes the manufacturing of complex and small structures more convenient, which has promoted the development of flapping-wing micro aerial vehicle (FMAV) in recent years. And structural reliability and weight play decisive role in service life and endurance of FMAV transmission system. Though topology optimization design is conducive to obtain a light-weight structure, current researches still lack guidance of considering non-linear factors of 3D printing material. In this paper, experiment as well as numerical computation efforts was acted in concert to gain a reliable topology optimization method. A numerical computation model describing the mechanical behavior of FMAV transmission structure was established and verified by experiments. And then topology optimization modeling method considering non-linear factors was presented and optimization results were verified by dynamic simulation and experiments. Detail comparison of different optimized results based on different load status was carried out to explore the leading factors affecting the optimization results. Engineering guidance for transmission system lightweight design of FMAV with better fatigue resistance was given, which may shed lights on light-weight design for future advanced FMAV.

Numerical calculation model investigation on response for connector assembly of a free-standing hybrid riser with experimental validation

In order to provide efficient and reliable method for structural design and optimization of upper assembly and lower assembly of a free standing hybrid riser, a numerical calculation model and model test device for the upper assembly and lower assembly of a free-standing hybrid riser were established independently based on similarity criterion and correlative elastic-plastic mechanics theories and methods. The static and dynamic numerical calculations and corresponding tests for the upper assembly and lower assembly of a free-standing hybrid riser were carried out. The results show that the deviation between numerical simulation results and experimental results is less than 6.83%, and the dynamic test results coincide with the dynamic results of numerical simulation. The results of the above study demonstrate that the accuracy and reliability of numerical results for static and dynamic responses of the upper assembly and lower assembly and suggestions on further optimization for upper assembly and lower assembly of a free standing hybrid riser are given based on numerical simulation and test results. These results provide guidance for structural optimization of the upper assembly and lower assembly of a free-standing hybrid riser.

Developing Models of the Control and Monitoring System of SMS-Network Platforms

This paper is concerned with analytical models and methods for reliability planning, optimization, and operation of the control and monitoring system of SMS-network platforms. The difference between classical models of reliability and models developed here is that the last ones take into account the economy is often an important aspect of reliability planning. Network providers operate in a competitive environment, so the main principle of reliability planning is not so much to increase reliability as well (as it’s done in fully technical approach) as to maximise the total profit of the network. Thus, new methods must allow to find the combination of factors (costs, investments into reliability, redundancy and increased maintenance, the profit from system operation, penalties for delays and failures, risks defined in a service level agreement, etc.) that maximises the profit. We employ the well-known from an actuarial science a model that considers a network through two cash flows: incoming cash from customers and outgoing claims paid due unreliability under SLA. Also we include into the optimisation model more general assumptions about the reliability of systems: different ways of reliability improvements and redundancy, non-exponentially distributed failures and recoveries, dependent failures, and reliability control.

Reliability-based Support Optimization of Rockbolt Reinforcement around Tunnels in Rock Masses

Traditionally, the design of tunnels is based on determinate parameter values. In practice, both the performance and safety of tunnels are affected by numerous uncertainties: for example,it is difficult for engineers to predict uncertainties in geological conditions and rock mass properties. The purpose of reliability-based optimization (RBO) is to find a balanced design that is not only economical but also reliable in the presence of uncertainty. In the past few decades, numerous reliability optimization techniques have been proposed for taking uncertainty into account in the design of engineering structures. In the present study, the first-order reliability method (FORM) was used to compute the reliability index using Excel Solver. The least squares support vector machine (LSSVM) approach was adopted to build a relationship between reliability index and design variables,and the artificial bee colony (ABC) algorithm was employed for the reliability-based optimization. A proposed LSSVM/ABC-based reliability optimization method was applied to the case of a tunnel with rockbolt reinforcement. The mechanical parameters of the rock mass, in-situ stress and internal pressure were considered as the random variables. The reliability index of tunnel was analysed. The length, distance out of plane and the number of rockbolts were determined and optimized considering the uncertainty based on RBO. The proposed method improved the efficiency of RBO while maintaining high accuracy. The results showed that the proposed method not only meets the design accuracy, but also improves the efficiency of reliability-based optimization.

A non‐probabilistic reliability‐based design optimization method for structures based on interval models

AbstractA non‐probabilistic reliability optimization method for structures with uncertain‐but‐bounded variables was presented. Based on the interval model description for the uncertain‐but‐bounded parameters, the non‐probabilistic reliability index was used to measure the structural reliability. The optimal design was formulated as a nested two‐loop optimization problem. For a nonlinear function, a number of constraints that satisfied the non‐probabilistic reliability index were treated as the serial system, and the feasible region of the uncertain parameters was determined by the target non‐probabilistic reliability index. The inner optimization method for solving a number of non‐probabilistic reliability indices was transformed into the problem of minimizing the functions within the feasible range. For a linear function, the non‐probabilistic reliability optimization problem can be transformed into a deterministic optimization problem. The high computational cost of determining the non‐probabilistic reliability was greatly reduced. Three numerical examples were presented to illustrate the efficiency of the proposed method.

Mixed Nonprobabilistic Reliability-Based Optimization Method for Heat Transfer System With Fuzzy and Interval Parameters

By combining the nonprobabilistic reliability theory with optimization design approach, this paper proposes a mixed reliability-based optimization method for the heat transfer system design with both fuzzy and interval parameters in material property, external load, and boundary condition. Fuzzy variables are used to represent subjective uncertainties associated with expert opinions; whereas, interval variables are adopted to quantify objective uncertainties with limited information. Based on the level-cut strategy, the mixed uncertain problem is transformed into a pure interval problem first. Then, a modified reliability analysis method using an interval ranking strategy and integral operation is presented to precisely assess the system safety possibility. Subsequently, a mixed reliability-based optimization model with fuzzy and interval parameters is established, which is a nested optimization problem with huge computational cost. A subinterval perturbation method is eventually presented for temperature field prediction, which can replace the inner loop optimization and improve the computational efficiency. A transient heat conduction example about a three-layer thermal structure verifies the superiority of the proposed model and method for mixed reliability analysis and optimization in practical engineering.