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The purpose of this paper is to expose computational methods as applied to engineering systems and evolutionary processes with randomness in external actions and inherent parameters.

In total, two approaches are distinguished that rely on solvers from deterministic algorithms. Probabilistic analysis is referred to as the approximation of the response by a Taylor series expansion about the mean input. Alternatively, stochastic simulation implies random sampling of the input and statistical evaluation of the output.

Beyond the characterization of random response, methods of reliability assessment are discussed. Concepts of design improvement are presented. Optimization for robustness diminishes the sensitivity of the system to fluctuating parameters.

Deterministic algorithms available for the primary problem are utilized for stochastic analysis by statistical Monte Carlo sampling. The computational effort for the repeated solution of the primary problem depends on the variability of the system and is usually high. Alternatively, the analytic Taylor series expansion requires extension of the primary solver to the computation of derivatives of the response with respect to the random input. The method is restricted to the computation of output mean values and variances/covariances, with the effort determined by the amount of the random input. The results of the two methods are comparable within the domain of applicability.

The present account addresses the main issues related to the presence of randomness in engineering systems and processes. They comprise the analysis of stochastic systems, reliability, design improvement, optimization and robustness against randomness of the data. The analytical Taylor approach is contrasted to the statistical Monte Carlo sampling throughout. In both cases, algorithms known from the primary, deterministic problem are the starting point of stochastic treatment. The reader benefits from the comprehensive presentation of the matter in a concise manner.

The extremely high and ever‐increasing annual disposal rates of solid waste have caused a big problem for environmental protection in the world. Unlike the first environmental revolution in the 1970s, which was aimed at cleaning up hazardous waste from contaminated sites and natural resources, the second revolution is addressing waste reduction at the source. The solution of these problems cannot rely only on legislation and must be supported by effective methods. This goal can be achieved through the design of products that promote disassembly, reusing and recycling. In order to design environmentally friendly products in concurrent engineering, this paper applies axiomatic design to develop the integrated design guidelines with Axiom 1 (independence axiom) for generating acceptable designs and an evaluation score with Axiom 2 (information axiom) for determining better or the best design from the acceptable designs.

The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO. Besides, to mitigate the computational burden caused by expensive simulation codes and employ both efficiently simplified and expensively detailed information in multidisciplinary design optimization (MDO), an effective framework combining variable fidelity metamodels (VFM) and modified BLISCO (MBLISCO) (VFM-MBLISCO) is proposed.

The concept of the quasi-separable MDO problems is introduced to limit range of applicability about the BLISCO method and then based on the quasi-separable MDO form, the modification of BLISCO method without any derivatives is presented to solve the problems of BLISCO. Besides, an effective framework combining VFM-MBLISCO is presented.

One mathematical problem conforms to the quasi-separable MDO form is tested and the overall results illustrate the feasibility and robustness of the MBLISCO. The design of a Small Waterplane Area Twin Hull catamaran demonstrates that the proposed VFM-MBLISCO framework is a feasible and efficient design methodology in support of design of engineering products.

The proposed approach exhibits great capability for MDO problems with tremendous computational costs.

A MBLISCO is proposed which can avoid the non-separability of the original BLISCO and an effective framework combining VFM-MBLISCO is presented to efficiently integrate the different fidelities information in MDO.

The objective of this work is to introduce a new method to carry out design optimization of a mechanical system for vibration and shock isolation, in particular, the viscous spring isolator mounting system for a forging hammer.

The system dynamics model for an isolated foundation and solution technique for obtaining system response under impact loads is introduced. A design optimization problem is formulated to minimize the maximum impact force transmissibility under design constraints, using stiffness and damping coefficients of the isolator, mass of the foundation block and support area of soil as design variables. A dedicated simulated annealing (SA) algorithm is applied to solve the optimization problem.

Viscous spring isolator mounting system, if properly designed, can considerably reduce shock and vibration transmission and the size of the foundation. The optimization leads to a mounting system with superior impact and vibration isolation capability over conventional designs. Sensitivity study and design optimization on a typical 3‐ton forging hammer has demonstrated the advantages of the new design method.

To further improve the accuracy of the design optimization, a more detailed system dynamics model might be introduced.

The work leads to a better design method for viscous spring isolator foundation systems.

This study forms the foundation for further research on design optimization of viscous spring isolator foundation systems, and contributes to the application of SA optimization technique to engineering design.

Constant flow valves have been presented in industrial applications or academic studies, which compensate pressures of bearing recesses as load fluctuates. The flow rate of constant-flow valves (CFVs) can be constant in spite of the pressure changes in recesses. However, specific condition of design parameters must be satisfied. The paper aims to discuss these issues.

This paper utilizes analytical method to study the static characteristics of CFVs, three types belong to traditional design of CFV are reviewed afresh. Moreover, an innovative design for constant flow is presented and studied.

The review and study results reveal that appropriate relationships among design parameters for these types of CFVs.

The numerical simulation is used to investigate the influence of design parameters on the change of flow rate when pressure ratio of recess is changed.

Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues.

Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures.

It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures.

The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components.

The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

The purpose of this paper is to present a hierarchical circuit synthesis system with a hybrid deterministic local optimization – multi‐objective genetic algorithm (DLO‐MOGA) optimization scheme for system‐level synthesis.

The use of a local optimization with a deterministic algorithm based on linear equations which is computationally efficient and improves the feasibility of designs, allows reduction in the number of MOGA generations required to achieve convergence.

This approach reduces the total number of simulation iterations required for optimization. Reduction in run time enables use of full transistor‐level models for simulation of critical system‐level sub‐blocks. Consequently, for system‐level synthesis, simulation accuracy is maintained. The approach is demonstrated for the design of pipeline analog‐to‐digital converters on a 0.35 μm process.

The use of a hybrid DLO‐MOGA optimization approach is a new approach to improve hierarchical circuit synthesis time while preserving accuracy.

The paper aims to extend the knowledge base for design and optimization of Star grain which is well known for its simplicity, reliability and efficiency. Star grain configuration is considered to be among the extensively used configurations for the past 60 years. The unexplored areas of treatment of ballistic constraints, non-neutral trace and freedom from use of generalized design equations and sensitivity analysis of optimum design point are treated in detail to bridge the gap. The foremost purpose is to expand the design domain by considering entire convex Star family under both neutral and non-neutral conditions.

This research effort optimizes Star grain configuration for use in Solid Rocket Motors with ballistic objective function (effective total impulse) and parametric modelling of the entire convex Star grain family using solid modelling module. Internal ballistics calculations are performed using equilibrium pressure method. Optimization process consists of Latinized hypercube generated initial population and Swarm Intelligence optimizer’s ability to search design space. Candidate solutions are passed to solid modelling module to simulate the burning process. Optimal design points, critical geometrical and important ballistic parameters (throat diameter, burn rate, characteristic velocity and propellant density) are then tested for sensitivities through Monte Carlo simulation.

The proposed approach takes the design of Star grain configuration to a new level with introduction of parametric modelling and sensitivity analysis, thus, offering practical optimum design points for use in various mission scenarios. The proposed design and optimization process provides essential data sets which can be useful prior to the production of large number of solid rocket motors. Results also advocate the adequacy of design from engineering perspective and practicality.

Results showed that few design parameters are sensitive to uncertainties. These uncertainties can be investigated in future by a robust design method.

Monte Carlo simulation can prove to be vital considering the production of a large number of motor units and enlightens the necessity to obtain statistical data during manufacturing.

This paper fulfils long-sought requirement on getting free from use of generalized set of equations for commonly used Star grain configurations.

The purpose of this paper is to design a robust control scheme to achieve robust tracking of velocity and altitude commands for a general hypersonic vehicle (HSV) in the presence of parameter variations and external disturbances.

The robust control scheme is composed of nonsingular terminal sliding mode control (NTSMC), super twisting control algorithm (STC) and recurrent neural network (RNN). First, by combing a novel NTSMC and STC algorithm, a second order NTSMC approach for HSV is proposed to provide fast, continuous and high precision tracking control. Second to relax the requirements for the bounds of the lumped uncertainties in control design, a RNN disturbance observer is presented to increase the robustness of the control system. The weights of RNN are updated by adaptive laws based on Lyapunov theorem, thus the closed‐loop stability can be guaranteed.

Simulation results demonstrate that the proposed method is effective, leading to promising performance.

The main contributions of this work are: first, both parameter variations and external disturbances are considered in control design for the longitudinal dynamic model of HSV; and second, the proposed controller can remove chattering and achieve more favorable tracking performances than conventional sliding mode control.

The purpose of this paper is to implement the modeling of the design process in response to volatile market demands. It mainly focuses on design activities for product development towards the early stage.

This paper describes the system of products from the systems engineering point of view, analyzes the attributes of products and components, studies the environment and its input/output relationship and implements product formal representation, respectively. This paper gives product structure and component connections, describes the process of structure generations and product decomposition by set representation, calculates the number of component connections and density of components; also it presents product module division and coupling degree analysis in accordance with axiomatic theory; coupling degree is calculated by considering the correspondence ratio and the cluster independence.

It describes the product as an engineering system, and analyzes product systems and product structures through using systems theory. Design process modeling is analyzed, and a strategic approach to decomposing product structure and calculating modularity is adopted for product development.

Accessibility and availability mainly focus on product system development.

The paper offers useful advice for the modeling process of product conceptual design.

The new approach to conceptual design is based on set representation and systems analysis as well as axiomatic theory. The paper is aimed at understanding evolution mechanism of complexity product systems.