Optimal design of a novel crash box with functional gradient negative Poisson’s ratio structure

Crashworthiness and anti-vibration performance play critical roles in the performance of passenger cars. Aiming at enhancing the crash resistance and vibration resistance of vehicles thus providing good protection for passengers and drivers, a novel crash box with three-dimensional double arrow type negative Poisson’s ratio structure with functional gradient filling inner core (FGNPR crash box) is introduced in this paper and its performance is studied in detail through the comparison with the conventional crash box and the crash box filled with the uniform gradient negative Poisson’s ratio structure (NPR crash box) in crashworthiness and vibration resistance. Furthermore, range analysis is used to screen out the design variables that have little influence on the evaluation indexes and eliminate them. Based on these, neighborhood cultivation genetic algorithm (NCGA) and non-dominated sorting genetic algorithm-ii (NSGA-II) are selected as the optimization algorithms to carry out optimization design respectively and a comparison is made between the two suboptimal results screened out based on the normal boundary intersection (NBI) method to determine the overall optimal solution. Results show that the optimized FGNPR crash box has better crashworthiness and vibration resistance over the other crash boxes and its performance is further verified based on the peak acceleration of B-pillar in full vehicle crash condition. This paper provides some theoretical reference support for the development and exploration of automobile crash box systems.

Augmented Lagrangian Method for Maximizing Expectation and Minimizing Risk for Optimal Well-Control Problems With Nonlinear Constraints

Summary Robust waterflooding optimization commonly refers to the problem of estimating well controls (wellbore pressures or rates at specified control steps) that maximize the expectation of net-present-value (NPV) of the life-cycle production over an ensemble of given reservoir models. Unfortunately, if the reservoir is operated under the “optimal” well controls obtained, the variance in NPV may be large; more importantly, if the smallest NPV obtained is close to the one that would be obtained for the true reservoir, the development of the reservoir might not be commercially viable. Liu and Reynolds (2015b) suggested that one way to manage risk was to consider the problem in which the dual objectives were to maximize the expected value of NPV and to minimize the risk in which the risk was defined as the minimum NPV from an ensemble of models spanning the uncertainty in reservoir description. However, the algorithms presented in Liu and Reynolds (2015b) considered only bound constraints. Here, we develop algorithms to generate points on the Pareto front when nonlinear state (output) constraints are present. The Pareto front is generated either by a constrained weighted-sum (WS) method or a constrained normal-boundary-intersection (NBI) method. In this paper, we extend the augmented Lagrangian approach given in Liu and Reynolds (2015b) for biobjective optimization with bound constraints to biobjective optimization problems in which nonlinear state constraints are present. We provide a detailed derivation of how to incorporate nonlinear constraints for multiobjective optimization problems (MOOPs) and illustrate, by means of example, that the methodology is viable for biobjective optimization with nonlinear constraints.

Gradient-Based Multiobjective Optimization for Maximizing Expectation and Minimizing Uncertainty or Risk With Application to Optimal Well-Control Problem With Only Bound Constraints

Summary We consider two procedures for multiobjective optimization, the classical weighted-sum (WS) method and the normal-boundary-intersection (NBI) method. To enhance computational efficiency, the methods use gradients calculated with the adjoint method. Our objective is to develop implementations that one can apply for waterflooding optimization under geological uncertainty when we wish to develop well controls that satisfy two objectives: The first is to maximize the expectation of life-cycle net present value (NPV) (commonly referred to as robust optimization), and the second is either to minimize the standard deviation of NPV over that set of plausible reservoir descriptions or to minimize the risk when risk means downside risk. Specifically, minimizing risk refers to maximizing the minimum value of the life-cycle NPV (i.e., is equivalent to a maximum/minimum (max/min) problem). To avoid nondifferentiability issues, we recast the max/min problem as a constrained optimization problem and apply a gradient-based version of either WS or NBI to construct a point on the Pareto front. To deal with the constraints introduced, we derive an augmented-Lagrange algorithm to find points on the Pareto front. To the best of our knowledge, the resulting versions of “constrained” WS and “constrained” NBI were not presented previously in the scientific literature. The methodology is demonstrated for two synthetic reservoirs. We only consider bound constraints in this paper.

Swarm Intelligence for Multiobjective Optimization of Extraction Process

Multi objective (MO) optimization is an emerging field which is increasingly being implemented in many industries globally. In this work, the MO optimization of the extraction process of bioactive compounds from the Gardenia Jasminoides Ellis fruit was solved. Three swarm-based algorithms have been applied in conjunction with normal-boundary intersection (NBI) method to solve this MO problem. The gravitational search algorithm (GSA) and the particle swarm optimization (PSO) technique were implemented in this work. In addition, a novel Hopfield-enhanced particle swarm optimization was developed and applied to the extraction problem. By measuring the levels of dominance, the optimality of the approximate Pareto frontiers produced by all the algorithms were gauged and compared. Besides, by measuring the levels of convergence of the frontier, some understanding regarding the structure of the objective space in terms of its relation to the level of frontier dominance is uncovered. Detail comparative studies were conducted on all the algorithms employed and developed in this work.

Supply chain design through QFD-based optimization

Purpose – The purpose of this paper is to demonstrate a methodology to design a supply chain with a view to achieve a strategic fit between competitive and supply chain strategies. Design/methodology/approach – Quality function deployment (QFD)-based optimization methodology is employed to design a supply chain for a product through aligning the competitive and supply chain strategies. Normal boundary intersection (NBI) method is adopted to obtain optimal weights of the supply chain design objectives. Weighted additive model is developed for multi-objective optimization. Utility-based attribute function, which structure the relationship between the elements of competitive and supply chain strategies is established. The utility functions and the information contained in the House of Quality (HOQ) of QFD are used to define the supply chain performance (SCP). Findings – SCP index is computed using the set of supply chain design objectives obtained by solving the weighted additive model. On the basis of SCP index, the supply chain activities are planned accordingly. An illustrative example is presented in this paper to describe the QFD-based optimization methodology for designing a supply chain. Originality/value – QFD-based optimization is a novel approach to design a supply chain with a focus on aligning competitive and supply chain strategies.

An Algorithmic Framework for Multiobjective Optimization

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

Pareto-Optimal Solutions for a Truss Problem

Most optimization problems, particularly those in engineering design, require the simultaneous optimization of more than one objective function. In this context, the solutions of these problems are based on the Pareto frontier construction. Substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The Normal Boundary Intersection (NBI) is a recent contribution that generates a well-distributed Pareto frontier efficiently. Nevertheless, this method should be combined with a global optimization method to ensure the convergence to the global Pareto frontier. This paper proposes the NBI method using Adaptive Simulated Annealing (ASA) algorithm, namely NBI-ASA as a global nonlinear multi-objective optimization method. A well known benchmark multi-objective problem has been chosen from the literature to demonstrate the validity of the proposed method, applicability of the method for structural problems has been tested through a truss problem and promising results were obtained. The results indicate that the proposed method is a powerful search and multi-objective optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms.

Imaging by Magnifying Endoscopy with NBI Implicates the Remnant Capillary Network As an Indication for Endoscopic Resection in Early Colon Cancer

Introduction. This study examined whether magnifying endoscopy with NBI observation (ME-NBI) could be useful selecting the appropriate treatment for submucosal invasive cancer (SM cancer).Patients and Methods. We analyzed 515 cases of colon tumors excised endoscopically or surgically. We classified capillary network pattern into four types according to the degree of dilatation, irregularity, and distribution of microcapillary features.Results. The comparison of capillary pattern and histological features revealed microcapillary networks by using confocal laser-scanning microscopy and ME-NBI in intramucosal lesion or SM cancer with remnant neoplastic glands at the superficial layer. In contrast, the network was absent in SM cancer with desmoplastic reactions, which invaded deeper into the submucosal layer.Conclusions. The remaining microcapillary network is designed to maintain the architecture of neoplastic glands. Consequently, loss of this network could correlate with depth of tumor invasion and desmoplastic reaction. Therefore, we can decide the appropriate treatment by using ME-NBI method.