Robust Suspension System Design

1993 ◽  
Author(s):  
John E. Beard ◽  
John W. Sutherland
Keyword(s):  
System Design ◽  
Robust Design ◽  
Loss Function ◽  
Kinematic Model ◽  
Small Variation ◽  
Suspension System ◽  
Product Performance ◽  
Design Variables ◽  
Manufacturing Assembly ◽  
The Impact

Abstract Traditionally, levels for design variables are sought that produce optimal performance of a product. When manufacturing and assembly processes are used to realize the design intent, however, the product performance may differ from that envisioned during design. This is because the performance of a product is often very sensitive to manufacturing and assembly variations. This paper presents a methodology for robust design that incorporates the impact of manufacturing/assembly variations. The methodology characterizes the performance of a manufactured product via a loss function. The loss function measure is attractive from a robust design standpoint since it stresses both desirable performance on the average and small variation in performance from product to product. The design methodology is demonstrated through a suspension system design application. A model for the kinematic behavior of a suspension system is developed. The scrub rate is selected as the response of interest to demonstrate the methodology. The behavior of the kinematic model, in terms of the loss function, is approximated near a set point and levels of the design variables are sought that minimize the loss. An iterative procedure is described for optimizing the loss function. The application demonstrates that substantial improvements can be made in terms of actual manufactured product performance through the use of the methodology.

Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2005 ◽  
Vol 128 (4) ◽  
pp. 945-958 ◽  
Author(s):  
Daniel W. Apley ◽  
Jun Liu ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Random Noise ◽  
Computer Experiments ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Discrete Design Variables ◽  
The Impact

The use of computer experiments and surrogate approximations (metamodels) introduces a source of uncertainty in simulation-based design that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty in which randomness is present in noise and/or design variables. Because the random noise and/or design variables are also inputs to the metamodel, the effects of metamodel interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on the robust design objective, under consideration of uncertain noise variables. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. We illustrate the proposed methodology with two robust design examples—a simple container design and an automotive engine piston design with more nonlinear response behavior and mixed continuous-discrete design variables.

Understanding the Impact of Decision Making on Robustness During Complex System Design: More Resilient Power Systems

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Joseph R. Piacenza ◽  
Kenneth John Faller ◽  
Mir Abbas Bozorgirad ◽  
Eduardo Cotilla-Sanchez ◽  
Christopher Hoyle ◽  
...  
Keyword(s):  
Complex System ◽  
Decision Making ◽  
System Design ◽  
Robust Design ◽  
System Performance ◽  
System Level ◽  
Individual Risk ◽  
Cascading Failure ◽  
The Impact ◽  
Complex System Design

Abstract Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design.

scholarly journals Exergy Analysis of Directional Solvent Extraction Desalination Process

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 321 ◽  
Author(s):  
Sorour Alotaibi ◽  
Osama Ibrahim ◽  
Yu Wang ◽  
Tengfei Luo
Keyword(s):  
Solvent Extraction ◽  
Parametric Study ◽  
Octanoic Acid ◽  
Exergy Analysis ◽  
Small Variation ◽  
Water Yield ◽  
Low Grade ◽  
Design Variables ◽  
Exergy Consumption ◽  
The Impact

This paper presents an exergy analysis to evaluate the performance of a continuous directional solvent extraction (DSE) desalination process using octanoic acid. The flow of exergy was calculated for each thermodynamic state and balanced for different components of the system to quantify the inefficiencies in the process. A parametric study was performed to evaluate the impact of three critical design variables on exergy consumption. The parametric study reveals that the total exergy input decreases significantly with an increase in heat exchanger effectiveness. The results also indicate that the heat exchangers account for the highest exergy destruction. The total exergy consumption, however, has a slightly declining trend as the recovery-ratio increases. There is a small variation in the total exergy consumption, within the uncertainty of the calculation, as the highest process temperature increases. When compared to conventional desalination processes, the exergy consumption of the DSE, with heat recovery of 90%, is comparable to those of multi-stage flashing (MSF), but much higher than reverse osmosis (RO). Octanoic acid, which has low product water yield, is identified as the primary factor negatively impacting the exergy consumptions. To exploit the low-grade and low-temperature heat source feature of the DSE process, directional solvents with higher yield should be identified or designed to enable its full implementation.

Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2006 ◽  
Author(s):  
Jun Liu ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Computer Experiments ◽  
Design Problems ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Complex Design ◽  
The Impact

The use of metamodels in simulation-based robust design introduces a new source of uncertainty that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty. With the randomness present in noise and/or design variables that propagates through the metamodel, the effects of model interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on robust design objective. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. Even though our proposed methodology is illustrated with a simple container design and an automotive engine piston design example here, the developed analytical approach is the most useful when applied to high-dimensional complex design problems in a similar manner.

A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors

1996 ◽  
Vol 118 (4) ◽  
pp. 478-485 ◽  
Author(s):  
Wei Chen ◽  
J. K. Allen ◽  
Kwok-Leung Tsui ◽  
F. Mistree
Keyword(s):  
Robust Design ◽  
Irrigation System ◽  
Small Variation ◽  
Type I ◽  
Closed Form Solutions ◽  
Control Factors ◽  
Design Variables ◽  
Solar Powered ◽  
And Control ◽  
Noise Factors

In this paper, we introduce a small variation to current approaches broadly called Taguchi Robust Design Methods. In these methods, there are two broad categories of problems associated with simultaneously minimizing performance variations and bringing the mean on target, namely, Type I—minimizing variations in performance caused by variations in noise factors (uncontrollable parameters). Type II—minimizing variations in performance caused by variations in control factors (design variables). In this paper, we introduce a variation to the existing approaches to solve both types of problems. This variation embodies the integration of the Response Surface Methodology (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example.

The Suspension System Design for the Tensioner of the Ocean Pipeline Barge

2011 ◽  
Vol 383-390 ◽  
pp. 98-103
Author(s):  
Zhi Ping Guo ◽  
Chao Zhang ◽  
Bao Zong Li ◽  
Lian Lei Wang ◽  
Shi Min Zhang
Keyword(s):  
Mathematical Model ◽  
System Design ◽  
Variable Stiffness ◽  
Suspension System ◽  
Pipe Diameter ◽  
Response Force ◽  
Elastic Suspension ◽  
Matlab Software ◽  
Stress Environment ◽  
The Impact

Based on the variable stiffness elastic principle, completed three series of variable stiffness elastic suspension system design for the ocean pipe laying tensioners, effectively improved the clamping ability of the tensioner when the local pipe diameter is changing, and improved the overall stress environment of the tensioner. Established non-linear mathematical model and got the impact of instant response and response force by using the Matlab software.

Robust Design Strategy Applied to Vehicle Suspension System

2005 ◽  
Author(s):  
Marie-Maud Chatillon ◽  
Louis Je´ze´quel
Keyword(s):  
Design Process ◽  
Robust Design ◽  
Design Strategy ◽  
Suspension System ◽  
Optimization Process ◽  
Design Parameters ◽  
Vehicle Suspension ◽  
Hierarchical Optimization ◽  
Design Variables ◽  
Vehicle Suspension System

This paper presents a robust design strategy for the design of complex mechanical systems. The design strategy is based on the hierarchical optimization of design variables. Simplified physics-based models are used in each step of the design and optimization process. The steps of the design process correspond to the different stages of the design process, from functional requirements and decomposition, to design of the parts. Besides optimization, robustness of the solution to variations in design variables and environmental conditions is essential. The methodology presented in this paper is applied to the optimization of the design parameters of a vehicle suspension system. The consideration of parameters uncertainties from early design stages, the organization of the optimization process, and the use of simplified models, allow to obtain performant and robust design, and to manage design trade-offs.

Simulation-based robust design of complex product considering uncertainties of metamodel, design variables, and noise parameters

2016 ◽  
Vol 231 (20) ◽  
pp. 3715-3727 ◽  
Author(s):  
Zhenyu Liu ◽  
Xiang Peng ◽  
Chan Qiu ◽  
Jianrong Tan ◽  
Guifang Duan ◽  
...  
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Product Performance ◽  
Robust Design Optimization ◽  
Complex Product ◽  
Noise Parameters ◽  
Design Variables ◽  
Simulation Based ◽  
Metamodel Uncertainty ◽  
Performance Uncertainty

The uncertainties of design variables, noise parameters, and metamodel are important factors in simulation-based robust design optimization. Most conventional metamodel construction methods only consider one or two uncertainties. In this paper, a new surrogate modeling method simultaneously measuring all the uncertainties is proposed for simulation-based robust design optimization of complex product. The effect of metamodel uncertainty on product performance uncertainty is quantified through uncertainty propagation analysis among design variables uncertainty, noise parameters uncertainty, metamodel uncertainty, and performance uncertainty. Then, the sampling points are selected and the metamodel is constructed based on the predictive interval of product performance and mean square error of the Kriging metamodel. The constructed metamodel is applied to robust design optimization considering multiple uncertainties. Results of two mathematical examples show that the proposed metamodel considering multiple uncertainties increases the result accuracy of robust design optimization. Finally, the proposed algorithm is applied to robust design optimization of a heat exchanger, and the total heat transfer rate is enhanced under uncertainties of fin structural parameters, operation conditions parameters and simulation metamodel.

Lighting energy performance determination in office environments through implementation of EN 15193-1 for Turkey

2021 ◽  
pp. 147715352098742
Author(s):  
FŞ Yilmaz
Keyword(s):  
System Design ◽  
Architectural Design ◽  
Office Buildings ◽  
Energy Requirements ◽  
System Control ◽  
Lighting System ◽  
Artificial Lighting ◽  
Design Alternatives ◽  
Lighting Energy ◽  
The Impact

Office buildings are building typologies where efficient and optimal use of lighting energy is crucial while providing comfortable visual environments. The purpose of this study is to explore the impact of diverse architectural design alternatives on lighting energy requirements and lighting energy saving possibilities through a case study. In this study, a total of 3888 design alternatives are investigated in a comparative way in terms of daylighting system design alternatives, artificial lighting system design scenarios, artificial lighting system control types and shading system control options. Introducing the adaptation process of the EN 15193-1:2017 standard for Turkey’s specific climatic and geographical conditions and considering diverse lighting design scenarios, results of this parametric study aim to underline the significance of architectural design strategies in office buildings for the reduction of lighting energy requirements.

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