ARC Researchers at the ASME
2015 International Design Engineering Technical Conferences
(August 2-5, 2015 at Boston, MA, USA)

ARC researchers (principal investigators in bold) will be presenting their latest research developments. Below are their papers which may include non-ARC funded research (paper titles in bold are ARC funded).

Monday, August 03, 2015

Session: DAC-17-1 Simulation-Based Design under Uncertainty I
Monday, August 03, 2015
08:00 AM-09:40 AM
DETC2015-46982 Development of a Conservative Model Validation Approach for Reliable Analysis
Min-yeong Moon, Kyung K. Choi, Hyunkyoo Cho, University of Iowa; Nicholas Gaul, RAMDO Solutions, LLC; David Lamb, David Gorsich, US ARMY TARDEC
Abstract: Simulation model validation is becoming vital for the simulation-based design process to provide manufacturing industries with the ability to design superior and reliable products in less time and at lower costs. Simulation models are approximate imitations of real-world systems and thus cannot exactly imitate a real-world system. The validated simulation model is to be developed to the degree needed for the intended purpose or application, such as reliability-based design optimization (RBDO). There are two challenges in simulation model validation: the lack of experimental data about a real-world system that inherently contains variability, and development of a validated simulation model. Due to the cost of product testing, experimental data in the context of model validation is limited for any given design. With this limited experimental data, the target output probability density function (PDF), which needs to be used for simulation model validation, cannot be correctly identified. In this paper, a new model validation approach is proposed to provide reliable simulation-based design using a conservative estimation of the simulation output PDF. The proposed method considers the uncertainty induced by insufficient experimental data. In the presence of limited experimental data, there are a number of possible output PDFs of the test results. These possible output PDFs are obtained using Bayesian analysis. Then, a PDF that is selected at a user-specified conservativeness level is obtained as the target output PDF. The calibration parameter and model bias are optimized to have a computer simulation model fit the target output PDF. For the optimization, accurate sensitivity of the validation measure is obtained using the complex variable method (CVM). As the target output PDF satisfies the user-specified conservativeness level, the validated simulation model provides a conservative representation of the experimental data. An eight-node cantilever beam is used to verify the accuracy of the sensitivity using CVM for the model-updating optimization. In addition, a simply supported beam is used to demonstrate the proposed method and convergence study. It is concluded that the proposed conservative model validation establishes the reliable and safe simulation model in the design application.
 
DETC2015-47359 Design Sensitivity Method for Sampling-Based RBDO With Fixed COV
Hyunkyoo Cho, Kyung K. Choi, University Of Iowa; David Lamb, US Army TARDEC; Ikjin Lee, University of Connecticut
Abstract: Conventional reliability-based design optimization (RBDO) uses the means of input random variables as its design variables; and the standard deviations (STDEVs) of the random variables are fixed constants. However, the fixed STDEVs may not correctly represent certain RBDO problems well, especially when a specified tolerance of the input random variable is presented as a percentage of the mean value. For this kind of design problem, the coefficients of variations (COVs) of the input random variables should be fixed, which means STDEVs are not fixed. In this paper, a method to calculate the design sensitivity of probability of failure for RBDO with fixed COV is developed. For sampling-based RBDO, which uses Monte Carlo simulation for reliability analysis, the design sensitivity of the probability of failure is derived using a first-order score function. The score function contains the effect of the change in the STDEV in addition to the change in the mean. As copulas are used for the design sensitivity, correlated input random variables also can be used for RBDO with fixed COV. Moreover, the design sensitivity can be calculated efficiently during the evaluation of the probability of failure. Using a mathematical example, the accuracy and efficiency of the developed method are verified. The RBDO result for mathematical and physical problems indicates that the developed method provides accurate design sensitivity in the optimization process.
 
Session: VIB-2-1 Structures and Continuous Systems I
Monday, August 03, 2015
08:00 AM-09:40 AM
DETC2015-48111 A Relative-Component Mode Synthesis Approach and Mode Acceleration: Application to Blisks with Large Blends and Mistuning
Yiqian Gan, John-Lavoie-Meyer, Kiran D’Souza, Olguta Marinescu, Bogdan I. Epureanu, University of Michigan
(not ARC funded)
Abstract: A method to perform component mode synthesis using relative coordinates is presented. The method can be used for constructing reduced order models and is complemented by a new node acceleration approach. The method can be applied to a large variety of systems. In particular, this presentation focuses on application to the study of the vibration of turbomachinery bladed disks (bliks) with large bends and small mistuning. In that context the method is shown to lead to very effective models which can accurately capture the dynamics despite the high geometric complexity of the system. In addition, the approach allows for seamlessly tackling systems with cracks also. The approach is shown to be particularly well suited for library-based and stochastic analyses. Numerical studies are used to demonstrate the effectiveness of the approach.
 
Session: DAC-17-2 Simulation-Based Design under Uncertainty II
Monday, August 03, 2015
10:00 AM-11:40 AM
DETC2015-46625 Flexible Design of Systems Considering Time-Dependent Reliability
Vijitashwa Pandey, Zissimos Mourelatos, Oakland University; Annette Skowronska, US Army TARDEC
(not ARC funded)
Abstract: Many repairable systems degrade with time and are subjected to time-varying loads. Their characteristics may change over time considerably, making the assessment of their performance and hence their design difficult. To address this issue, we introduce in this paper the concept of flexible design of repairable systems under time-dependent reliability considerations. In flexible design, the system can be modified in the future to accommodate uncertain events. As a result, regardless of how uncertainty resolves itself, a modification is available that will keep the system close to optimal, provided failure events have been properly characterized. We discuss how flexible design of repairable systems requires a fundamentally new approach and demonstrate its advantages using the design of a hydrokinetic turbine. Our results show that long-term metrics are improved when time-dependent characteristics and flexibility are considered together.
 
DETC2015-46823 Time-Dependent Reliability Using Metamodels With Transformed Random Inputs
Dorin Drignei, Zissimos Mourelatos, Ervisa Kosova, Igor Baseski, Oakland University
Abstract: We have recently proposed a method for time-dependent reliability based on metamodels with random inputs. In that method, we employed multiple sets of inputs sampled from the input distribution to construct a new metamodel as a mixture of classical metamodels. Because the sampled inputs may cluster around a mode of the input distribution, they may result in a metamodel of reduced quality. We address this issue in this paper by using a transformation to de-cluster the sample inputs and then use our previously developed metamodel with random inputs. We first obtain the output of the computer model for a limited number of transformed input draws which do not cluster in high probability regions of the input space. Then, conditioned on these transformed sampled inputs, we construct a classical Kriging surrogate and obtain the distribution of the new surrogate as the marginal of the joint distribution between the classical surrogate and the transformed sampled inputs. The proposed method is illustrated with a corroding beam example. A more accurate time-dependent reliability estimation is obtained compared with our previously developed metamodel method.
 
DETC2015-46847 Time Dependent Reliability Analysis of Vibratory Systems With Random Parameters
Zissimos Mourelatos, Monica Majcher, Vasileios Geroulas, Oakland University
Abstract: The field of random vibrations of large-scale systems with millions of degrees of freedom is of significant importance in many engineering disciplines. In this paper, we propose a method to calculate the time-dependent reliability of linear vibratory systems with random parameters excited by non-stationary Gaussian processes. The approach combines principles of random vibrations, the total probability theorem and recent advances in time-dependent reliability using an integral equation involving the up-crossing and joint up-crossing rates. A space-filling design, such as optimal symmetric Latin hypercube sampling, is first used to sample the input parameter space. For each design point, the corresponding conditional time-dependent probability of failure is calculated efficiently using random vibrations principles to obtain the statistics of the output process and an efficient numerical estimation of the up-crossing and joint up-crossing rates. A time-dependent metamodel is then created between the input parameters and the output conditional probabilities allowing us to estimate the conditional probabilities for any set of input parameters. The total probability theorem is finally applied to calculate the time-dependent probability of failure. The proposed method is demonstrated using a vibratory beam example.
 

Session: DTM-13-1 User Preferences
Monday, August 03, 2015
10:00 AM-11:40 AM

DETC2015-47908

Balancing Design Freedom and Brand Recognition in the Evolution of Automotive Brand Styling
Alex Burnap, University of Michigan; Jeffrey Hartley, General Motors; Yanxin Pan, Richard Gonzalez, Panos Papalambros, University Of Michigan
(not ARC funded)
Abstract: Designers faced with the task of developing the next model of a brand must balance several considerations. The design must be novel and express attributes important to the customers, while also recognizable as a representative of the brand. This balancing is left to the intuition of the designers, who must anticipate how all customers will perceive the new design. Oftentimes, the design freedom used to meet a styling attribute such as aggressiveness can compromise the recognition of the product as a member of the brand. In this paper, an experiment is conducted measuring change in ten styling attributes common to both design freedom and brand recognition for automotive designs, using customer responses to vehicle designs created interactively. Results show that, while brand recognition is highly dependent on the particular manufacturer, tradeoffs between design freedom and brand recognition may be measured using predictive models to inform strategic design decisions.
 

Session: DAC-9-2 Design of Engineering Materials and Structures II
Monday, August 03, 2015
10:00 AM-11:40 AM

DETC2015-46289 Numerical Methods for the Design of Meso-Structures: A Comparative Review
Marcus Yoder, Zachary Satterfield, Mohammad Fazelpour, Joshua Summers, Georges Fadel, Clemson University
(not ARC funded)
Abstract: Over the past decade, there has been an increase in the intentional design of meso-structured materials that are optimized to target desired material properties. This paper reviews and critically compares common numerical methodologies and optimization techniques used to design these meso-structures by analyzing the methods themselves and published applications and results. Most of the reviewed research targets mechanical material properties, including effective stiffness and crushing energy absorption. The numerical methodologies reviewed include topology and size/shape optimization methods such as homogenization, Solid Isotropic Material with Penalization, and level sets. The optimization techniques reviewed include genetic algorithms (GAs), particle swarm optimization (PSO), gradient based, and exhaustive search methods. The research reviewed shows notable patterns. The literature reveals a push to apply topology optimization in an ever-growing number of 3-dimensional applications. Additionally, researchers are beginning to apply topology optimization and size/shape optimization to multi-physics problems. The research also shows notable gaps. Although PSOs are comparable evolutionary algorithms to GAs, the use of GAs dominates over PSOs. These patterns and gaps, along with others, are discussed in terms of possible future research in the design of meso-structured materials.
 

Session: DAC-17-3 Simulation-Based Design under Uncertainty III
Monday, August 03, 2015
14:00 PM-15:40 PM

DETC2015-47370 Modified Bayesian Kriging for Noisy Response Problems for Reliability Analysis
Nicholas Gaul, RAMDO Solutions, LLC; Mary Kathryn Cowles, Kyung K. Choi, Hyunkyoo Cho, University of Iowa; David Lamb, US Army TARDEC
Abstract: This paper develops a new modified Bayesian Kriging (MBKG) surrogate modeling method for problems in which simulation analyses are inherently noisy and thus standard Kriging approaches fail to properly represent the responses. The purpose is to develop a method that can be used to carry out reliability analysis to predict probability of failure. The formulation of the MBKG surrogate modeling method is presented, and the full conditional distributions of the unknown MBKG parameters are presented. Using the full conditional distributions with a Gibbs sampling algorithm, Markov chain Monte Carlo is used to fit the MBKG surrogate model. A sequential sampling method that uses the posterior credible sets for inserting new design of experiment (DoE) sample points is proposed. The sequential sampling method is developed in such a way that the newly added DoE sample points will provide the maximum amount of information possible to the MBKG surrogate model, making it an efficient and effective way to reduce the number of DoE sample points needed. Therefore, the proposed method improves the posterior distribution of the probability of failure efficiently. To demonstrate the developed MBKG and sequential sampling methods, a 2-D mathematical example with added random noise is used. It is shown how, with the use of the sequential sample method, the posterior distribution of the probability of failure converges to capture the true probability of failure. A 3-D multibody dynamics (MBD) engineering block-car example illustrates an application of the new method to a simple engineering example for which standard Kriging methods fail.
 

Session: MSNDC-10-3 Vehicle Dynamics III
Monday, August 03, 2015
14:00 PM-15:40 PM

DETC2015-46173 Development of Shear Deformable Laminated Shell Element and its Application to ANCF Tire Model
Hiroki Yamashita, The University of Iowa; Paramsothy Jayakumar, US Army TARDEC; Hiroyuki Sugiyama, The University of Iowa
Abstract: In this investigation, a physics-based tire model for multibody vehicle dynamics simulation is developed using the laminated composite shell element based on the absolute nodal coordinate formulation with the transverse slope coordinates. The shell element accounts for the complex deformation coupling exhibited in fiber-reinforced composite rubber materials used in tires, and the element lockings are systematically eliminated by the assumed natural strain and enhanced strain approaches. Furthermore, various nonlinear material models including incompressible rubber material models can be considered for each layer in a way same as solid elements. The load-deflection curve and the contact patch lengths are validated against the test data to ensure that the fundamental structural tire properties can be correctly captured by the tire model.

 

Tuesday, August 04, 2015

Session: DAC-13-1 Multidisciplinary Design Optimization
Tuesday, August 04, 2015
11:40 AM-12:40 PM

DETC2015-46861 Decomposition-Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design
Alparslan Emrah Bayrak, Namwoo Kang, Panos Papalambros, University Of Michigan
(not ARC funded)
Abstract: Effective electrification of automotive vehicles requires designing the powertrain’s configuration along with sizing its components for a particular vehicle type. Employing planetary gear systems in hybrid electric vehicle powertrain architectures allows various architecture alternatives to be explored, including single-mode architectures that are based on a fixed configuration and multi-mode architectures that allow switching power flow configuration during vehicle operation. Previous studies have addressed the configuration and sizing problems separately. However, the two problems are coupled and must be optimized together to achieve system optimality. An all-in-one system solution approach to the combined problem is not viable due to the high complexity of the resulting optimization problem. In this paper we propose a partitioning and coordination strategy based on Analytical Target Cascading for simultaneous design of powertrain configuration and sizing for given vehicle applications. The capability of the proposed design framework is demonstrated by designing powertrains with one and two planetary gears for a mid-size passenger vehicle.
 
Session: DAC-13-2 Multidisciplinary Design Optimization
Tuesday, August 04, 2015
14:00 PM-15:00 PM
DETC2015-47002 Dual Residual for Distributed Augmented Lagrangian Coordination Based on Optimality Conditions
Meng Xu, Georges Fadel, Margaret M. Wiecek, Clemson University
(not ARC funded)
Abstract: Augmented Lagrangian Coordination (ALC) is one of the more popular coordination strategies for decomposition based optimization. It employs the augmented Lagrangian relaxation approach and has shown great improvements in terms of efficiency and solution accuracy when compared to other methods addressing the same type of problem. Additionally, by offering two variants: the centralized ALC in which an artificial master problem in the upper level is created to coordinate all the sub-problems in the lower level, and the distributed ALC in which coordination can be performed directly between sub-problems without a master problem, ALC provides more flexibility than other methods. However, the initial setting and the update strategy of the penalty weights in ALC still significantly affect its performance and thus are worth further research. For centralized ALC, the non-monotone weight update strategy based on the theory of dual residual has shown very good improvements over the traditional monotone update, in which the penalty weights can either increase or decrease. In this paper, we extend the research on the dual residual in centralized ALC to the distributed ALC. Through applying the Karush-Kuhn-Tucker (KKT) optimality conditions to the All-In-One (AIO) and decomposed problems, the necessary conditions for the decomposed solution to be optimal are derived, which leads to the definition of primal and dual residuals in distributed ALC. A new non-monotone weight based on both residuals is then proposed, by which all AIO KKT conditions are guaranteed after decomposition. Numerical tests are conducted on two mathematical problems and one engineering problem and the performances of the new update are compared to those of the traditional update. The results show that our proposed methods improve the process efficiency, accuracy, and robustness for distributed ALC.
 

Session: CIE-11-3 Design and Simulation for AM - I
Tuesday, August 04, 2015
15:30 PM-17:10 PM

DETC2015-47856 Optimization of Process Parameters in Laser Engineered Net Shaping (LENS) Deposition of Multi-Materials
Jingyuan Yan, Nafiseh Masoudi, Clemson University; Ilenia Battiato, San Diego State University; Georges Fadel, Clemson University
(not ARC funded)
Abstract: During the past few years, the requirement of metallic rapid prototyping technologies has evolved into direct fabrication of heterogeneous object composed of multiple materials with spatial variations. A heterogeneous object has potentially many advantages and in many cases can realize appearance and/or functionality that homogeneous objects cannot achieve, and a well-fabricated dense structure can increase the mechanical/physical properties of the part. To better control the fabrication quality and optimize the fabrication process, this article aims at determining a pre-process computing and decision algorithm based on the modeling of the LENS deposition of multiple materials. The optimization methodology is applied to the fabrication of cermet composite (using Inconel 718 and ceramic powders) given certain material feeding rates. The multi-objective optimization considers that the energy consumption and the waste of material during the fabrication process should be reduced simultaneously, while the probability of the melting of the powders should be maximized. The optimization software modeFRONTIER® is used to drive the computation procedure with the MATLAB process simulation code. The results show the design and objective spaces of the Pareto optimal solutions, and enable the engineer to select a setting from the obtained solutions based on their preference.

Wednesday, August 05, 2015

Session: DAC-10-1 Data-Driven Design
Wednesday, August 05, 2015
09:30 AM-11:10 AM

DETC2015-46836 Ecoracer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
Yi Ren, Arizona State University; Alparslan Emrah Bayrak, Panos Papalambros, University Of Michigan
(not ARC funded)
Abstract: We investigate the cost and benefit of crowdsourcing solutions to an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition, and received 2391 game plays by 124 anonymous players during the first week from the launch. We compare the performance of human players against that of the Efficient Global Optimization (EGO) algorithm. We show that while only a small portion of human players can outperform the algorithm in long term, players tend to formulate good heuristics early on, from where good solutions can be extracted and used to constrain the solution space. Incorporating this constraint into the search enhances the efficiency of the algorithm, even for problem settings different from the game. These findings indicate that human computation is promising in solving comprehensible and computationally hard optimal design and control problems.
 

Session: DAC-5-1 Design for Market Systems
Wednesday, August 05, 2015
11:40 AM-12:40 PM

DETC2015-46491 Autonomous Electric Vehicle Sharing System Design
Namwoo Kang, Fred M. Feinberg, Panos Papalambros, University Of Michigan
(not ARC funded)
Abstract: Car-sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: Autonomous vehicles eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero-emissions. Designing an Autonomous Electric Vehicle (AEV) fleet must account for the relationships among fleet operations, charging station operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four sub-system problems: Fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. A case study for an autonomous fleet operating in Ann Arbor, Michigan, is used to examine AEV sharing system profitability and feasibility for a variety of market scenarios.
 

Session: DFMLC-6-1 Design for Additive Manufacturing
Wednesday, August 05, 2015
15:30 PM-17:10 PM

DETC2015-47841 Manufacturing Functionally Gradient Material Objects With an Off the Shelf 3D Printer: Challenges and Solutions
Anthony Garland, Georges Fadel, Clemson University
(not ARC funded)
Abstract: This paper presents the challenges and solutions encountered while printing functionally gradient material (FGM) objects using an off the shelf Fused Deposition Modeling (FDM) 3D printer. The printer, Build Builder Dual-Feed Extruder from 3dprinter4u, has the unique design of extruding two different filaments out of one nozzle. Two separate motors independently pull two different filaments into one chamber causing plastic to extrude out of one nozzle. By using different filaments with different properties, an object with a material properties gradient can be manufactured. These material property gradients can help minimize the difficulty of bonding two different materials directly to each other, and give the designer the ability to tailor individual regions of one object to have different physical properties. Finding an optimal object design that can take advantage of this gradation is a unique problem that goes beyond the traditional solid modeling found in most CAD software. Once a design is obtained, slicing the object and creating an optimal build path is challenging because the gradation must be considered. Several objects were designed and manufactured to test the printers ability to print FGM objects. The python code used to add gradient change commands to the existing Gcode, and to slice a FGM flywheel while taking into account the material gradation has been made open source.