Your Selections

Nikolaidis, Efstratios
Show Only

Collections

File Formats

Content Types

Dates

Sectors

Topics

Authors

Publishers

Affiliations

Events

   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Separable and Standard Monte Carlo Simulation of Linear Dynamic Systems Using Combined Approximations

SAE International Journal of Commercial Vehicles

Grand Valley State University, USA-Mahdi Norouzi
University of Toledo, USA-Efstratios Nikolaidis
  • Journal Article
  • 02-12-02-0008
Published 2019-01-25 by SAE International in United States
Reliability analysis of a large-scale system under random dynamic loads can be a very time-consuming task since it requires repeated studies of the system. In many engineering problems, for example, wave loads on an offshore platform, the excitation loads are defined using a power spectral density (PSD) function. For a given PSD function, one needs to generate many time histories to make sure the excitation load is modeled accurately. Global and local approximation methods are available to predict the system response efficiently. Each way has their advantages and shortcomings. The combined approximations (CA) method is an efficient method, which combines the advantages of local and global approximations. This work demonstrates two methodologies that utilize CA to reduce the cost of crude or separable Monte Carlo simulation (MCS) of linear dynamic systems when the excitation loads are defined using PSD functions. The system response is only calculated at a few frequencies within the range of the PSD function, and CA is used to estimate the response for the other frequencies of excitation. This approach significantly reduces…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Random Vibration Analysis Using Quasi-Random Bootstrapping

University of Toledo-Md Asad Rahman, Efstratios Nikolaidis
Published 2018-04-03 by SAE International in United States
Reliability analysis of engineering structures such as bridges, airplanes, and cars require calculation of small failure probabilities. These probabilities can be calculated using standard Monte Carlo simulation, but this method is impractical for most real-life systems because of its high computational cost.Many studies have focused on reducing the computational cost of a reliability assessment. These include bootstrapping, Separable Monte Carlo, Importance Sampling, and the Combined Approximations. The computational cost can also be reduced using an efficient method for deterministic analysis such as the mode superposition, mode acceleration, and the combined acceleration method. This paper presents and demonstrates a method that uses a combination of Sobol quasi-random sequences and bootstrapping to reduce the number of function calls.The study demonstrates that the use of quasi-random numbers in conjunction bootstrapping reduces dramatically computational cost.
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Value of Information for Comparing Dependent Repairable Assemblies and Systems

SAE International Journal of Materials and Manufacturing

University of Toledo-Shawn P. Capser, Efstratios Nikolaidis
  • Journal Article
  • 2018-01-1103
Published 2018-04-03 by SAE International in United States
This article presents an approach for comparing alternative repairable systems and calculating the value of information obtained by testing a specified number of such systems. More specifically, an approach is presented to determine the value of information that comes from field testing a specified number of systems in order to appropriately estimate the reliability metric associated with each of the respective repairable systems. Here the reliability of a repairable system will be measured by its failure rate. In support of the decision-making effort, the failure rate is translated into an expected utility based on a utility curve that represents the risk tolerance of the decision-maker. The algorithm calculates the change of the expected value of the decision with the sample size. The change in the value of the decision represents the value of information obtained from testing. The approach uses a Bayesian probability model, which allows the decision-maker to incorporate subjective priors on the reliability performance of the design alternatives. The dependency is modeled using copulas to couple the marginal prior distributions of the alternatives…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Assessing the Value of Information for Multiple, Correlated Design Alternatives

SAE International Journal of Commercial Vehicles

University of Toledo-Shawn P. Capser, Efstratios Nikolaidis
  • Journal Article
  • 2017-01-0208
Published 2017-03-28 by SAE International in United States
Design optimization occurs through a series of decisions that are a standard part of the product development process. Decisions are made anywhere from concept selection to the design of the assembly and manufacturing processes. The effectiveness of these decisions is based on the information available to the decision maker. Decision analysis provides a structured approach for quantifying the value of information that may be provided to the decision maker. This paper presents a process for determining the value of information that can be gained by evaluating linearly correlated design alternatives. A unique approach to the application of Bayesian Inference is used to provide simulated estimates in the expected utility with increasing observations sizes. The results provide insight into the optimum observation size that maximizes the expected utility when assessing correlated decision alternatives.
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Inverse Modeling: Theory and Engineering Examples

University of Toledo-Rahul Rama Swamy Yarlagadda, Efstratios Nikolaidis, Vijay Kumar Devabhaktuni
Published 2016-04-05 by SAE International in United States
Over the last two decades inverse problems have become increasingly popular due to their widespread applications. This popularity continuously demands designers to find alternative methods, to solve the inverse problems, which are efficient and accurate. It is important to use effective techniques that are both accurate and computationally efficient. This paper presents a method for solving inverse problems through Artificial Neural Network (ANN) theory. The paper also presents a method to apply Grey Wolf optimizer (GWO) algorithm to inverse problems. GWO is a recent optimization method producing superior results. Both methods are then compared to traditional methods such as Particle Swarm Optimization (PSO) and Markov Chain Monte Carlo (MCMC). Four typical engineering design problems are used to compare the four methods. The results show that the GWO outperforms other methods both in terms of efficiency and accuracy. The error is comparable among the ANN and PSO methods, while the latter has better computational efficiency.
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Multi-Level Decoupled Optimization of Wind Turbine Structures

University of Toledo-Jin Woo Lee, Efstratios Nikolaidis, Vijay Devabhaktuni
Published 2015-04-14 by SAE International in United States
This paper proposes a multi-level decoupled method for optimizing the structural design of a wind turbine blade. The proposed method reduces the design space by employing a two-level optimization process. At the high-level, the structural properties of each section are approximated by an exponential function of the distance of that section from the blade root. High-level design variables are the coefficients of this approximating function. Target values for the structural properties of the blade are determined at that level. At the low-level, sections are divided into small decoupled groups. For each section, the low-level optimizer finds the thickness of laminate layers with a minimum mass, whose structural properties meet the targets determined by the high-level optimizer. In the proposed method, each low-level optimizer only considers a small number of design variables for a particular section, while traditional, single-level methods consider all design variables simultaneously. The proposed method converges to the optimum solution faster for a problem with a large number of design variables, because it divides the design space into multiple subspaces, each containing a…
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Bootstrapping and Separable Monte Carlo Simulation Methods Tailored for Efficient Assessment of Probability of Failure of Structural Systems

SAE International Journal of Materials and Manufacturing

University of Toledo-Musarrat Jehan, Efstratios Nikolaidis
  • Journal Article
  • 2015-01-0420
Published 2015-04-14 by SAE International in United States
There is randomness in both the applied loads and the strength of systems. Therefore, to account for the uncertainty, the safety of the system must be quantified using its reliability. Monte Carlo Simulation (MCS) is widely used for probabilistic analysis because of its robustness. However, the high computational cost limits the accuracy of MCS. Smarslok et al. [2010] developed an improved sampling technique for reliability assessment called Separable Monte Carlo (SMC) that can significantly increase the accuracy of estimation without increasing the cost of sampling. However, this method was applied to time-invariant problems involving two random variables. This paper extends SMC to problems with multiple random variables and develops a novel method for estimation of the standard deviation of the probability of failure of a structure. The method is demonstrated and validated on reliability assessment of an offshore wind turbine under turbulent wind loads. The results show the method can reduce the computational cost by two orders of magnitude compared to standard MCS for failure probabilities as low as 10−4.
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Combined Approximation for Efficient Reliability Analysis of Linear Dynamic Systems

Frostburg State University-Mahdi Norouzi, Zachary Crawford
University of Toledo-Efstratios Nikolaidis
Published 2015-04-14 by SAE International in United States
The Combined Approximation (CA) method is an efficient reanalysis method that aims at reducing the cost of optimization problems. The CA uses results of a single exact analysis, and it is suitable for different types of structures and design variables. The second author utilized CA to calculate the frequency response function of a system at a frequency of interest by using the results at a frequency in the vicinity of that frequency. He showed that the CA yields accurate results for small frequency perturbations. This work demonstrates a methodology that utilizes CA to reduce the cost of Monte Carlo simulation (MCs) of linear systems under random dynamic loads. The main idea is to divide the power spectral density function (PSD) of the input load into several frequency bins before calculating the load realizations. The system response is only calculated at the central frequency of each bin; for other frequencies the system response is approximated via CA instead of full system analysis. This approach significantly increases the efficiency of the simulation as the approach performs an…
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Reliability Analysis of Composite Inflatable Space Structures Considering Fracture Failure

University of Toledo-Jin Woo Lee, Efstratios Nikolaidis
Published 2014-04-01 by SAE International in United States
Inflatable space structures can have lower launching cost and larger habitat volume than their conventional rigid counterparts. These structures are made of composite laminates, and they are flexible when folded and partially inflated. They contain light-activated resins, and can be cured with the sun light after being inflated in space.A spacecraft can burst due to cracks caused by meteor showers or debris. Therefore, it is critical to identify the important fracture failure modes, and assess their probability. This information will help a designer minimize the risk of failure and keep the mass and cost low.This paper presents a probabilistic approach for finding the required thickness of an inflatable habitat shell for a prescribed reliability level, and demonstrates the superiority of probabilistic design to its deterministic counterpart.
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Probability of Failure of Dynamic Systems by Importance Sampling

SAE International Journal of Materials and Manufacturing

Univ of Toledo-Mahdi Norouzi, Efstratios Nikolaidis
  • Journal Article
  • 2013-01-0607
Published 2013-04-08 by SAE International in United States
Estimation of the probability of failure of mechanical systems under random loads is computationally expensive, especially for very reliable systems with low probabilities of failure. Importance Sampling can be an efficient tool for static problems if a proper sampling distribution is selected. This paper presents a methodology to apply Importance Sampling to dynamic systems in which both the load and response are stochastic processes. The method is applicable to problems for which the input loads are stationary and Gaussian and are represented by power spectral density functions. Shinozuka's method is used to generate random time histories of excitation. The method is demonstrated on a linear quarter car model. This approach is more efficient than standard Monte Carlo simulation by several orders of magnitude.
Annotation ability available