Confidence Estimates due to Uncertainty in Multi-Disciplinary Computational Analysis

2013 ◽  
Author(s):  
Animesh Dey ◽  
Robert Tryon
Keyword(s):  
Computational Analysis ◽  
Input Parameter ◽  
Virtual Simulation ◽  
System Response ◽  
Model Refinement ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Final Output ◽  
Input Variables ◽  
Confidence Estimates

Simulation-based design and certification is fundamentally about making decisions with uncertainty. However, minimizing uncertainty comes at a price — more testing to better define the variability in input parameters, higher fidelity analyses at a finer scale to limit the uncertainty in the physics, etc. Variability in each input parameter does not affect the uncertainty in the system response equally. Nor does every model refinement reduce the uncertainty in the system response. This paper presents a computational methodology that estimates the sensitivity of uncertainty in input variables and the sensitivity of modeling approximations to the final output. In the current age of large multi-disciplinary virtual simulation, this is useful in determining how to minimize overall uncertainty in analytical predictions. In addition, the methodology can be used to optimize for the best use of computational and testing resources to arrive at most robust predictions.

Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty

2004 ◽  
Author(s):  
Wei Chen ◽  
Ruichen Jin ◽  
Agus Sudjianto
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Simulation Models ◽  
Design Under Uncertainty ◽  
Global Sensitivity ◽  
Functional Relationships ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Input Variables ◽  
The Impact

The importance of sensitivity analysis in engineering design cannot be over-emphasized. In design under uncertainty, sensitivity analysis is performed with respect to the probabilistic characteristics. Global sensitivity analysis (GSA), in particular, is used to study the impact of variations in input variables on the variation of a model output. One of the most challenging issues for GSA is the intensive computational demand for assessing the impact of probabilistic variations. Existing variance-based GSA methods are developed for general functional relationships but require a large number of samples. In this work, we develop an efficient and accurate approach to GSA that employs analytic formulations derived from metamodels of engineering simulation models. We examine the types of GSA needed for design under uncertainty and derive generalized analytical formulations of GSA based on a variety of metamodels commonly used in engineering applications. The benefits of our proposed techniques are demonstrated and verified through both illustrative mathematical examples and the robust design for improving vehicle handling performance.

PDE Computing Model of Internet Virtual Simulation Based on ANSYS

2014 ◽  
Vol 556-562 ◽  
pp. 6106-6110
Author(s):  
Jin Gen Tang ◽  
Ji Yuan Deng
Keyword(s):  
Parallel Computing ◽  
Computing System ◽  
Simulation System ◽  
Virtual Simulation ◽  
Machining Process ◽  
System Response ◽  
Computing Model ◽  
Simulation Based ◽  
Response Performance ◽  
Parallel Link

This paper establishes a parallel Internet virtual simulation PDE computing model based on quasi-linear, linear and nonlinear mathematical principles of computer, and obtains a partial differential equation for parallel computing system. The parallel computing can greatly improve the speed and accuracy of computer virtual simulation system. In order to validate the calculation, the paper uses ACC-8S interface board to realize the parallel link of computer, and two pieces of different material deformation in machining process was simulated by ANSYS software, and it concludes the contour of deformation . Finally, the system has been applied to the volleyball match virtual simulation system, and it gets the system response performance through simulation and calculation, which provides a technical reference for the training of volleyball players.

Multi-Model Bayesian Optimization for Simulation-Based Design (DETC2020-22651)

2021 ◽  
pp. 1-39
Author(s):  
Siyu Tao ◽  
Anton van Beek ◽  
Daniel Apley ◽  
Wei Chen
Keyword(s):  
Simulation Models ◽  
Global Optimum ◽  
Bayesian Optimization ◽  
System Response ◽  
Engineering Systems ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Need To Evaluate ◽  
Complex Engineering ◽  
Component Models

Abstract We enhance the Bayesian optimization (BO) approach for simulation-based design of engineering systems consisting of multiple interconnected expensive simulation models. The goal is to find the global optimum design with minimal model evaluation costs. A commonly used approach is to treat the whole system as a single expensive model and apply an existing BO algorithm. This approach is inefficient due to the need to evaluate all the component models in each iteration. We propose a multi-model BO approach that dynamically and selectively evaluates one component model per iteration based on the uncertainty quantification of linked emulators (metamodels) and the knowledge gradient of system response as the acquisition function. Building on our basic formulation, we further solve problems with constraints and feedback couplings that often occur in real complex engineering design by penalizing the objective emulator and reformulating the original problem into a decoupled one. The superior efficiency of our approach is demonstrated through solving two analytical problems and the design optimization of a multidisciplinary electronic packaging system.

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

scholarly journals Investigating Preconditions for Sustainable Renewable Energy Product–Service Systems in Retail Electricity Markets

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1877
Author(s):  
Widha Kusumaningdyah ◽  
Tetsuo Tezuka ◽  
Benjamin C. McLellan
Keyword(s):  
Renewable Energy ◽  
Electricity Markets ◽  
Service Systems ◽  
Energy Market ◽  
Energy Product ◽  
Simulation Based Design ◽  
Product Service ◽  
Simulation Based ◽  
Sustainable Renewable Energy ◽  
Product Service Systems

Energy transitions are complex and involve interrelated changes in the socio-technical dimensions of society. One major barrier to renewable energy transitions is lock-in from the incumbent socio-technical regime. This study evaluates Energy Product–Service Systems (EPSS) as a renewable energy market mechanism. EPSS offer electricity service performance instead of energy products and appliances for household consumers. Through consumers buying the service, the provider company is enabled to choose, manage and control electrical appliances for best-matched service delivery. Given the heterogenous market players and future uncertainties, this study aims to identify the necessary conditions to achieve a sustainable renewable energy market. Simulation-Based Design for EPSS framework is implemented to assess various hypothetical market conditions’ impact on market efficiency in the short term and long term. The results reveal the specific market characteristics that have a higher chance of causing unexpected results. Ultimately, this paper demonstrates the advantage of implementing Simulation-Based Design for EPSS to design retail electricity markets for renewable energy under competing market mechanisms with heterogenous economic agents.

scholarly journals Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints

2021 ◽  
pp. 247263032110217
Author(s):  
Takeshi D. Itoh ◽  
Takaaki Horinouchi ◽  
Hiroki Uchida ◽  
Koichi Takahashi ◽  
Haruka Ozaki
Keyword(s):  
Scheduling Algorithms ◽  
Mixed Integer ◽  
Time Constraints ◽  
Live Cells ◽  
Laboratory Automation ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Optimal Allocations ◽  
Scheduling Method ◽  
Time Required

In automated laboratories consisting of multiple different types of instruments, scheduling algorithms are useful for determining the optimal allocations of instruments to minimize the time required to complete experimental procedures. However, previous studies on scheduling algorithms for laboratory automation have not emphasized the time constraints by mutual boundaries (TCMBs) among operations, which is important in procedures involving live cells or unstable biomolecules. Here, we define the “scheduling for laboratory automation in biology” (S-LAB) problem as a scheduling problem for automated laboratories in which operations with TCMBs are performed by multiple different instruments. We formulate an S-LAB problem as a mixed-integer programming (MIP) problem and propose a scheduling method using the branch-and-bound algorithm. Simulations show that our method can find the optimal schedules of S-LAB problems that minimize overall execution time while satisfying the TCMBs. Furthermore, we propose the use of our scheduling method for the simulation-based design of job definitions and laboratory configurations.

Energy-saving design of variable-displacement bi-directional pump-controlled electrohydraulic system

2020 ◽  
pp. 095965182097389
Author(s):  
Samir Kumar Hati ◽  
Nimai Pada Mandal ◽  
Dipankar Sanyal
Keyword(s):  
Energy Saving ◽  
Absolute Error ◽  
Fast Response ◽  
Maximum Energy ◽  
Radial Clearance ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Displacement Pump ◽  
Variable Displacement ◽  
Axial Piston

Losses in control valves drag down the average overall efficiency of electrohydraulic systems to only about 22% from nearly 75% for standard pump-motor sets. For achieving higher energy efficiency in slower systems, direct pump control replacing fast-response valve control is being put in place through variable-speed motors. Despite the promise of a quicker response, displacement control of pumps has seen slower progress for exhibiting undesired oscillation with respect to the demand in some situations. Hence, a mechatronic simulation-based design is taken up here for a variable-displacement pump–controlled system directly feeding a double-acting single-rod cylinder. The most significant innovation centers on designing an axial-piston pump with an electrohydraulic compensator for bi-directional swashing. An accumulator is conceived to handle the flow difference in the two sides across the load piston. A solenoid-driven sequence valve with P control is proposed for charging the accumulator along with setting its initial gas pressure by a feedforward design. Simple proportional–integral–derivative control of the compensator valve is considered in this exploratory study. Appropriate setting of the gains and critical sizing of the compensator has been obtained through a detailed parametric study aiming low integral absolute error. A notable finding of the simulation is the achievement of the concurrent minimum integral absolute error of 3.8 mm s and the maximum energy saving of 516 kJ with respect to a fixed-displacement pump. This is predicted for the combination of the circumferential port width of 2 mm for the compensator valve and the radial clearance of 40 µm between each compensator cylinder and the paired piston.

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