We invite you to stop by our booth #806 at ASME Turbo Expo 2018; meet experts in engineering analytics and uncertainty quantification, see demonstrations, and explore how SmartUQ can improve your analysis.
June 12 - 14:00 PM to 15:30 PM GMT+2 - Room: E-6
Presented by Dr. Mark Andrew, SmartUQ Technology Steward
Experienced practitioners who construct complex simulation models of critical systems know that replicating real-world performance is challenging due to uncertainties in found in simulation and physical tests. This course will discuss the types of uncertainties in the context of representing them with a design of experiments, constructing surrogate models and finally applying analytical methods to understand how sources of uncertainty impact replicating reality. This course will discuss the broad applications these probabilistic techniques have in analyzing numerous forms of engineering systems including Digital Thread/Digital Twins.
June 14 - 16:00 PM to 17:30 PM GMT+2 - Event Room
Presented by Dr. Mark Andrew, SmartUQ Technology Steward
This tutorial will provide an overview of UQ concepts and methodology and discuss the strategies for addressing these challenges. One of the strategies is performing statistical calibration to understand how well numerical simulation represents reality. Using a case study for illustration, the tutorial will sequentially walk through the statistical calibration process used to quantify uncertainties for simulations and physical experiments. Attendees should leave the tutorial with an understanding of UQ concepts and techniques, how to apply statistical calibration to their combined simulation and testing environments, and the fundamental value that UQ brings.
June 11 - 8:40 AM to 9:20 AM GMT+2 - Room: E-8
Presented by Dr. Mark Andrew, SmartUQ Technology Steward
The growing use of simulations in the engineering design process promises to reduce the need for extensive physical testing, decreasing both development time and cost. Statistical calibration optimizes tuning of model parameters to improve simulation accuracy, and estimates any remaining discrepancy which is useful for model diagnosis and validation. Also, because model discrepancy is assumed to exist in this framework, it enables robust calibration even for inaccurate models. The presentation will show an application of statistical calibration to a bracket design fatigue model simulated in COMSOL. The calibration will tune the bracket’s material properties and fatigue characteristics. For illustrative purposes, the physical test data will be generated using the same simulation routine, but with an intentionally applied bias and random noise to replicate physical testing errors. The accuracy and conclusions from the statistically calibrated model will be compared with the uncalibrated model as well as a model calibrated with conventional error minimization methods. The metrics used in the comparison will include optimization, sensitivity analysis, and propagation of uncertainties motivated by sources like manufacturing variations during bracket fabrication. The results will illustrate the importance of calibrating a model before drawing design conclusions. Multiple metrics will be shown which can be used for model validation, including a discrepancy map which characterizes inadequacies in the simulation.
June 12 - 14:00 PM to 14:30 AM GMT+2 - Exhibit Hall
Presented by John Randazzo, SmartUQ Account Executive
SmartUQ is a predictive analytics tool for reducing the time, cost, and risk of solving complex data and engineering problems. With the evergrowing abundance of complex systems and data, SmartUQ's engineering analytics and Uncertainty Quantification tools have the solutions to fundamental questions like: What data (quantity and quality) do I need?, What resulting information is actionable?, and How do I do this efficiently under uncertainty?
