We invite you to learn from experts in engineering analytics and uncertainty quantification, see demonstrations, and explore how SmartUQ can improve your analysis.
June 18 - 1:30 PM to 3:30 PM EST
Presented by Gavin Jones, Sr. SmartUQ Application Engineer
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 18 - 4:00 PM to 6:00 PM EST
Presented by Gavin Jones, Sr. SmartUQ Application Engineer
Analyses of complex data streams using basic Uncertainty Quantification (UQ) methods do not always yield the results sought. The emergence of complex data from modern computer simulations has spawned new UQ methods for extracting meaningful information. Complex simulations can be time-consuming to run, have a temporal, functional, or transient response, contain calibration parameters, or involve inputs of aleatoric and epistemic uncertainties. This course discusses the challenges of complex data from such simulations and emerging technologies like Digital Thread – Digital Twin, the new UQ methods, and the results they yield.
June 16 - 4:30 PM to 5:00 PM EST
Presented by Ray McConnell, SmartUQ Application Engineer
Statistical calibration can reduce design cycle time and costs by ensuring that simulations are as close to reality as possible and by quantifying how close that really is. It optimizes tuning of model parameters to improve simulation accuracy, and estimates any remaining discrepancy which is useful for model diagnosis and validation. Because model discrepancy is assumed to exist in this framework, it enables robust calibration even for inaccurate models. The presentation will introduce the concepts and advantages of statistical calibration and model validation. Using an air foil CFD model case study, this presentation will walk through the step‐by‐step process of performing statistical calibration and quantification of the uncertainty of the final calibrated model. An analysis will be performed to compare results from a calibrated model and an uncalibrated model. The analysis will include optimization and sensitivity analysis. The results will illustrate the importance of calibrating a model before drawing design conclusions.
June 17 - 11:00 AM to 11:30 AM EST
Presented by Gavin Jones, Sr. SmartUQ Application Engineer
This study presents a statistical approach for prediction and uncertainty quantification of a hepatic tumor Microwave Ablation treatment simulation with a spatial and transient response. The study examines the variation in the volume of necrotic tissue in a human liver tissue model during Microwave Ablation/Coagulation therapy due to tissue property variation as a function of wattage and treatment time.
June 17 - 4:00 PM to 4:30 PM EST
Presented by Gavin Jones, Sr. SmartUQ Application Engineer
This tutorial focuses on statistical model calibration, a process used to quantify the uncertainties in the simulation model which provides an understanding of this mismatch and a means to narrow the simulation – physical test gap. Using a case study, the tutorial will sequentially walk through model calibration process used to quantify uncertainties for simulations and physical experiments.
June 17 - 4:30 PM to 5:00 PM EST
Presented by Gavin Jones, Sr. SmartUQ Application Engineer
This presentation illustrates both conceptual and practical applications of using Uncertainty Quantification (UQ) techniques to perform probabilistic analyses. The application of UQ techniques to the output from engineering analyses using model-based approaches is essential to providing critical decision-quality information at key decision points in a aerospace system’s life cycle. Approaches will be presented for the continued collection and application of UQ knowledge over each stage of a generalized life cycle framework covering system design, manufacture, and sustainment. The use of this approach allows engineers to quantify and reduce uncertainties systematically and provides decision makers with probabilistic assessments of performance, risk, and costs which are essential to critical decisions. As an illustration, a series of probabilistic analyses performed as part of the initial design of a turbine blade will be used to demonstrate the utility of UQ in identifying program risks and improving design quality. The application of UQ concepts to life cycle management will be addressed, highlighting the benefits to decision makers of having actionable engineering information throughout a system’s life cycle.
June 18 - 11:00 AM to 11:30 AM EST
Presented by Ray McConnell, SmartUQ Application Engineer
The process and results of using engineering analytics methods will be presented for three unique automotive applications in order to demonstrate the utility of performing advanced analytical techniques in a variety of scenarios. The engineering analytics solutions for these applications have been successfully tested on real industry challenges. The applications to be demonstrated are:
June 18 - 5:00 PM to 5:30 PM EST
Presented by Gavin Jones, Sr. SmartUQ Application Engineer
This presentation will discuss the role of anlaytics in the Digital Twin, the industrial challenges and benefits, and how Uncertainty Quantification and other analytics are the solution to building and running an efficient and accurate Digital Twin.
