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additive-complex-structure

Design of Experiments, Calibration, and Machine Learning for Materials Simulation

This white paper discusses the role design of experiments (DOE), predictive machine learning (ML) models, and ML tools such as statistical calibration play in calibrating materials simulations to physical data, validating their accuracy, and maximizing the knowledge gained from their use. Points are illustrated through the use of examples.
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Design of Experiment

Design of Experiments and Data Sampling

As design cycles tighten and engineering challenges become more complex, companies must fully leverage their simulation and testing capabilities to remain competitive. Busy engineers are faced with the daunting task of collecting sufficient data to accurately represent the observed system, while controlling costs by minimizing the number of data samples required.
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decision-making

Decision Making under Uncertainty

As the world has entered uncertain times, companies and organizations must continue to reevaluate and adapt their decision-making processes to the ever-changing environment. Decisionmakers must accept that every decision made has some level of uncertainty – some greater than others.
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Pharmaceutical Industry

Uncertainty Quantification for Simulation and Modeling in the Pharmaceutical Industry

This whitepaper will focus on two simulation challenges, the computational cost of simulation and the need to perform calibration / validation of the model. Then the whitepaper will discuss strategies to alleviate them by leveraging SmartUQ’s predictive analytics and Uncertainty Quantification (UQ) toolset.
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Sensitivity Analytics

Sensitivity Analysis for Engineering Simulation

Many engineering simulations contain multiple inputs, making it critical for the engineer to identify the importance of each input to the overall model. This issue can be addressed by using Sensitivity Analysis (SA).
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Engineering Analytics

SmartUQ: Pushing the Boundaries of Engineering Analytics

As simulation, monitoring, and testing techniques become more sophisticated and produce increasing quantities of data, the need for a powerful engineering analytics tool to accurately and efficiently extract information and insights from the data has never been greater.
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Statistical Calibration

Statistical Calibration: Grounding Simulations in Reality

Unlike traditional calibration, SmartUQ’s statistical calibration considers the discrepancy between the simulation and physical results which reduces bias and increases the model’s accuracy.
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Additive Manufacturing

Uncertainty Quantification in 3D Printing

Deemed the “Third Industrial Revolution”, 3D printing appears to have endless capabilities, but a key barrier prevents additive manufacturing from reaching its full potential: uncertainty.
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Manufacturing Uncertainty

Quantifying Uncertainty for Manufacturing Variations

Manufacturing variations can be introduced from numerous sources such as imprecise equipment, varied raw material properties, and heat treatment processes. Variations lead to uncertain performance and may result in compromised parts or decreased service life.
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Statistical Methods

How Statistical Methods Can Help You Get More Out of Your Simulation Tools

This paper focuses on solutions to three major challenges in the CAE design processes and how statistical methods are a powerful and viable approach to meet these challenges.
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Lightweighting Bracket

SmartUQ Engineering Analytics Light-Weighting Application with COMSOL FEA Bracket Example

This paper documents an application of SmartUQ’s engineering analytics capabilities to a mounting bracket subject to fatigue testing.
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Digital Engineering Article

Digital Engineering Article: Take Uncertainty Out of Engineering Simulation

This article discusses how SmartUQ uncertainty quantification and analytics platform allows simulation to overcome ambiguities for better informed decision making.
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Medical Device

Uncertainty Quantification for the Medical Device Industry

This paper will introduce the challenges of creating medical devices, present UQ strategies to handle these challenges and conclude with a case study of an end-to-end coronary stent model validation study.
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Digital Twins

Introduction to SmartUQ Analytics and Digital Twins

This paper introduces SmartUQ’s predictive analytics capabilities and how analytics like Bayesian calibration can be applied to a digital twin.
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Digital Twin Motor

SmartUQ Case Study – Brushless Stator Motor Digital Twin

This case study walks through applying SmartUQ analytics to a digital twin of a brushless stator motor and how to quantify uncertainty surrounding the digital twin.
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Space Rocket

Vibrational Data Analysis Example Rocket Natural Frequencies

This example documents SmartUQ’s initial analysis of a vibrational mode data set from ground & flight simulations of the ARES launch vehicle. The goal was to show how the uncertainties in different physical properties could be propagated through a finite element model to assess which uncertainties might be important to the performance characteristics of the final flight vehicle.
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UP to UQ

Uncertainty Quantification Goes Beyond Uncertainty Propagation

Uncertainty Quantification encompasses Uncertainty Propagation techniques and other topics. This article discusses how Uncertainty Quantification goes beyond Uncertainty Propagation.
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MSC Adams View

Coupling SmartUQ's UQ & Predictive Analytics Toolset with MSC Adams View Multi-Body Dynamics Simulations

To highlight the integration between SmartUQ and Adams View, a case study of the quarter-vehicle toe angle undergoes a reliability analysis under uncertainty.
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Sports Car

Predictive Analytics for Simulation: Coupling SmartUQ’s Analytics Toolset with MSC Adams Car

To highlight the integration between SmartUQ and Adams Car, a case study of a simulated roll stability test maneuver undergoes a reliability analysis under uncertainty.
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Predictive Analytics

Fast & Easy Uncertainty Quantification via Predictive Analytics

Using a vehicle suspension system model, this paper compares the results of an uncertainty propagation and sensitivity analysis using either direct Monte Carlo or predictive analytics. Additionally, the paper walks through performing other advanced analytics using predictive models.
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