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Model granularity and related concepts
Models are integral to engineering design and basis for many decisions. Therefore, it is necessary to comprehend how a model’s properties might influence its behaviour. Model granularity is an important property but has so far only received limited attention. The terminology used to describe granularity and related phenomena varies and pertinent concepts are distributed across communities. This article positions granularity in the theoretical background of models, collects formal definitions for relevant terms from a range of communities and discusses the implications for engineering design
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location
This paper develops mfEGRA, a multifidelity active learning method using
data-driven adaptively refined surrogates for failure boundary location in
reliability analysis. This work addresses the issue of prohibitive cost of
reliability analysis using Monte Carlo sampling for expensive-to-evaluate
high-fidelity models by using cheaper-to-evaluate approximations of the
high-fidelity model. The method builds on the Efficient Global Reliability
Analysis (EGRA) method, which is a surrogate-based method that uses adaptive
sampling for refining Gaussian process surrogates for failure boundary location
using a single-fidelity model. Our method introduces a two-stage adaptive
sampling criterion that uses a multifidelity Gaussian process surrogate to
leverage multiple information sources with different fidelities. The method
combines expected feasibility criterion from EGRA with one-step lookahead
information gain to refine the surrogate around the failure boundary. The
computational savings from mfEGRA depends on the discrepancy between the
different models, and the relative cost of evaluating the different models as
compared to the high-fidelity model. We show that accurate estimation of
reliability using mfEGRA leads to computational savings of 46% for an
analytic multimodal test problem and 24% for a three-dimensional acoustic horn
problem, when compared to single-fidelity EGRA. We also show the effect of
using a priori drawn Monte Carlo samples in the implementation for the acoustic
horn problem, where mfEGRA leads to computational savings of 45% for the
three-dimensional case and 48% for a rarer event four-dimensional case as
compared to single-fidelity EGRA
Multi-disciplinary robust design of variable speed wind turbines
This paper addresses the preliminary robust multi-disciplinary design of small wind turbines. The turbine to be designed is assumed to be connected to the grid by means of power electronic converters. The main input parameter is the yearly wind distribution at the selected site, and it is represented by means of a Weibull distribution. The objective function is the electrical energy delivered yearly to the grid. Aerodynamic and electrical characteristics are fully coupled and modelled by means of low- and medium-fidelity models. Uncertainty affecting the blade geometry is considered, and a multi-objective hybrid evolutionary algorithm code is used to maximise the mean value of the yearly energy production and minimise its variance
GTTC Future of Ground Testing Meta-Analysis of 20 Documents
National research, development, test, and evaluation ground testing capabilities in the United States are at risk. There is a lack of vision and consensus on what is and will be needed, contributing to a significant threat that ground test capabilities may not be able to meet the national security and industrial needs of the future. To support future decisions, the AIAA Ground Testing Technical Committees (GTTC) Future of Ground Test (FoGT) Working Group selected and reviewed 20 seminal documents related to the application and direction of ground testing. Each document was reviewed, with the content main points collected and organized into sections in the form of a gap analysis current state, future state, major challenges/gaps, and recommendations. This paper includes key findings and selected commentary by an editing team
Marshall Space Flight Center Research and Technology Report 2019
Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come
Panel on future challenges in modeling methodology
This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing
A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing
This work introduces an innovative parallel, fully-distributed finite element
framework for growing geometries and its application to metal additive
manufacturing. It is well-known that virtual part design and qualification in
additive manufacturing requires highly-accurate multiscale and multiphysics
analyses. Only high performance computing tools are able to handle such
complexity in time frames compatible with time-to-market. However, efficiency,
without loss of accuracy, has rarely held the centre stage in the numerical
community. Here, in contrast, the framework is designed to adequately exploit
the resources of high-end distributed-memory machines. It is grounded on three
building blocks: (1) Hierarchical adaptive mesh refinement with octree-based
meshes; (2) a parallel strategy to model the growth of the geometry; (3)
state-of-the-art parallel iterative linear solvers. Computational experiments
consider the heat transfer analysis at the part scale of the printing process
by powder-bed technologies. After verification against a 3D benchmark, a
strong-scaling analysis assesses performance and identifies major sources of
parallel overhead. A third numerical example examines the efficiency and
robustness of (2) in a curved 3D shape. Unprecedented parallelism and
scalability were achieved in this work. Hence, this framework contributes to
take on higher complexity and/or accuracy, not only of part-scale simulations
of metal or polymer additive manufacturing, but also in welding, sedimentation,
atherosclerosis, or any other physical problem where the physical domain of
interest grows in time
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