Search NASA⌕ Search

DOE OSTI · 1599276

Characterizing Dynamic Test Fixtures Through the Modal Projection Error

Abstract

Across many industries and engineering disciplines, physical components and systems of components are designed and deployed into their environment of intended use. It is the desire of the design agency to be able to predict whether their component or system will survive its physical environment or if it will fail due to mechanical stresses. One method to determine if the component will survive the environment is to expose the component to a simulation of the environment in a laboratory. One difficulty in doing this is that the component may not have the same boundary condition in the laboratory as is in the field configuration. This paper presents a novel method of quantifying the error in the modal domain that occurs from the impedance difference between the laboratory test fixture and the next level of assembly in the field configuration. The error is calculated from the projection from one mode shape space to the other, and the error is in terms of each mode of the field configuration. This provides insight into the effectiveness of the test fixture with respect to the ability to recreate the mode shapes of the field configuration. A case study is presented to show that the error in the modal projection between two configurations is a lower limit for the error that can be achieved by a laboratory test.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Schoenherr, Tyler Franklin, Rouse, Jerry W., Harvie, Julie. 2020-01-01. Characterizing Dynamic Test Fixtures Through the Modal Projection Error. https://doi.org/10.2172/1599276

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗