Search NASA⌕ Search

DOE OSTI · 2429978

Data Based Shock Test Specification

Abstract

It is vital that avionic packages used for testing and certifying the reliability and safety of U.S. nuclear weapons with platform aircraft survive exposure to shock environments during transportation and delivery. The objective of this research was to characterize the response to these transportation shock environments delivering accurate shock test specifications in order to set laboratory programming material and device certification rigor. Responses to shock events were analyzed in the frequency domain via the Shock Response Spectrum (SRS). Shocks were then grouped based on respective behavior of maximum response accelerations which were pseudorandomly resampled and compared to test data to form test specifications based on the MinerPalmgren hypothesis. In addition to discovering significant over testing in current shock specifications, a new systematic, data-driven approach to designing shock specifications was formulated.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gilliam, James Stuart, Montoya, Angela Cork, Payne, Lloyd Rus. 2024-05-01. Data Based Shock Test Specification. https://doi.org/10.2172/2429978

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↗