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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Dynamic Modeling of Gaseous Multicomponent Trace Contaminant Adsorption

Activated carbon is a porous material in the Trace Contaminant Control (TCC) system that physically adsorbs volatile organic compounds (VOCs) generated within spacecraft and spacesuit environments. Several isotherm models exist to predict adsorption equilibria for processes involving multicomponent systems. This paper investigates the use of Ideal Adsorbed Solution Theory (IAST) for predicting multicomponent trace contaminant adsorption behavior using single-component isotherms based on potential theory. Developing simulations for experimental breakthrough curves will gauge the validity on the sizing and design of TCC architecture. Model results on predicting bed performance and roll-up effects are compared with available multicomponent test data.

Ideal Adsorbed Solution Theory (IAST)↗
NASA NTRS2020

Dynamic Modeling, Simulation, and Analysis of Orbiting Sample Capture for Potential Mars Sample Return

The current notional architecture for the Mars Sample Return (MSR) campaign would require autonomous on-orbit rendezvous and capture of a sample container after it has been delivered to Mars orbit by a preceding MSR mission. As part of the complete orbital payload known as the Capture, Contain, and Return System (CCRS), a concept has been developed for autonomously sensing and capturing this Orbiting Sample (OS) container. The concept, comprised of a capture mechanism and multiple optical break-beam arrays, would detect and enclose the OS into CCRS, and engage a dust-tight seal to prevent the escape of the OS or other unsterilized particles. A challenging goal for the system is to close the lid prior to the OS coming into physical contact after entering CCRS in order to reduce the risk of unsterilized Mars particles on the surface of the OS from contaminating the outside of the spacecraft. The mechanical and sensor performance requirements were driven by the broader architecture of CCRS, OS rendezvous parameters, and Planetary Protection requirements regarding the interaction and handling of the OS. The mechanism and break-beam array concepts were designed accordingly, and kinematic simulations of the mechanism’s behavior, combined with a Monte-Carlo simulation of OS rendezvous/collision behaviors and corresponding breakbeam sensor responses verify compliance.

Ishigo, Alyssa↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.

97 MATHEMATICS AND COMPUTING↗