Search NASA⌕ Search

SEARCH · Search NASA

Results for “minimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Global Precipitation Measurement (GPM) and International Space Station (ISS) Coordination for CubeSat Deployments to Minimize Collision Risk

The Global Precipitation Measurement Mission (GPM) is a joint U.S. and Japan mission to observe global precipitation, extending the Tropical Rainfall Measuring Mission (TRMM), which was launched by H-IIA from Tanegashima in Japan on February 28TH, 2014 directly into its 407km operational orbit. The International Space Station (ISS) is an international human research facility operated jointly by Russia and the USA from NASA's Johnson Space Center (JSC) in Houston Texas. Mission priorities lowered the operating altitude of ISS from 415km to 400km in early 2105, effectively placing both vehicles into the same orbital regime. The ISS has begun a program of deployments of cost effective CubeSats from the ISS that allow testing and validation of new technologies. With a major new asset flying at the same effective altitude as the ISS, CubeSat deployments became a serious threat to GPM and therefore a significant indirect threat to the ISS. This paper describes the specific problem of collision threat to GPM and risk to ISS CubeSat deployment and the process that was implemented to keep both missions safe from collision and maximize their project goals.

Pawloski, James H.↗
NASA NTRS2017

Minimizing Human Risk: Human Performance Models in the Human Factors and Behavioral Performance Element

Human space exploration has never been more exciting than it is today. Human presence to outer worlds is becoming a reality as humans are leveraging much of our prior knowledge to the new mission of going to Mars. Exploring the solar system at greater distances from Earth than ever before will possess some unique challenges, which can be overcome thanks to the advances in modeling and simulation technologies. The National Aeronautics and Space Administration (NASA) is at the forefront of exploring our solar system. NASA's Human Research Program (HRP) focuses on discovering the best methods and technologies that support safe and productive human space travel in the extreme and harsh space environment. HRP uses various methods and approaches to answer questions about the impact of long duration missions on the human in space including: gravitys impact on the human body, isolation and confinement on the human, hostile environments impact on the human, space radiation, and how the distance is likely to impact the human. Predictive models are included in the HRP research portfolio as these models provide valuable insights into human-system operations. This paper will provide an overview of NASA's HRP and will present a number of projects that have used modeling and simulation to provide insights into human-system issues (e.g. automation, habitat design, schedules) in anticipation of space exploration.

human systems integrations↗
NASA NTRS2018

Guidelines for Verification Strategies to Minimize RISK Based on Mission Environment, -Application and -Lifetime (MEAL)

There is a trend of compromising verification testing to address the cost and schedule constraints, which poses a high-risk posture for programs/projects. Current and emerging aerospace scientific and/or human exploration programs continue to pose new technological challenges. These technological challenges combined with finite budgets and truncated schedules are forcing designers, scientists, engineers, and managers to push technologies to their physical limits. In addition, budget and schedule pressures challenge how those technologies/missions are verified. A clear understanding of the different verification processes is needed to ensure the proper verification of the technology within the mission (i.e., capabilities, advantages, and limitations). The goal of verification is to prove through test, analysis, inspection, and/or demonstration that a product provides its required function while meeting the performance requirements. It is important that verification yield understanding of representative performance under worst-case conditions so that margins to failure can be evaluated for proposed applications. The capabilities, advantages, and limitations of the testing and inspection performed at each level are different, and the risk incurred by omitting a verification step depends on the level of integration as well as Mission, Environment, Application and Lifetime (MEAL). This paper focuses on verification processes. The goal of the verification process is to ensure the given avionics technology could be safely implemented on the given MEAL consistent with the program/project risk posture.

Gonzalez, Oscar↗
NASA NTRS2018

Global Precipitation Measurement (GPM) Core Satellite & International Space Station (ISS) Coordination for CubeSat Deployments to Minimize Collision Risk

The Global Precipitation Measurement Mission (GPM) is a joint U.S. and Japan mission to observe global precipitation, extending the Tropical Rainfall Measuring Mission (TRMM) data set, which was launched by H-IIA from Tanegashima in Japan on February 28TH, 2014 directly into its 407km operational orbit. The International Space Station (ISS) is an international human research facility operated jointly by Russia and the USA from NASA's Johnson Space Center (JSC) in Houston Texas. Mission priorities lowered the operating altitude of ISS from 415km to 400km in early 2015, effectively placing both vehicles into the same orbital regime. The ISS has begun a program of deployments of cost effective CubeSats from the ISS that allow testing and validation of new technologies. With a major NASA asset flying at the same effective altitude as the ISS, CubeSat deployments became a serious threat to GPM and therefore a significant indirect threat to the ISS. This presentation describes the specific problem of collision threat to GPM and risk to ISS CubeSat deployment, the process that was implemented to keep both missions safe from collision and maximize their project goals, and the history of the process since implementation.

GPM↗
NASA NTRS2019

Minimally Invasive Expeditionary Surgical Care Using Human-Inspired Robots

This technical report serves as an updated collection of subject matter experts on surgical care using human-inspired robotics for human exploration. It is a summary of the ‘Blue Sky Meeting’, organized by the Florida Institute for Human and Machine Cognition (IHMC), Pensacola, Florida, and held on October 2-3, 2018. It contains an executive summary, the final report, all of the presentation materials, and an updated reference list.

Pantalos, George↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗
NASA NTRS2019

Minimizing Sonic Boom Through Simulation-Based Design: The X-59 Airplane

One of NASA’s six Strategic Thrusts for aeronautics is “Innovation in Commercial Supersonic Aircraft,” with a vision of fast air travel widely available to the traveling public. Future supersonic aircraft will be efficient, affordable, and environmentally responsible, generating an acceptable level of en-route noise (sonic booms). The first major step is the ongoing construction of the new X-59 Quiet SuperSonic Technology X-plane to demonstrate technologies that reduce sonic booms to gentle thumps. By using highresolution Cart3D computational fluid dynamics simulations, the shape of the aircraft can be designed to control the non-linear interactions of shock waves to reduce the sonic boom noise on the ground to within outdoor ambient levels, thereby enabling supersonic overland flight.

Nemec, Marian↗
NASA NTRS2003

Optimizing coronagraph designs to minimize their contrast sensitivity to low-order optical aberrations

The presence of optical aberrations in the entrance pupil of a coronagraph causes the stellar light to scatter about the occulting spot, reducing the effective contrast achievable. Even if these aberrations are sufficiently corrected with a deformable mirror to enable planet detection, small drifts in the optical alignment of the telescope introduce additional low-order aberrations.

Shaklan, Stuart B.↗