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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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NASA NTRS2014

NASA Rocket Propulsion Test Replacement Effort for Oxygen System Cleaner - Hydrochlorofluorocarbon (HCFC) 225

Gaseous and liquid oxygen are extremely reactive materials used in bipropellant propulsion systems. Both flight and ground oxygen systems require a high level of cleanliness to support engine performance, testing, and prevent mishaps. Solvents used to clean and verify the cleanliness of oxygen systems and supporting test hardware must be compatible with the system's materials of construction and effective at removing or reducing expected contaminants to an acceptable level. This paper will define the philosophy and test approach used for evaluating replacement solvents for the current Marshall Space Flight Center/Stennis Space Center baseline HCFC‐225 material that will no longer be available for purchase after 2014. MSFC/SSC applications in cleaning / sampling oxygen propulsion components, support equipment, and test system were reviewed then candidate replacement cleaners and test methods selected. All of these factors as well as testing results will be discussed.

DeWitt Burns, H.↗
NASA NTRS2014

Hazard Analysis for Building 34 Vacuum Glove Box Assembly

One of the characteristics of an effective safety program is the recognition and control of hazards before mishaps or failures occur. Conducting potentially hazardous tests necessitates a thorough hazard analysis in order to prevent injury to personnel, and to prevent damage to facilities and equipment. The primary purpose of this hazard analysis is to define and address the potential hazards and controls associated with the Building 34 Vacuum Glove Box Assembly, and to provide the applicable team of personnel with the documented results. It is imperative that each member of the team be familiar with the hazards and controls associated with his/her particular tasks, assignments and activities while interfacing with facility test systems, equipment and hardware. In fulfillment of the stated purposes, the goal of this hazard analysis is to identify all hazards that have the potential to harm personnel, damage the facility or its test systems or equipment, test articles, Government or personal property, or the environment. This analysis may also assess the significance and risk, when applicable, of lost test objectives when substantial monetary value is involved. The hazards, causes, controls, verifications, and risk assessment codes have been documented on the hazard analysis work sheets in Appendix A of this document. The preparation and development of this report is in accordance with JPR 1700.1, "JSC Safety and Health Handbook" and JSC 17773 Rev D "Instructions for Preparation of Hazard Analysis for JSC Ground Operations".

Meginnis, Ian↗
NASA NTRS2014

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Luxhoj, J. T.↗
NASA NTRS2014

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP.

Ancel, Ersin↗
NASA NTRS2015

Small UAV Automatic Ground Collision Avoidance System Design Considerations and Flight Test Results

The National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center Small Unmanned Aerial Vehicle (SUAV) Automatic Ground Collision Avoidance System (Auto GCAS) project demonstrated several important collision avoidance technologies. First, the SUAV Auto GCAS design included capabilities to take advantage of terrain avoidance maneuvers flying turns to either side as well as straight over terrain. Second, the design also included innovative digital elevation model (DEM) scanning methods. The combination of multi-trajectory options and new scanning methods demonstrated the ability to reduce the nuisance potential of the SUAV while maintaining robust terrain avoidance. Third, the Auto GCAS algorithms were hosted on the processor inside a smartphone, providing a lightweight hardware configuration for use in either the ground control station or on board the test aircraft. Finally, compression of DEM data for the entire Earth and successful hosting of that data on the smartphone was demonstrated. The SUAV Auto GCAS project demonstrated that together these methods and technologies have the potential to dramatically reduce the number of controlled flight into terrain mishaps across a wide range of aviation platforms with similar capabilities including UAVs, general aviation aircraft, helicopters, and model aircraft.

Sorokowski, Paul↗
NASA NTRS2015

Probabilistic Survivability Versus Time Modeling

This technical paper documents Kennedy Space Centers Independent Assessment team work completed on three assessments for the Ground Systems Development and Operations (GSDO) Program to assist the Chief Safety and Mission Assurance Officer (CSO) and GSDO management during key programmatic reviews. The assessments provided the GSDO Program with an analysis of how egress time affects the likelihood of astronaut and worker survival during an emergency. For each assessment, the team developed probability distributions for hazard scenarios to address statistical uncertainty, resulting in survivability plots over time. The first assessment developed a mathematical model of probabilistic survivability versus time to reach a safe location using an ideal Emergency Egress System at Launch Complex 39B (LC-39B); the second used the first model to evaluate and compare various egress systems under consideration at LC-39B. The third used a modified LC-39B model to determine if a specific hazard decreased survivability more rapidly than other events during flight hardware processing in Kennedys Vehicle Assembly Building (VAB).Based on the composite survivability versus time graphs from the first two assessments, there was a soft knee in the Figure of Merit graphs at eight minutes (ten minutes after egress ordered). Thus, the graphs illustrated to the decision makers that the final emergency egress design selected should have the capability of transporting the flight crew from the top of LC 39B to a safe location in eight minutes or less. Results for the third assessment were dominated by hazards that were classified as instantaneous in nature (e.g. stacking mishaps) and therefore had no effect on survivability vs time to egress the VAB. VAB emergency scenarios that degraded over time (e.g. fire) produced survivability vs time graphs that were line with aerospace industry norms.

Joyner, James J., Sr.↗
NASA NTRS2015

Human Systems Integration (HSI) Practitioner's Guide

The NASA/SP-2015-3709, Human Systems Integration (HSI) Practitioner's Guide, also known as the "HSIPG," provides a tool for implementing HSI activities within the NASA systems engineering framework. The HSIPG is written to aid the HSI practitioner engaged in a program or project (P/P), and serves as a knowledge base to allow the practitioner to step into an HSI lead or team member role for NASA missions. Additionally, this HSIPG is written to address the role of HSI in the P/P management and systems engineering communities and aid their understanding of the value added by incorporating good HSI practices into their programs and projects. Through helping to build a community of knowledgeable HSI practitioners, this document also hopes to build advocacy across the Agency for establishing strong, consistent HSI policies and practices. Human Systems Integration (HSI) has been successfully adopted (and adapted) by several federal agencies-most notably the U.S. Department of Defense (DoD) and the Nuclear Regulatory Commission (NRC)-as a methodology for reducing system life cycle costs (LCCs). These cost savings manifest themselves due to reductions in required numbers of personnel, the practice of human-centered design, decreased reliance on specialized skills for operations, shortened training time, efficient logistics and maintenance, and fewer safety-related risks and mishaps due to unintended human/system interactions. The HSI process for NASA establishes how cost savings and mission success can be realized through systems engineering. Every program or project has unique attributes. This HSIPG is not intended to provide one-size-fits-all recommendations for HSI implementation. Rather, HSI processes should be tailored to the size, scope, and goals of individual situations. The instructions and processes identified here are best used as a starting point for implementing human-centered system concepts and designs across programs and projects of varying types, including manned and unmanned, human spaceflight, aviation, robotics, and environmental science missions. The practitioner using this guide should have expertise in Systems Engineering or other disciplines involved in producing systems with anticipated human interactions. (See section 1.6 of this guide for further discussion on HSI discipline domains.) The HSIPG provides an "HSI layer" to the NASA Systems Engineering Engine (SEE), detailed in NASA Procedural Requirement (NPR) 7123.1B, NASA Systems Engineering Processes and Requirements, and further explained in NASA/SP-2007-6105, Systems Engineering Handbook (see HSIPG Table 2.2-1, NASA Documents with HSI Content, for specific references and document versions).

Zumbado, Jennifer Rochlis↗
NASA NTRS2015

Safety Awareness & Communications Internship

The projects that I have worked on during my internships were updating the JSC Safety & Health Action Team JSAT Employee Guidebook, conducting a JSC mishap case study, preparing for JSC Today Close Call success stories, and assisting with event planning and awareness.

Jefferson, Zanani↗
NASA NTRS2011

Human Factors Throughout the Life Cycle: Lessons Learned from the Shuttle Program

With the ending of the Space Shuttle Program, it is critical that we not forget the Human Factors lessons we have learned over the years. At every phase of the life cycle, from manufacturing, processing and integrating vehicle and payload, to launch, flight operations, mission control and landing, hundreds of teams have worked together to achieve mission success in one of the most complex, high-risk socio-technical enterprises ever designed. Just as there was great diversity in the types of operations performed at every stage, there was a myriad of human factors that could further complicate these human systems. A single mishap or close call could point to issues at the individual level (perceptual or workload limitations, training, fatigue, human error susceptibilities), the task level (design of tools, procedures and aspects of the workplace), as well as the organizational level (appropriate resources, safety policies, information access and communication channels). While we have often had to learn through human mistakes and technological failures, we have also begun to understand how to design human systems in which individuals can excel, where tasks and procedures are not only safe but efficient, and how organizations can foster a proactive approach to managing risk and supporting human enterprises. Panelists will talk about their experiences as they relate human factors to a particular phase of the shuttle life cycle. They will conclude with a framework for tying together human factors lessons-learned into system-level risk management strategies.

human factors↗
NASA NTRS2016

You Can't Reach for the Stars if You are Tripping Over the Ground! (Preventing Slips, Trips, and Falls)

Although there are very few mishaps related to ground, vehicle or payload processing at the Kennedy Space Center (KSC), employees have experienced a significant number of injuries due to slips, trips, and falls outside of performing flight processing operations. Slips, trips, and falls are major causes of occupational injuries at KSC, the National Aeronautics and Space Administration (NASA), and in general industry. To help KSC employees avoid these injuries, and allow them to be fully productive, KSC launched an initiative in 2013 to reduce slips, trips, and falls. This initiative is based on a four-part model focusing on DATA analysis, HAZARD awareness, PREVENTIVE methods, and BALANCE.

Falls↗
NASA NTRS2017

Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAPSentinel-1 Active-Passive Soil Moisture Product in April 2017.This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correctioncalibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m(exp 3)/m(exp 3) unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

Enhanced↗
NASA NTRS2016

Real-Time Early Warning Techniques

Contemporary approaches to aerospace vehicle system monitoring rely heavily on thresholds that represent a compromise between providing warning early enough to avert a mishap while simultaneously minimizing false alarms. While this reliance on thresholds has been in place for decades and has permeated both cockpits and control rooms, we often find it insufficient when retrospectively analyzing data from an accident. To modernize and enhance flight test safety, we introduce new methods of monitoring for anomalous patterns of interaction rather than for thresholds exceedance. For systems with a well characterized baseline we show how the Inductive Monitoring System (IMS), utilized by NASA in the aftermath of the Columbia accident, might be implemented in real-time to provide earlier warning than currently employed techniques. For systems without such a baseline, we introduce new developments in statistical methods relating to critical slowing down, first applied in medicine and physics, which show promise for adaptation to flight test. Finally, the familiar resource constrained environment leads to a reliance on increased instrumentation that is challenging the limits of the current one-sensor, one-indicator threshold paradigm. Existing methods thus fail to accurately reflect the true complexity of a vehicle rich with interdependent interacting systems. We highlight these concepts in a brief summary of the 2001 Air Transat Flight 236 deadstick landing in the Azores. We then suggest control room and cockpit modifications to better display the information gleaned using these novel monitoring methods.

Javorsek, Dan↗
NASA NTRS2016

Enhancing Flight Test Safety with Real-Time Early Warning Techniques

Contemporary approaches to aerospace vehicle system monitoring rely heavily on thresholds that represent a compromise between providing warning early enough to avert a mishap while simultaneously minimizing false alarms. While this reliance on thresholds has been in place for decades and has permeated both cockpits and control rooms, we often find it insufficient when retrospectively analyzing data from an accident. To modernize and enhance flight test safety, we introduce new methods of monitoring for anomalous patterns of interaction rather than for thresholds exceedance. For systems with a well characterized baseline we show how the Inductive Monitoring System (IMS), utilized by NASA in the aftermath of the Columbia accident, might be implemented in real-time to provide earlier warning than currently employed techniques. For systems without such a baseline, we introduce new developments in statistical methods relating to critical slowing down, first applied in medicine and physics, which show promise for adaptation to flight test. Finally, the familiar resource constrained environment leads to a reliance on increased instrumentation that is challenging the limits of the current "one-sensor, one-indicator" threshold paradigm. Existing methods thus fail to accurately reflect the true complexity of a vehicle rich with interdependent interacting systems. We highlight these concepts in a brief summary of the 2001 Air Transat Flight 236 deadstick landing in the Azores. We then suggest control room and cockpit modifications to better display the information gleaned using these novel monitoring methods.

Javorsek, Daniel, II↗
NASA NTRS2019

NASA's Journey to Safety Culture

One of the organizational causes of the Shuttle Columbia mishap found by the Columbia Accident Investigation Board (CAIB) Report was a "Broken Safety Culture". Since its release, NASA has strived to improve its Safety Culture In 2009, the Chief of the Office of Safety and Mission Assurance asked a member of the Air Force Safety Center and of the CAIB for assistance to develop a NASA Safety Culture Program. This presentation describes the journey that NASA personnel has taken developing a NASA Safety Culture Program to "repair" its Safety Culture.

Dillinger, Tracy↗
NASA NTRS2019

Integration and Test (I&T) Horror Stories and the Lessons Learned

I've been asked to be a presenter along with a panel of fellow NASA Electromagnetic Compatibility (EMC) engineers at this year's IEEE EMC & Signal and Power Integrity (SIPI) 2019 symposium. The topic I am asked to cover is in regards to I&T horror stories because of my 15 years of personal experience as an EMC test engineer with Code 549. I am planning on only having slides with photos of test methods we no longer use as well as cover some mishaps that have happened over the years both within my organization and throughout the space industry. Highlighting the mistakes that were made, the lessons learned from those incidents and how they've effected how we test today.

EMI↗
NASA NTRS2020

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.

Colombano, Silvano↗
NASA NTRSDate not supplied

TPS and Entry Systems Technologies for Future Mars and Titan Exploration

During the past twenty years, NASA effectively erased earlier Mars mishaps with six successful missions to the Red Planet. These missions delivered one orbiter and five payloads to the surface. Those payloads included three rovers, Spirit (2004) which roamed 11 years, Opportunity (2004) which roamed nearly 15 years, and Curiosity (2012) which is in its ninth year, along with two landers, Phoenix (2008) and InSIGHT (2018). In July, the Mars 2020 mission will send another large rover, Perseverance, which will land in 2021. The InSIGHT mission even demonstrated the capability to send CubeSats along to help with communication back to the surface. NASA has demonstrated the capability to land a metric ton of vehicles plus science instruments on Mars and expects that the same technologies will be equally successful landing Dragonfly on Titan in the 2030’s. The thermal protection systems (TPS) used on the Mars missions are sufficiently developed and matured to continue furthering science on both Mars and Titan, assuming that the TPS materials are sustained by industry. The purpose of this white paper is to encourage further exploration and science on both Mars and Titan because we have the technologies to support them. In addition, we will look forward to human exploration of Mars and identify the improvements in TPS materials required to facilitate landing the larger payloads.

Thermal Protection Systems↗
NASA NTRSDate not supplied

Machine Learning Enabled Quantitative Risk Assessment of Aerial Wildfire Response

Aerial wildfire operations are high risk and account for a large number of firefighter deaths. Increasing intensity of wildfires is driving a surge in aerial operations, while simultaneously there is growing interest in improving system safety and performance. In this work, wildfire aviation mishaps documented using the SAFECOM system are analyzed using a previously developed framework for hazard extraction and analysis of trends (HEAT). Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard are calculated, including frequency, rate, and severity. We examine whether these metrics change over time, and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on rate of occurrence and average severity. Results identify jumper operations hazards as high-risk, in addition to bucket drop failures, cargo let down failures, and severe weather as medium risk.

machine learning↗