Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Risk Informed Decision Making”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

A Corrosion Risk Assessment Model for Underground Piping

The Pressure Systems Manager at NASA Ames Research Center (ARC) has embarked on a project to collect data and develop risk assessment models to support risk-informed decision making regarding future inspections of underground pipes at ARC. This paper shows progress in one area of this project - a corrosion risk assessment model for the underground high-pressure air distribution piping system at ARC. It consists of a Corrosion Model of pipe-segments, a Pipe Wrap Protection Model; and a Pipe Stress Model for a pipe segment. A Monte Carlo simulation of the combined models provides a distribution of the failure probabilities. Sensitivity study results show that the model uncertainty, or lack of knowledge, is the dominant contributor to the calculated unreliability of the underground piping system. As a result, the Pressure Systems Manager may consider investing resources specifically focused on reducing these uncertainties. Future work includes completing the data collection effort for the existing ground based pressure systems and applying the risk models to risk-based inspection strategies of the underground pipes at ARC.

Datta, Koushik↗
NASA NTRS2020

NASA Risk Management: Speaking the Format of Risk

NASA's Risk Management Program is designed to communicate risk at all levels of the Agency. This presentation serves as an introduction to NASA Risk Management principles and risk-informed decision making. The contents of this presentation are intended to familiarize attendees of NASA's Quality Leadership Forum with how to incorporate risk communication into their practices.

Dacko, Taylor↗
NASA NTRSDate not supplied

3D Representation of UAV-obstacle Collision Risk Under Off-nominal Conditions

Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The risk is represented in terms of a matrix with rows corresponding to the likelihood of occurrence of collision and columns representing severity of collision to the vehicle and surrounding structures. Risk likelihood is generated using a Bayesian Belief Network (BBN) that compiles knowledge from related Failure Modes and Effects Analysis (FMEAs) and Subject Matter Experts (SMEs) to determine the probability of collision based on on-board sensor measurements indicative of vehicle health and controllability. Risk severity is computed utilizing a point-mass 3D kinematic model of the vehicle in presence of wind. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.

risk analysis↗
NASA NTRS2023

Earth Observations into Action: Systemic Integration of Earth Observation Applications into National Risk Reduction Decision Structures

As stated in the United Nations Global Assessment Report 2022 Concept Note, decision makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible, and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of Earth observation, specifically ongoing efforts within the Group on Earth Observations Work Programme and the Committee on Earth Observation Satellites Work Plan, to support risk-informed decision making, based on documented national and subnational needs and requirements.

earth observations↗
NASA NTRS2011

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment.

Stamatelatos,Michael↗
NASA NTRS2015

Statistical Engineering in Air Traffic Management Research

NASA is working to develop an integrated set of advanced technologies to enable efficient arrival operations in high-density terminal airspace for the Next Generation Air Transportation System. This integrated arrival solution is being validated and verified in laboratories and transitioned to a field prototype for an operational demonstration at a major U.S. airport. Within NASA, this is a collaborative effort between Ames and Langley Research Centers involving a multi-year iterative experimentation process. Designing and analyzing a series of sequential batch computer simulations and human-in-the-loop experiments across multiple facilities and simulation environments involves a number of statistical challenges. Experiments conducted in separate laboratories typically have different limitations and constraints, and can take different approaches with respect to the fundamental principles of statistical design of experiments. This often makes it difficult to compare results from multiple experiments and incorporate findings into the next experiment in the series. A statistical engineering approach is being employed within this project to support risk-informed decision making and maximize the knowledge gained within the available resources. This presentation describes a statistical engineering case study from NASA, highlights statistical challenges, and discusses areas where existing statistical methodology is adapted and extended.

Wilson, Sara R.↗
NASA NTRSDate not supplied

Risk Assessment of Obstacle Collision for UAVs under off-nominal conditions

Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace, demands the need of robust risk monitoring framework for assessing the safety of airspace, ground-structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV's predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV's trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.

Portia Banerjee↗
NASA NTRS2023

Exploration Medical Capability Science and Research Overview and Update

The mission of the Exploration Medical Capability (ExMC) Element is to advance medical system design and risk-informed decision making for exploration beyond low Earth orbit to promote human health and performance in space. In order to accomplish this mission, the Element takes a progressively Earth-independent approach to three main areas: - Answering key clinical and science research questions that will help to address the challenges of providing medical care in the extreme environment of space - Applying systems engineering processes to medical system design with the goal of developing robust requirements that can be fully integrated into future space exploration vehicle designs - Developing and demonstrating novel medical technologies that will improve future medical capabilities in space This presentation will focus on selected scientific and technical conceptual drivers for the Element, the current and future research risks and gaps, and provide an overview of the Element’s progress in 2022. In particular, it will address the ongoing effort for the IMPACT trade space analysis tool suite, the closeout work on a clinical decision support prototype, potential requirements for a long-duration lunar orbit and lunar surface medical system, and an International Space Station technology demonstration of a point-of-care laboratory analysis capability.

Kris Lehnhardt↗
NASA NTRSDate not supplied

Exploration Medical Capability Science and Research Overview and Update

The mission of the Exploration Medical Capability (ExMC) Element is to advance medical system design and risk-informed decision making for exploration beyond low Earth orbit to promote human health and performance in space. In order to accomplish this mission, the Element takes a progressively Earth-independent approach to three main areas: 1)answering key clinical and science research questions that will help to address the challenges of providing medical care in the extreme environment of space, 2)applying systems engineering processes to medical system design with the goal of developing robust requirements that can be fully integrated into future space exploration vehicle designs, and 3)developing and demonstrating novel medical technologies that will improve future medical capabilities in space. This presentation will focus on selected scientific and technical conceptual drivers for the Element, the current and future research risks and gaps, and provide an overview of the Element’s progress in 2020.

Kris Lehnhardt↗
NASA NTRS2010

Overview of EVA PRA for TPS Repair for Hubble Space Telescope Servicing Mission

Following the Columbia accident in 2003, NASA developed techniques to repair the Thermal Protection System (TPS) in the event of damage to the TPS as one of several actions to reduce the risk to future flights from ascent debris, micro-meteoroid and/or orbital debris (MMOD). Other actions to help reduce the risk include improved inspection techniques, reduced shedding of debris from the External Tank and ability to rescue the crew with a launch on need vehicle. For the Hubble Space Telescope (HST) Servicing Mission the crew rescue capability was limited by the inability to safe haven on the International Space Station (ISS), resulting in a greater reliance on the repair capability. Therefore it was desirable to have an idea of the risk associated with conducting a repair, where the repair would have to be conducted using an Extra-Vehicular Activity (EVA). Previously, focused analyses had been conducted to quantify the risk associated with certain aspects of an EVA, for example the EVA Mobility Unit (EMU) or Space Suit; however, the analyses were somewhat limited in scope. A complete integrated model of an EVA which could quantify the risk associated with all of the major components of an EVA had never been done before. It was desired to have a complete integrated model to be able to assess the risks associated with an EVA to support the Space Shuttle Program (SSP) in making risk informed decisions. In the case of the HST Servicing Mission, this model was developed to assess specifically the risks associated with performing a TPS repair EVA. This paper provides an overview of the model that was developed to support the HST mission in the event of TPS damage. The HST Servicing Mission was successfully completed on May 24th 2009 with no critical TPS damage; therefore the model was not required for real-time mission support. However, it laid the foundation upon which future EVA quantitative risk assessments could be based.

Bigler, Mark↗
NASA NTRS2012

Fault Management in an Objectives-Based/Risk-Informed View of Safety and Mission Success

Theme of this talk: (1) Net-benefit of activities and decisions derives from objectives (and their priority) -- similarly: need for integration, value of technology/capability. (2) Risk is a lack of confidence that objectives will be met. (2a) Risk-informed decision making requires objectives. (3) Consideration of objectives is central to recent guidance.

Groen, Frank↗
NASA NTRS2015

Stratospheric Aerosol and Gas Experiment, SAGE III on ISS, An Earth Science Mission on the International Space Station, Schedule Risk Analysis, A Project Perspective

The presentation provides insight into the schedule risk analysis process used by the Stratospheric Aerosol and Gas Experiment III on the International Space Station Project. The presentation focuses on the schedule risk analysis process highlighting the methods for identification of risk inputs, the inclusion of generic risks identified outside the traditional continuous risk management process, and the development of tailored analysis products used to improve risk informed decision making.

Bonine, Lauren↗
NASA NTRSDate not supplied

3D Representation of UAV-obstacle Collision Risk under off-nominal conditions

Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk likelihood of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The deviation in the planned trajectory caused due to wind is computed utilizing a point-mass 3D kinematic simulation model of the vehicle. Likelihood of risk for the flight plan is then analyzed based on generating the probability of collision for each point in the trajectory. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, distance from obstacles, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.

Portia Banerjee↗
NASA NTRS2019

A Risk Informed Approach to Reliability Requirements Tailoring

Every institution has their “recipe” for success. Cell phones and personal computers seem like they are designed to be obsolete in 2-3 years. Automobiles seem like they are designed to have a power train failure within 1000 miles of the extended warranty expiration; at least that’s been my experience. The Jet Propulsion Laboratory (JPL) is not any different. JPL has a tried and proven recipe for success because in space things can’t fail. Or else. However, in today’s competitive environment and funding limitations, that recipe for success is being challenged and the resultant increased risk accepted. This paper will describe JPL’s Risk Informed Decision Making (RIDM) approach to tailoring reliability requirements based on mission classification and other project characteristics.

Klohoker, John↗
NASA NTRSDate not supplied

A Practical Approach to Uncertainty Quantification Using Probability Boxes

To date, while the use of CFD for aerospace vehicle design and development is prevalent, the documentation of uncertainties associated with the simulations are rare. Instead, the current state-of-the-art relies heavily on the experience of the CFD practitioner to estimate the uncertainty associated with their simulations through simple sensitivity studies or subject matter expertise. This practice will have to be replaced with a formal uncertainty quantification (UQ) process if CFD is to play an expanded role in the research and engineering design community, test and evaluation community, and ultimately certification for flight. Accounting for uncertainties in a formal manner is a tedious process. Moreover, the typical CFD practitioner is not likely to be familiar with formal UQ methods. These factors have prevented the adoption of UQ methods in the engineering design and development cycle. This presentation will outline a credible approach to UQ using Probability Boxes that is straightforward to apply, and can readily be automated using existing UQ tool sets such as the DAKOTA packaged developed at Sandia. The added expense incurred when moving away from a deterministic CFD process to a stochastic one that captures uncertainties to enable risk-informed decision making will be discussed, as well as effective ways to reduce the computational costs.

Uncertainty Quantification↗
NASA NTRSDate not supplied

NASA’s Planetary Protection Program to Assure Mission Safety and Success

NASA’s planetary protection program seeks to understand and control harmful contamination of solar systems targets of exploration by terrestrial contamination and prevent harmful biological contamination of the Earth-Moon system by extraterrestrial life, should it exist. To accomplish these objective’s NASA has developed a balanced safety and mission assurance strategy that leverages COSPAR Policy guidelines, workshops, scientific consensus, partnerships and international working groups to develop policy and implementation guidelines. Upcoming crewed missions to the Moon and Mars, as well as robotic missions to small solar system bodies, Europa, Titan and Mars are some of the driving activities of astrobiological interest that continue to emphasize the importance of planetary protection throughout the project life cycle. Development of a responsive and updated agency planetary protection policy has been a focus area in supporting upcoming mission opportunities for exploration to include Mars sample return and crewed mission concepts. An extensive update of this policy is underway which encompasses crewed and robotic procedural polices, a general technical requirements standard, and an implementation handbook. During this timeframe NASA has been working with the international community to develop scientific consensus, and to identify and fill in knowledge gaps for developing balanced policy guidelines, incorporation of risk informed decision making and quantitative technical standards. NASA has developed a planetary protection roadmap as a technology management strategy to track and monitor the development of each knowledge gaps. The Committee of Space Research (COSPAR) Policy on Planetary Protection and the National Academies of Science, Engineering, and Medicine’s Committee of Planetary Protection are used to inform updates to the planetary protection polices and guidelines. This integrated strategy for planetary protection seeks to provide a transparent, structured approach for enabling missions, providing guidance for NASA and NASA partnered missions, and being responsive to the increased interest and activities in space exploration whilst maintaining an understanding and control of harmful contamination.

James Benardini↗
NASA NTRS2022

NASA’s Revised Planetary Protection Policy and Implementation

NASA has updated its planetary protection policy and implementation approach in response to advances in scientific understanding of solar system targets, upcoming mission opportuni-ties for exploration and sample return, and the private sector’s emerging capability to plan missions to Earth’s Moon and Mars. In September 2021, the NASA Procedural Requirements NPR 8715.24, entitled “Planetary Protection Provisions for Robotic Extraterrestrial Missions” was released which repositions planetary protection in existing NASA mission and program management structures, introduces risk-informed decision making, expands on key roles and responsibilities for both programmatic and the Oÿce of Safety and Mission Assurance, updates the categorization process and streamlines the planetary protection documentation approval and schedule. NASA is working on a more detailed technical standard to accompany NPR 8715.24 which will include the detailed technical requirements to address organic contamina-tion, inadvertent impact avoidance, biological control and management, end of mission disposal and restricted Earth-Return sample safety and assurance. This report to the COSPAR com-munity will describe the current NASA planetary protection policy and its alignment with the revised COSPAR planetary protection policy. NASA’s plans for updating the supporting “Handbook for Implementing Planetary Protection Technical Requirements” and future policies for addressing planetary protection of crewed missions will also be presented.

Elaine Seasly↗
NASA NTRSDate not supplied

Probability of Obstacle Collision for UAVs in Presence of Wind

For incorporation of unmanned aerial vehicles into the National Airspace, ensuring safety of the airspace including the vehicles, people, and property on the ground is of utmost importance. One of the safety-critical factors for unmanned aviation flights is the risk of deviating from a planned trajectory resulting in a variety of hazards, including potential loss of separation between vehicle and obstacles or unexpected battery energy consumption. Off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts can lead to unacceptable unexpected deviations from the flight trajectory. It is essential to accurately model such effects on the flight trajectory while computing safety thresholds such as minimum separation from surrounding obstacles, available battery resource to complete the mission or determining delay in the expected time of arrival of flights. In this paper, a tool is presented based on Gaussian Process Regression for wind representation over a pre-defined trajectory for fast, yet approximated, in-time evaluation of possible trajectory deviations caused by wind gusts. The deviation in the planned trajectory caused by wind is further simulated utilizing a 6 degrees-of-freedom (DOF) UAV trajectory simulator comprising of a rotorcraft lumped-mass model with LQRI controller. Both steady-state wind and wind gust effects are investigated. The probability of collision with obstacle is computed and demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in the presence of simulated obstacles and wind conditions. Effect of varying wind conditions and varying UAV airspeed is further demonstrated on experimental flights in the presence of wind measured by ground based weather service stations. The proposed approach would eventually benefit timely mitigation of current and future safety-critical events in autonomous systems by enabling risk-informed decision making.

Portia Banerjee↗