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NASA NTRS1996

Implementation of Probabilistic Design Methodology at Tennessee State University

Engineering Design is one of the most important areas in engineering education. Deterministic Design Methodology (DDM) is the only design method that is taught in most engineering schools. This method does not give a direct account of uncertainties in design parameters. Hence, it is impossible to quantify the uncertainties in the response and the actual safety margin remains unknown. The desire for a design methodology tha can identify the primitive (random) variables that affect the structural behavior has led to a growing interest on Probabilistic Design Methodology (PDM). This method is gaining more recognition in industries than in educational institutions. Some of the reasons for the limited use of the PDM at the moment are that many are unaware of its potentials, and most of the software developed for PDM are very recent. The central goal of the PDM project at Tennessee State University is to introduce engineering students to the method. The students participating in the project learn about PDM and the computer codes that are available to the design engineer. The software being used of this project is NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) developed under NASA probabilistic structural analysis program. NESSUS has three different modules which make it a very comprehensive computer code for PDM. A research in technology transfer through course offering in PDM is in effect a Tennessee State University. The aim is to familiarize students with the problem of uncertainties in engineering design. Included in the paper are some projects on PDM carried out by some students and faculty. The areas this method is being applied at the moment include, Design of Gears (spur and worm); Design of Shafts; Design of Statistically Indeterminate Frame Structures; Design of Helical Springs; and Design of Shock Absorbers. Some of the current results of these projects are presented.

Onwubiko, Chinyere↗

NASA NTRS1995

Implementation of probabilistic design methodology at Tennessee State University

The fact that Deterministic Design Method no longer satisfies most design needs calls for methods that will cope with the high trend in technology. The advance in computer technology has reduced the rigors that normally accompany many design analysis methods that account for uncertainties in design parameters. Probabilistic Design Methodology (PDM) is beginning to make impact in engineering design. This method is gaining more recognition in industries than in educational institutions. Some of the reasons for the limited use of the PDM at the moment are that many are unaware of its potentials, and most of the software developed for PDM are very recent. The central goal of the PDM project at Tennessee State University is to introduce engineering students to this method. The students participating in the project learn about PDM and the computer codes that are available to the design engineer. The software being used for this project is NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) developed under NASA probabilistic structural analysis program. NESSUS has three different modules which make it a very comprehensive computer code for PDM. Since this method is new to the students, its introduction into the engineering curriculum is to be in stages. These range from the introduction of PDM and its software to the applications. While this program is being developed for its eventual inclusion into the engineering curriculum, some graduate and undergraduate students are already carrying out some projects using this method. As the students are increasing their understanding on PDM, they are at the same time applying it to some common design problems. The areas this method is being applied at the moment include, Design of Gears (spur and worm); Design of Brakes; Design of Heat Exchangers Design of Helical Springs; and Design of Shock Absorbers. Some of the current results of these projects are presented.

Onwubiko, Chinyere↗

NASA NTRS1965

Apollo reliability analysis.

Apollo spacecraft reliability analysis techniques, both probabilistic and qualitative

APOLLO PROJECT↗

DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2005

Probabilistic Reasoning for Plan Robustness

A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations witnessed by execution. The improvement is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.

autonomous planning↗

NASA NTRS2000

A systematic risk management approach employed on the CloudSat project

The CloudSat Project has developed a simplified approach for fault tree analysis and probabilistic risk assessment. A system-level fault tree has been constructed to identify credible fault scenarios and failure modes leading up to a potential failure to meet the nominal mission success criteria.

risk management fault tree analysis probabilistic ↗

NASA NTRSDate not supplied

A Decision Support System for Extravehicular Operations Under Significant Communication Latency

Humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) and Earth-based mission control. Nextgeneration operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the onplanet extravehicular crewmember(s), and intermediate mission support from intravehicular (IV) crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. Thus, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automatically tracking and projecting consumables usage over an EVA timeline, providing real-time probabilistic safety assessments and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the IV crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g., training bias.

Mars↗

NASA NTRSDate not supplied

PersEIDS: A Biomedical Decision Support System for Extravehicular Operations Under Significant Communication Latency

Mars↗

NASA NTRS2013

Probabilistic Modeling of the Renal Stone Formation Module

The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously randomly sampling the probability distributions of the electrolyte concentrations and system parameters that are inputs into the deterministic model. The total urine chemistry concentrations are used to determine the urine chemistry activity using the Joint Expert Speciation System (JESS), a biochemistry model. Information used from JESS is then fed into the deterministic growth model. Outputs from JESS and the deterministic model are passed back to the probabilistic model where a multivariate regression is used to assess the likelihood of a stone forming and the likelihood of a stone requiring clinical intervention. The parameters used to determine to quantify these risks include: relative supersaturation (RS) of calcium oxalate, citrate/calcium ratio, crystal number density, total urine volume, pH, magnesium excretion, maximum stone width, and ureteral location. Methods and Validation: The RSFM is designed to perform a Monte Carlo simulation to generate probability distributions of clinically significant renal stones, as well as provide an associated uncertainty in the estimate. Initially, early versions will be used to test integration of the components and assess component validation and verification (V&V), with later versions used to address questions regarding design reference mission scenarios. Once integrated with the deterministic component, the credibility assessment of the integrated model will follow NASA STD 7009 requirements.

kidney stones↗

NASA NTRS2012

Safety Risk Knowledge Elicitation in Support of Aeronautical R and D Portfolio Management: A Case Study

Aviation is a problem domain characterized by a high level of system complexity and uncertainty. Safety risk analysis in such a domain is especially challenging given the multitude of operations and diverse stakeholders. The Federal Aviation Administration (FAA) projects that by 2025 air traffic will increase by more than 50 percent with 1.1 billion passengers a year and more than 85,000 flights every 24 hours contributing to further delays and congestion in the sky (Circelli, 2011). This increased system complexity necessitates the application of structured safety risk analysis methods to understand and eliminate where possible, reduce, and/or mitigate risk factors. The use of expert judgments for probabilistic safety analysis in such a complex domain is necessary especially when evaluating the projected impact of future technologies, capabilities, and procedures for which current operational data may be scarce. Management of an R&D product portfolio in such a dynamic domain needs a systematic process to elicit these expert judgments, process modeling results, perform sensitivity analyses, and efficiently communicate the modeling results to decision makers. In this paper a case study focusing on the application of an R&D portfolio of aeronautical products intended to mitigate aircraft Loss of Control (LOC) accidents is presented. In particular, the knowledge elicitation process with three subject matter experts who contributed to the safety risk model is emphasized. The application and refinement of a verbal-numerical scale for conditional probability elicitation in a Bayesian Belief Network (BBN) is discussed. The preliminary findings from this initial step of a three-part elicitation are important to project management practitioners as they illustrate the vital contribution of systematic knowledge elicitation in complex domains.

Shih, Ann T.↗

NASA NTRS1983

U.S. planetary protection program - Implementation highlights

The implementation of the United States planetary protection program, intended to prevent the contamination of the celestial bodies in the solar system during their exploration, in recent interplanetary projects is reviewed. The shift in planetary protection policy from that of absolute sterilization to a probabilistic approach is noted. Attention is then given to the use of microbiological assay, organic materials inventory, microbial burden reduction, contamination control, trajectory biasing and the analysis and identification of contaminating processes for purposed of planetary protection for the Pioneer 10 and 11, Viking and Voyager projects. The success of these measures, as demonstrated by the prevention of accidental planetary impact and the failure to detect terrestrial organisms by the Viking life detection experiments, is pointed out.

Barengoltz, J.↗

NASA NTRS1967

Probabilistic approach to long range planning of manpower

Publication presents a total long range planning model for project oriented organizations. The total model consists of planning systems which originate - /1/ at the project level and consolidate into an overall plan, and /2/ from a budetary ceiling and allocate to the individual projects. Analysis of /1/ and /2/ is provided for management decision making.

Lejk, R. A.↗

NASA NTRS2008

Probalistic Assessment of Radiation Risk for Solar Particle Events

For long duration missions outside of the protection of the Earth's magnetic field, exposure to solar particle events (SPEs) is a major safety concern for crew members during extra-vehicular activities (EVAs) on the lunar surface or Earth-to-moon or Earth-to-Mars transit. The large majority (~90%) of SPEs have small or no health consequences because the doses are low and the particles do not penetrate to organ depths. However, there is an operational challenge to respond to events of unknown size and duration. We have developed a probabilistic approach to SPE risk assessment in support of mission design and operational planning. Using the historical database of proton measurements during the past 5 solar cycles, the functional form of hazard function of SPE occurrence per cycle was found for nonhomogeneous Poisson model. A typical hazard function was defined as a function of time within a non-specific future solar cycle of 4000 days duration. Distributions of particle fluences for a specified mission period were simulated ranging from its 5th to 95th percentile. Organ doses from large SPEs were assessed using NASA's Baryon transport model, BRYNTRN. The SPE risk was analyzed with the organ dose distribution for the given particle fluences during a mission period. In addition to the total particle fluences of SPEs, the detailed energy spectra of protons, especially at high energy levels, were recognized as extremely important for assessing the cancer risk associated with energetic particles for large events. The probability of exceeding the NASA 30-day limit of blood forming organ (BFO) dose inside a typical spacecraft was calculated for various SPE sizes. This probabilistic approach to SPE protection will be combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks in future work.

Kim, Myung-Hee Y.↗

NASA NTRS2009

Probabilistic Assessment of Cancer Risk for Astronauts on Lunar Missions

During future lunar missions, exposure to solar particle events (SPEs) is a major safety concern for crew members during extra-vehicular activities (EVAs) on the lunar surface or Earth-to-moon transit. NASA s new lunar program anticipates that up to 15% of crew time may be on EVA, with minimal radiation shielding. For the operational challenge to respond to events of unknown size and duration, a probabilistic risk assessment approach is essential for mission planning and design. Using the historical database of proton measurements during the past 5 solar cycles, a typical hazard function for SPE occurrence was defined using a non-homogeneous Poisson model as a function of time within a non-specific future solar cycle of 4000 days duration. Distributions ranging from the 5th to 95th percentile of particle fluences for a specified mission period were simulated. Organ doses corresponding to particle fluences at the median and at the 95th percentile for a specified mission period were assessed using NASA s baryon transport model, BRYNTRN. The cancer fatality risk for astronauts as functions of age, gender, and solar cycle activity were then analyzed. The probability of exceeding the NASA 30- day limit of blood forming organ (BFO) dose inside a typical spacecraft was calculated. Future work will involve using this probabilistic risk assessment approach to SPE forecasting, combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks.

Kim, Myung-Hee Y.↗

NASA NTRS2010

Probabilistic Assessment of Cancer Risk from Solar Particle Events

For long duration missions outside of the protection of the Earth s magnetic field, space radiation presents significant health risks including cancer mortality. Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons (less than several hundred MeV); and galactic cosmic ray (GCR), which include high energy protons and heavy ions. While the frequency distribution of SPEs depends strongly upon the phase within the solar activity cycle, the individual SPE occurrences themselves are random in nature. We estimated the probability of SPE occurrence using a non-homogeneous Poisson model to fit the historical database of proton measurements. Distributions of particle fluences of SPEs for a specified mission period were simulated ranging from its 5 th to 95th percentile to assess the cancer risk distribution. Spectral variability of SPEs was also examined, because the detailed energy spectra of protons are important especially at high energy levels for assessing the cancer risk associated with energetic particles for large events. We estimated the overall cumulative probability of GCR environment for a specified mission period using a solar modulation model for the temporal characterization of the GCR environment represented by the deceleration potential (^). Probabilistic assessment of cancer fatal risk was calculated for various periods of lunar and Mars missions. This probabilistic approach to risk assessment from space radiation is in support of mission design and operational planning for future manned space exploration missions. In future work, this probabilistic approach to the space radiation will be combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks.

Kim, Myung-Hee Y.↗

NASA NTRS2017

Probabilistic Asteroid Impact Risk Assessment for the Hypothetical PDC17 Impact Exercise

Performing impact risk assessment for the 2017 Planetary Defense Conference (PDC17) hypothetical impact exercise, to take place at the PDC17 conference, May 15-20, 2017. Impact scenarios and trajectories are developed and provided by NASA's Near Earth Objects Office at JPL (Paul Chodas). These results represent purely hypothetical impact scenarios, and do not reflect any known asteroid threat. Risk assessment was performed using the Probabilistic Asteroid Impact Risk (PAIR) model developed by the Asteroid Threat Assessment Project (ATAP) at NASA Ames Research Center. This presentation includes sample results that may be presented or used in discussions during the various stages of the impact exercisecenter dot Some cases represent alternate scenario options that may not be used during the actual impact exercise at the PDC17 conference. Updates to these initial assessments and/or additional scenario assessments may be performed throughout the impact exercise as different scenario options unfold.

Asteroid Impact Risk↗

NASA NTRSDate not supplied

A Decision Support System for Extravehicular Operations Under Significant Communication Latency

Within the next few decades, humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant two-way communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) performing an EVA and an Earth-based mission control. Next-generation operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the on-planet extravehicular crewmember(s), and intermediate mission support from intravehicular crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. For this purpose, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automating the tracking and projection of consumables usage over an EVA timeline, providing real-time probabilistic safety assessments of an EVA timeline given consumables constraints, and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the intravehicular crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g. training bias.

Mars↗

NASA NTRS2005

Assessing the Relative Risk of Aerocapture Using Probabalistic Risk Assessment

A recent study performed for the Aerocapture Technology Area in the In-Space Propulsion Technology Projects Office at the Marshall Space Flight Center investigated the relative risk of various capture techniques for Mars missions. Aerocapture has been proposed as a possible capture technique for future Mars missions but has been perceived by many in the community as a higher risk option as compared to aerobraking and propulsive capture. By performing a probabilistic risk assessment on aerocapture, aerobraking and propulsive capture, a comparison was made to uncover the projected relative risks of these three maneuvers. For mission planners, this knowledge will allow them to decide if the mass savings provided by aerocapture warrant any incremental risk exposure. The study focuses on a Mars Sample Return mission currently under investigation at the Jet Propulsion Laboratory (JPL). In each case (propulsive, aerobraking and aerocapture), the Earth return vehicle is inserted into Martian orbit by one of the three techniques being investigated. A baseline spacecraft was established through initial sizing exercises performed by JPL's Team X. While Team X design results provided the baseline and common thread between the spacecraft, in each case the Team X results were supplemented by historical data as needed. Propulsion, thermal protection, guidance, navigation and control, software, solar arrays, navigation and targeting and atmospheric prediction were investigated. A qualitative assessment of human reliability was also included. Results show that different risk drivers contribute significantly to each capture technique. For aerocapture, the significant drivers include propulsion system failures and atmospheric prediction errors. Software and guidance hardware contribute the most to aerobraking risk. Propulsive capture risk is mainly driven by anomalous solar array degradation and propulsion system failures. While each subsystem contributes differently to the risk of each technique, results show that there exists little relative difference in the reliability of these capture techniques although uncertainty for the aerocapture estimates remains high given the lack of in-space demonstration.

Percy, Thomas K.↗