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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

CHP-PRA: Sensorimotor Countermeasures Proof of Concept

The capabilities included in Crew Health and Performance (CHP) systems are designed to keep the crew healthy, happy, and productive. In doing so, the CHP system allows reductions in human related risks to be realized. All human missions, though particularly future Artemis and Mars missions, have constraints on the mass and volume allocated to the CHP system. Thus, trades must be made on how to best buy down risk while still meeting other requirements. Understanding how different CHP capabilities influence medical, performance, and long-term health risks is key to making informed choices among these trades. To address this, an integrated CHP Probabilistic Risk Assessment (PRA) model is being developed. Much like how IMPACT is designed to allow medical resource trades informed by medical risks, CHP PRA will enable analogous trades in human system risks across all CHP functions and capabilities. There are 29 Human System Risk Board (HSRB) risk areas to consider for model implementation. This effort focused solely on the implementation of countermeasures associated with sensorimotor risk. The framework and lessons learned from this proof of concept will benefit the implementation of other risks and countermeasures. Astronauts experience sensorimotor changes when entering or exiting a microgravity environment. While most sensorimotor issues are resolved within a few days, they pose a serious risk to crew health and performance during the adaptation period. This adaptation period aligns with gravity transitions and key mission phases where the crew members may be required to complete challenging tasks ideally with an undisturbed sensorimotor system. Countermeasures such as training or pharmaceuticals can be used to mitigate risk. This effort began by identifying physiological changes to the sensorimotor system that could lead to functional performance decrements or medical conditions and potential countermeasures, so that the overall effect of sensorimotor disturbance on human risk could be captured. We then selected example countermeasures, mapped relationships between countermeasures and outcomes, and used results from existing sensorimotor investigations to define the relationship between a sensorimotor countermeasure and subsequent performance of a sensorimotor-affected task. Specifically, using shuttle landing performance data, we compared the use or absence of inflight training capabilities to yield a relative performance change that can be applied to the prediction of the risk associated with manual control performance in future missions, as depicted in the figure. This sensorimotor proof of concept demonstrates how HRP research information can be transformed into quantifiable relationships to describe changes in HSRB medical and performance risks.

Caroline R Austin↗
DOE OSTI2025

EVALUATION OF HRA METHODOLOGIES FOR APPLICATION IN SDP WORK

This study critically evaluates human reliability analysis (HRA) methodologies applicable to regulatory probabilistic safety assessment (PSA) model, with a particular focus on their role in supporting the significance determination process (SDP) in nuclear safety assessment. Firstly, three widely utilized HRA methods – IDHEAS-ECA, SPAR-H, and ASEP/THERP – were qualitatively and quantitatively assessed. Qualitative assessments were conducted using attributes from the NEA/CSNI/R(2015)1 report, while quantitative evaluations employed regression and correlation analyses to compare predicted human error probabilities (HEPs) against empirical data. Results reveal distinct strengths, for example, IDHEAS-ECA’s robust predictive accuracy and K-HRA’s alignment with operational practices. In addition, dependency analysis and recovery analysis were critically evaluated. For dependency analysis, the methods’ handling of inter-task dependencies and their impact on HEPs were examined, while recovery analysis highlighted strategies for mitigating failure events. Furthermore, strategies were proposed to evaluate performance-shaping factors under conditions of reduced human performance, such as stress, fatigue, or cognitive overload, addressing specific challenges faced in SDP evaluations. Human errors from KINS’s operational performance information system event reports were evaluated as a case study. This study identifies gaps and provides actionable insights to ensure their validity and applicability in SDP HRA applications. This paper is a part of research conducted by KINS, and it should be noted that this result does not represent the regulatory position of KINS.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Development of A Crew Health and Performance System Probabilistic Risk Assessment Tool: Proof-of-Concept Approach

The crew health and performance (CHP) system represents the span of technological interventions and tested processes and procedures that in combination address the human risk to space flight. The Human Research Program (HRP) mental model of the CHP system breaks the capabilities needed to meet NASA human flight systems standards into specific categories (i.e., countermeasures, behavioral health, medical intervention). These categories are further broken down into specific sub-groups generally associated with the human system risks that these capabilities seek to mitigate. Like the approach used to develop the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Analysis Tool (MEDPRAT), HRP tasked NASA GRC’s Cross-Cutting Computational Modeling Project with developing a CHP probabilistic risk assessment tool, the CHP-PRA. The CHP-PRA model seeks to quantify and relatively assess the human risk state within the crew health and performance domain, using a combination of knowledge about human system risks and technology and practices likely to be applied during space flight missions. This modeling system will incorporate customer and stakeholder feedback and be flexible enough to address multiple different questions about important low-level mission-specific parameters. This presentation will introduce the initial concept and development timeline for this tool and demonstrate proof-of-concept through an application addressing a specific human risk question posed within the Artemis program.

Risk analysis↗
NASA NTRS2007

Identification and Control of Aircrafts using Multiple Models and Adaptive Critics

We compared two possible implementations of local linear models for control: one approach is based on a self-organizing map (SOM) to cluster the dynamics followed by a set of linear models operating at each cluster. Therefore the gating function is hard (a single local model will represent the regional dynamics). This simplifies the controller design since there is a one to one mapping between controllers and local models. The second approach uses a soft gate using a probabilistic framework based on a Gaussian Mixture Model (also called a dynamic mixture of experts). In this approach several models may be active at a given time, we can expect a smaller number of models, but the controller design is more involved, with potentially better noise rejection characteristics. Our experiments showed that the SOM provides overall best performance in high SNRs, but the performance degrades faster than with the GMM for the same noise conditions. The SOM approach required about an order of magnitude more models than the GMM, so in terms of implementation cost, the GMM is preferable. The design of the SOM is straight forward, while the design of the GMM controllers, although still reasonable, is more involved and needs more care in the selection of the parameters. Either one of these locally linear approaches outperform global nonlinear controllers based on neural networks, such as the time delay neural network (TDNN). Therefore, in essence the local model approach warrants practical implementations. In order to call the attention of the control community for this design methodology we extended successfully the multiple model approach to PID controllers (still today the most widely used control scheme in the industry), and wrote a paper on this subject. The echo state network (ESN) is a recurrent neural network with the special characteristics that only the output parameters are trained. The recurrent connections are preset according to the problem domain and are fixed. In a nutshell, the states of the reservoir of recurrent processing elements implement a projection space, where the desired response is optimally projected. This architecture trades training efficiency by a large increase in the dimension of the recurrent layer. However, the power of the recurrent neural networks can be brought to bear on practical difficult problems. Our goal was to implement an adaptive critic architecture implementing Bellman s approach to optimal control. However, we could only characterize the ESN performance as a critic in value function evaluation, which is just one of the pieces of the overall adaptive critic controller. The results were very convincing, and the simplicity of the implementation was unparalleled.

Principe, Jose C.↗
NASA NTRSDate not supplied

Airburst & Blast Damage Modeling Sensitivities for Asteroid Impact Risk Assessment

Blast overpressure from a high-energy airburst or surface impact is the primary source of damage from potentially hazardous asteroid strikes. There are many sources of uncertainty in evaluating these potential damage risks, both in the approaches used to model the entry, breakup, and airburst behaviors of diverse asteroid properties, and in the blast modeling approaches used to estimate the ground damage from these very large-scale, high-energy events. In this study, we use NASA’s Probabilistic Asteroid Impact Risk (PAIR) model to investigate trends and sensitivities in asteroid airburst altitudes and the resulting blast damage estimates across a range of asteroid sizes. In particular, we show how uncertainties in asteroid breakup behavior and effective airburst altitudes combine with height-of-burst (HOB) blast damage models to produce key sensitivities and trends in the amount of damage expected from different asteroid sizes and airburst altitudes. We show airburst altitude ranges and probabilities stemming from asteroid entry and breakup modeling uncertainties, compare differences between traditional nuclear-based HOB blast models and simulation-based HOB models for larger asteroid energies, and show how the resulting interplay between likely burst altitudes and optimal burst heights affects blast damage trends across different asteroid sizes. Finally, we combine the relative likelihoods of asteroid sizes, airburst altitudes, and resulting blast damage severity to evaluate what airburst regimes pose the highest overall level of risk (when considering both the relative likelihood and scale of potential damage) for a mid-sized asteroid threat scenario. Results show what asteroid size regimes are most sensitive to airburst and blast modeling uncertainties, provide insight into nonintuitive trends in the size and severity of blast damage expected from different airburst events, and highlight where additional blast modeling studies or refinements may help improve future impact risk estimates

ATAP↗
NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗
NASA NTRS1980

Evaluation of computing systems using functionals of a Stochastic process

An intermediate model was used to represent the probabilistic nature of a total system at a level which is higher than the base model and thus closer to the performance variable. A class of intermediate models, which are generally referred to as functionals of a Markov process, were considered. A closed form solution of performability for the case where performance is identified with the minimum value of a functional was developed.

Meyer, J. F.↗
NASA NTRS1993

Interactive Reliability Model for Whisker-toughened Ceramics

Wider use of ceramic matrix composites (CMC) will require the development of advanced structural analysis technologies. The use of an interactive model to predict the time-independent reliability of a component subjected to multiaxial loads is discussed. The deterministic, three-parameter Willam-Warnke failure criterion serves as the theoretical basis for the reliability model. The strength parameters defining the model are assumed to be random variables, thereby transforming the deterministic failure criterion into a probabilistic criterion. The ability of the model to account for multiaxial stress states with the same unified theory is an improvement over existing models. The new model was coupled with a public-domain finite element program through an integrated design program. This allows a design engineer to predict the probability of failure of a component. A simple structural problem is analyzed using the new model, and the results are compared to existing models.

Palko, Joseph L.↗
DOE OSTI2025

A knowledge-informed large language model framework for U.S. nuclear power plant shutdown initiating event classification for probabilistic risk assessment

Identifying and classifying shutdown initiating events (SDIEs) is critical for developing shutdown probabilistic risk assessment for nuclear power plants. Existing computational approaches cannot achieve satisfactory performance due to the challenges of unavailable large, labeled datasets, imbalanced event types, and label noise. To address these challenges, we propose a hybrid pipeline that integrates a knowledge-informed machine learning model to prescreen non-SDIEs and a large language model (LLM) to classify SDIEs into four types. In the prescreening stage, we proposed a set of 44 SDIE text patterns that consist of the most salient keywords and phrases from six SDIE types. Text vectorization based on the SDIE patterns generates feature vectors that are highly separable by using a simple binary classifier. The second stage builds Bidirectional Encoder Representations from Transformers (BERT)-based LLM, which learns generic English language representations from self-supervised pretraining on a large dataset and adapts to SDIE classification by fine-tuning it on an SDIE dataset. The proposed approaches are evaluated on a dataset with 10,928 events using precision, recall ratio, F 1 score, and average accuracy. In conclusion, the results demonstrate that the prescreening stage can exclude more than 97% non-SDIEs, and the LLM achieves an average accuracy of 95.1% for SDIE classification.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Impact Real World System Validation

Introduction NASA has developed a new evidence-based data-driven probabilistic risk assessment and tradespace analysis tool as a successor to the Integrated Medical Model. This updated decision support tool is known as IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). IMPACT estimates the frequency and consequences of medical conditions that might arise during exploration missions. A validation analysis of IMPACT was performed with respect to a set of International Space Station (ISS) and Shuttle Transportation System (STS) real world system (RWS) referent data due to the limited referent data available from exploration missions. Methods Observed mission and crew characteristics from STS and ISS missions were used as model inputs within MEDPRAT (Medical Extensible Dynamic Probabilistic Risk Assessment Tool). For each mission, two hundred thousand simulations were generated. For each mission, model outputs included occurrence counts for each condition, total medical events (TME), and the probability of loss of crew life (LOCL). These simulated model outputs were compared to the RWS referent data. Results The predicted number of total medical events exceeded the total RWS medical events for ISS missions and combined ISS and STS missions and fell within the 90% confidence interval for STS missions. For the 32 ISS missions simulated by IMPACT, the number of total medical events was overpredicted for 19 missions and fell within the 90% confidence interval for 13 missions. For the 21 STS missions, the total number of medical events was overpredicted for 3 missions, fell within the 90% confidence interval for 16 missions, and was underpredicted for 2 missions. Combined, 29 missions were in range, 22 were overpredicted, and 2 were underpredicted. The predicted LOCL probability for the 32 ISS missions, the 21 STS missions, and the combined ISS and STS missions was consistent with the zero LOCL events observed in the RWS referent data. The validation analysis included a comparison of the number of medical events predicted by IMPACT and the number of medical events observed in the RWS data on a condition-by-condition basis. For ISS missions, 50 conditions were in range, 52 conditions were statistically underpowered (not enough observed sample to draw any conclusions on precision), 8 conditions were overpredicted, and 9 conditions were underpredicted. Overall, only 14% (17/119) of conditions were out of range for STS missions, 40 conditions were in range, 59 conditions were statistically underpowered, 10 conditions were overpredicted, and 10 conditions were underpredicted. Overall, only 17% (20/119) of conditions were out of range. For combined ISS and STS missions, 11 conditions were overpredicted, and 11 conditions were underpredicted. Overall, only 18% (22/119) of conditions were out of range. For combined ISS and STS missions, 49 conditions were in range, 46 conditions were statistically underpowered, 18 conditions were overpredicted, and 8 conditions were underpredicted. Overall, 21% (26/121) of conditions were out of range. Conclusion The results of this validation analysis should not be interpreted as a pass/fail test of the validity of IMPACT. Instead, this validation analysis should be used to assess some of the IMPACT outcomes in terms of consistencies and inconsistencies with the ISS and STS RWS referent data.

L. Boley↗
NASA NTRS1991

Probabilistic/Fracture-Mechanics Model For Service Life

Computer program makes probabilistic estimates of lifetime of engine and components thereof. Developed to fill need for more accurate life-assessment technique that avoids errors in estimated lives and provides for statistical assessment of levels of risk created by engineering decisions in designing system. Implements mathematical model combining techniques of statistics, fatigue, fracture mechanics, nondestructive analysis, life-cycle cost analysis, and management of engine parts. Used to investigate effects of such engine-component life-controlling parameters as return-to-service intervals, stresses, capabilities for nondestructive evaluation, and qualities of materials.

Watkins, T., Jr.↗
NASA NTRS1990

The composite load spectra project

Probabilistic methods and generic load models capable of simulating the load spectra that are induced in space propulsion system components are being developed. Four engine component types (the transfer ducts, the turbine blades, the liquid oxygen posts and the turbopump oxidizer discharge duct) were selected as representative hardware examples. The composite load spectra that simulate the probabilistic loads for these components are typically used as the input loads for a probabilistic structural analysis. The knowledge-based system approach used for the composite load spectra project provides an ideal environment for incremental development. The intelligent database paradigm employed in developing the expert system provides a smooth coupling between the numerical processing and the symbolic (information) processing. Large volumes of engine load information and engineering data are stored in database format and managed by a database management system. Numerical procedures for probabilistic load simulation and database management functions are controlled by rule modules. Rules were hard-wired as decision trees into rule modules to perform process control tasks. There are modules to retrieve load information and models. There are modules to select loads and models to carry out quick load calculations or make an input file for full duty-cycle time dependent load simulation. The composite load spectra load expert system implemented today is capable of performing intelligent rocket engine load spectra simulation. Further development of the expert system will provide tutorial capability for users to learn from it.

Newell, J. F.↗
NASA NTRSDate not supplied

MEDPRAT Treatment Clusters: Improving Representation of Mission Medical Risk

INTRODUCTION The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) implements a computational model that aims to quantify spaceflight medical risk by utilizing probabilistic techniques to simulate critical event incidence and outcomes over thousands of simulated mission trials. The goal of MEDPRAT is to characterize mission medical risk and provide insight into medical resource utilization. In order to analyze the medical resource space, treatment must be mapped from each simulated condition, and resources consumed as a result of this treatment must be tracked throughout the course of the mission. A new MEDPRAT feature, ‘treatment clusters’, provide a more sophisticated method of defining the structure and interaction between resources, more closely mimicking the way treatment is carried out clinically. METHODS Treatment clusters expand on the two existing treatment groupings (combination and alternate) adding a new grouping: bundled treatment. Treatment clusters may be combined to any depth, giving users the ability to specify complex treatment trees whose behavior is governed by several user-specified parameters. This approach emphasizes reusability, as treatment clusters, once defined, can be used to create more complex treatment trees or applied to many conditions. By configuring parameters for contribution, efficacy, necessity, primacy, and equivalence, resource relationships and dependencies can be more accurately represented, thereby allowing users to build capabilities with desired treatment properties, for example an intravenous capability for conditions such as anaphylaxis, acute radiation syndrome, etc. MEDPRAT v1.0 remains backward compatible with existing treatment structures, giving users the ability to define new treatment clusters as evidence becomes available, without having to recode their existing treatment databases. In addition to facilitating the representation of more complex treatment options, by pairing treatment clusters with the internal optimization routine, the MEDPRAT set selector, medical resources can be identified as organized in bundles, where appropriate, so that optimized resource sets include groups of highly-dependent resources only when all resources of the group are together. For example, it would be wasteful to include ultrasound gel but not an ultrasound machine, since the gel provides no benefit as a treatment without the ultrasound machine. With treatment clusters, the user may require that both resources are available to provide any benefit as treatment, so that if one resource is optimized out of the set, the other resource will be optimized out as well. RESULTS AND CONCLUSIONS We will report on MEDPRAT treatment clusters used in a bundling study under the IMPACT project of the ExMC element. We will discuss an example of a complex treatment tree. Through the implementation of this feature MEDPRAT enables treatment to be defined and applied in a way that is more representative of the real world, providing more accurate insight into mission medical risk and the medical resource space.

Lawrence Leinweber↗
DOE OSTI2026

Generative large language models for predictive maintenance planning

Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Integration of NASA-Developed Lifing Technology for PM Alloys into DARWIN (registered trademark)

In recent years, Southwest Research Institute (SwRI) and NASA Glenn Research Center (GRC) have worked independently on the development of probabilistic life prediction methods for materials used in gas turbine engine rotors. The two organizations have addressed different but complementary technical challenges. This report summarizes a brief investigation into the current status of the relevant technology at SwRI and GRC with a view towards a future integration of methods and models developed by GRC for probabilistic lifing of powder metallurgy (P/M) nickel turbine rotor alloys into the DARWIN (Darwin Corporation) software developed by SwRI.

McClung, R. Craig↗
NASA NTRS1993

Reliability based structural optimization - A simplified safety index approach

A probabilistic optimal design methodology for complex structures modelled with finite element methods is presented. The main emphasis is on developing probabilistic analysis tools suitable for optimization. An advanced second-moment method is employed to evaluate the failure probability of the performance function. The safety indices are interpolated using the information at mean and most probable failure point. The minimum weight design with an improved safety index limit is achieved by using the extended interior penalty method of optimization. Numerical examples covering beam and plate structures are presented to illustrate the design approach. The results obtained by using the proposed approach are compared with those obtained by using the existing probabilistic optimization techniques.

Reddy, Mahidhar V.↗
NASA NTRS1991

Pattern-recognition techniques applied to performance monitoring of the DSS 13 34-meter antenna control assembly

The results of applying pattern recognition techniques to diagnose fault conditions in the pointing system of one of the Deep Space network's large antennas, the DSS 13 34-meter structure, are discussed. A previous article described an experiment whereby a neural network technique was used to identify fault classes by using data obtained from a simulation model of the Deep Space Network (DSN) 70-meter antenna system. Described here is the extension of these classification techniques to the analysis of real data from the field. The general architecture and philosophy of an autonomous monitoring paradigm is described and classification results are discussed and analyzed in this context. Key features of this approach include a probabilistic time-varying context model, the effective integration of signal processing and system identification techniques with pattern recognition algorithms, and the ability to calibrate the system given limited amounts of training data. Reported here are recognition accuracies in the 97 to 98 percent range for the particular fault classes included in the experiments.

Mellstrom, J. A.↗
NASA NTRS2003

An Advanced Hierarchical Hybrid Environment for Reliability and Performance Modeling

The key issue we intended to address in our proposed research project was the ability to model and study logical and probabilistic aspects of large computer systems. In particular, we wanted to focus mostly on automatic solution algorithms based on a state-space exploration as their first step, in addition to the more traditional discrete-event simulation approaches commonly employed in industry. One explicitly-stated goal was to extend by several orders of magnitude the size of models that can be solved exactly, using a combination of techniques: 1) Efficient exploration and storage of the state space using new data structures that require an amount of memory sublinear in the number states; and 2) Exploitation of the existing symmetries in the matrices describing the system behavior using Kronecker operators. Not only we have been successful in achieving the above goals, but we exceeded them in many respects.

Ciardo, Gianfranco↗