Results for “Risk Model” — Page 97

NASA NTRS2013

Adams-Based Rover Terramechanics and Mobility Simulator - ARTEMIS

The Mars Exploration Rovers (MERs), Spirit and Opportunity, far exceeded their original drive distance expectations and have traveled, at the time of this reporting, a combined 29 kilometers across the surface of Mars. The Rover Sequencing and Visualization Program (RSVP), the current program used to plan drives for MERs, is only a kinematic simulator of rover movement. Therefore, rover response to various terrains and soil types cannot be modeled. Although sandbox experiments attempt to model rover-terrain interaction, these experiments are time-intensive and costly, and they cannot be used within the tactical timeline of rover driving. Imaging techniques and hazard avoidance features on MER help to prevent the rover from traveling over dangerous terrains, but mobility issues have shown that these methods are not always sufficient. ARTEMIS, a dynamic modeling tool for MER, allows planned drives to be simulated before commands are sent to the rover. The deformable soils component of this model allows rover-terrain interactions to be simulated to determine if a particular drive path would take the rover over terrain that would induce hazardous levels of slip or sink. When used in the rover drive planning process, dynamic modeling reduces the likelihood of future mobility issues because high-risk areas could be identified before drive commands are sent to the rover, and drives planned over these areas could be rerouted. The ARTEMIS software consists of several components. These include a preprocessor, Digital Elevation Models (DEMs), Adams rover model, wheel and soil parameter files, MSC Adams GUI (commercial), MSC Adams dynamics solver (commercial), terramechanics subroutines (FORTRAN), a contact detection engine, a soil modification engine, and output DEMs of deformed soil. The preprocessor is used to define the terrain (from a DEM) and define the soil parameters for the terrain file. The Adams rover model is placed in this terrain. Wheel and soil parameter files can be altered in the respective text files. The rover model and terrain are viewed in Adams View, the GUI for ARTEMIS. The Adams dynamics solver calls terramechanics subroutines in FORTRAN containing the Bekker-Wong equations.

Trease, Brian P.↗

DOE OSTI2026

Demand response event simulator and risk-aware bidding tool for industrial customers

Incentive Based Demand Response (IBDR) program participation delivers financial benefits to the consumers and resiliency benefits to the electricity grid. Effectively participating in these programs as an industrial consumer requires bidding strategies that balance financial risk with operational constraints. Existing bidding tools tend not to fully incorporate stochastic IBDR event modeling, program specific baseline and payment/penalty calculations, or demand reduction process control schemes that account for the cascading impacts of shutdown in complex facilities. Here, this work presents an IBDR event simulator and risk-aware bidding framework tool integrating three key components: a flexible, parameterized demand response event generator that rigorously accounts for program structures and stochasticity, a demand response operational simulation model that generates explicit control strategies for load reduction, and a Monte Carlo simulator to evaluate financial risk for varied capacity bids. A case study at a wastewater treatment plant participating in PG&E's Capacity Bidding Program demonstrates the framework's utility. In the peak capacity price month of August, optimal bidding by the wastewater treatment plant nets a mean IBDR benefit of $101,000 (67% of the August electricity bill) with 0.4% probability of a financial loss. This framework enables industrial operators to make informed bidding decisions, negotiate better program terms with demand response load aggregators, and analyze energy flexibility investments at their facilities. Ultimately, this work reduces participation barriers in IBDR programs and supports the broader goal of enhancing grid reliability and renewable energy integration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

Developing High-Throughput Organ-on-a-Chip Models to Investigate the Effects of Ionizing Radiation on the Central Nervous System

One of the main health risks in human space exploration is central nervous system (CNS) damage by ionizing radiation due to exposure to the galactic cosmic rays (GCRs). In animal models, irradiation with simulated GCRs or their components has been shown to cause neuronal damage and neuroinflammation associated with cognitive and behavioral dysfunction. In general, the extent of CNS damage is partially regulated by the blood-brain barrier (BBB), which enables immune cells to enter the CNS. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal death, immune responses and oxidative stress, and thus could serve as a robust CNS-specific target for countermeasure development. However, studies on BBB permeability and astrocyte functions in regulating CNS responses to ionizing radiation have been limited, especially in human tissue/organ analogs. Therefore, we established a high-throughput 3D organ-on-a-chip system to study human CNS and BBB impairments in response to ionizing radiation, based on commercially available OrganoPlates (Mimetas, Inc.) seeded with primary or induced pluripotent stem cell-derived human cells. We investigated both immediate and delayed CNS responses to major GCR components: 0.15-0.5 Gy 250MeV/n 4-He, and 0.3-0.8 Gy 600 MeV/n 56-Fe; as well as to 0.5-1 Gy X-rays. We observed ionizing radiation-mediated increases in BBB permeability that was exacerbated by astrocyte presence and accompanied by morphological changes in endothelial cells and tight junctions, altered cytokine profile including TNFa upregulation, and increased oxidative stress. We also quantified irradiation-mediated changes in astrocyte activation and neuronal functions, revealing major astrocyte damage mediated by 600MeV/n 56-Fe particles. Thus, we demonstrate that deep space radiation may contribute to CNS damage by disrupting both astrocyte and endothelial cell components of the blood-brain barrier. Our next steps include mapping and validating the transcriptomic changes induced by simulated GCRs and their components in human CNS models. Ultimately, we aim to uncover potential novel targets for countermeasure developments to mitigate CNS damage in long duration spaceflight.

Radiation↗

DOE OSTI2024

Quantifying the basic reproduction number and underestimated fraction of Mpox cases worldwide at the onset of the outbreak

In 2022, there was a global resurgence of mpox, with different clinical-epidemiological features compared with previous outbreaks. Sexual contact was hypothesized as the primary transmission route, and the community of men having sex with men (MSM) was disproportionately affected. Because of the stigma associated with sexually transmitted infections, the real burden of mpox could be masked. We quantified the basic reproduction number (R 0 ) and the underestimated fraction of mpox cases in 16 countries, from the onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-/low-risk populations) and two-route (sexual/non-sexual transmission) mathematical model. Machine learning (ML) was harnessed to identify underestimation determinants. Estimated R 0 ranged between 1.37 (Canada) and 3.68 (Germany). The underestimation rates for the high- and low-risk populations varied between 25–93% and 65–85%, respectively. The estimated total number of mpox cases, relative to the reported cases, is highest in Colombia (3.60) and lowest in Canada (1.08). In the ML analysis, two clusters of countries could be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and the importance of religion. Given the substantial mpox underestimation, surveillance should be enhanced, and country-specific campaigns against the stigmatization of MSM should be organized, leveraging community-based interventions.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

Analysis of the cost, schedule, and risk for Lynx mirror assembly production

This paper analyzes a recently published model for Lynx Mirror Assembly production duration, to understand the sensitivity of duration to various factors. The factors considered are finite process yield, knowledge of the individual process times and finite server reliability. In all of these cases, closed form estimates are given along with numerical examples. This initial analysis indicates that accurate and precise knowledge of the process times is fundamental to making an accurate prediction of schedule duration. Analysis of the failure of any given server is also explained and can be used as the basis of a rational sparing policy.

Jonathan W. Arenberg↗

NASA NTRS2021

Radar Observations from the Haystack Ultrawideband Satellite Imaging Radar in 2019

The NASA Orbital Debris Program Office (ODPO) relies primarily on ground-based radar measurements to characterize the distribution of small debris, down to approximately 3 mm depending upon altitude and the sensor used, in low Earth orbit (LEO). Since the early 1990’s, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL) has been collecting radar measurements for the NASA ODPO under agreements with the U.S. Department of Defense. The Haystack Ultrawideband Satellite Imaging Radar (HUSIR) is the primary ground-based radar sensor used by the ODPO and provides data on orbital debris down to an approximate size of 5.5 mm below 1000km altitude using the NASA size estimation model (SEM). Since orbital debris of this size are a significant risk to both human and robotic missions in LEO, the sensitivity of this radar makes it a high-value sensor. The NASA ODPO radar measurements are conducted on a continual basis for monitoring and enabling modeling of the orbital debris environment over time. HUSIR observations from 2019 are the most recent snapshot of the environment that has been measured and analyzed to date. In recent years, HUSIR measurements indicated relatively stable populations for the orbital debris objects that it is able to detect. In 2019, several interesting events happened on-orbit, including the start of large constellation deployments into LEO, as well as the Indian anti-satellite test with Microsat-R (International Designator 2019-006A, U.S. Strategic Command Space Surveillance Network catalog number 43947). Due to these events, coupled with a general increase in the number of missions and participants launching missions in recent years, continual monitoring is necessary to determine the effects of this increased activity on the orbital debris environment. This paper will explore the results of the 2019 HUSIR radar measurements, including above-average flux measurements at lower LEO altitudes and the evolution of the flux during the time of observations.

James Murray↗

NASA NTRSDate not supplied

Radar Observations from the Haystack Ultrawideband Satellite Imaging Radar in 2019

The NASA Orbital Debris Program Office (ODPO) relies primarily on ground-based radar measurements to characterize the distribution of small debris, down to approximately 3 mm depending upon altitude and the sensor used, in low Earth orbit (LEO). Since the early 1990’s, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL) has been collecting radar measurements for the NASA ODPO under agreements with the U.S. Department of Defense. The Haystack Ultrawideband Satellite Imaging Radar (HUSIR) is the primary ground-based radar sensor used by the ODPO and provides data on orbital debris down to an approximate size of 5.5 mm below 1000km altitude using the NASA size estimation model (SEM). Since orbital debris of this size are a significant risk to both human and robotic missions in LEO, the sensitivity of this radar makes it a high-value sensor. The NASA ODPO radar measurements are conducted on a continual basis for monitoring and enabling modeling of the orbital debris environment over time. HUSIR observations from 2019 are the most recent snapshot of the environment that has been measured and analyzed to date. In recent years, HUSIR measurements indicated relatively stable populations for the orbital debris objects that it is able to detect. In 2019, several interesting events happened on-orbit, including the start of large constellation deployments into LEO, as well as the Indian anti-satellite test with Microsat-R (International Designator 2019-006A, U.S. Strategic Command Space Surveillance Network catalog number 43947). Due to these events, coupled with a general increase in the number of missions and participants launching missions in recent years, continual monitoring is necessary to determine the effects of this increased activity on the orbital debris environment. This paper will explore the results of the 2019 HUSIR radar measurements, including above-average flux measurements at lower LEO altitudes and the evolution of the flux during the time of observations.

James Murray↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRS2024

Coronado National Memorial Disasters: Investigating Geohazards & Slope Failure Susceptibility Utilizing NASA Earth Observations

The Coronado National Memorial (CORO), located in Hereford, Arizona, is situated along the United States' southern border, featuring recently established but still incomplete border barrier roads. This landscape is inherently prone to geohazards, and debris flow due to the steep mountainous topography, complex terrain, monsoonal rains, and freeze/thaw action - and the new infrastructure has exhibited these processes in the form of rockfall, embankment failure, and debris flow. NASA DEVELOP partnered with CORO to conduct a feasibility assessment of Earth observations for identification of geohazards and slope failure susceptibility. Leveraging Earth observations (United States Geological Survey 3D Elevation Program Digital Elevation Model and a locally obtained Light Detection And Ranging-derived Digital Elevation Model) from 2019 to 2023, and geospatial datasets starting from 2008, the study was able to provide tools to determine the focus for damage mitigation, identify areas most susceptible to slope failures, and prioritize at risk assets through three products: change detection maps, slope failure susceptibility maps, and a slope failure prioritization model. With an emphasis on monitoring high-risk areas and prioritizing mitigation efforts, the project addresses a critical gap in remediation strategies and aims to enhance preservation and safety of the region. Results of this study found that (i) the most identifiable areas of change were the road cuts and debris directly adjacent to the roads created for border construction, (ii) areas of highest slope failure susceptibility are located in mountainous areas with erosive geology, and (iii) roads resulting from border construction have approximately twice the risk of slope failure as roads created by the national park service.

Geohazard↗

DOE OSTI2026

A decade of progress in understanding and managing legacy well integrity for geologic carbon storage

This study reviews a decade of research progress in legacy well integrity and risk management for geologic carbon storage (GCS) to commemorate the 20 th anniversary of the Intergovernmental Panel on Climate Change’s 2005 Special Report on Carbon Capture and Storage. In the past ten years, legacy well research has benefited from global efforts to constrain emissions from abandoned oil and gas wells, a continued focus on well materials performance in the presence of CO 2 -rich fluids, and practical experience gained through GCS implementation. Field measurements of abandoned well emissions show that leakage is not universal or catastrophic but forms a continuum of low-to-moderate fluxes that depend on isolation integrity and environmental attenuation. Materials research has constrained the conditions under which Portland cements exhibit self-sealing and non-sealing behaviors, and has identified the impact of geomechanical properties, non-uniform pathway apertures, multi-phase flow, and impurities in the CO 2 stream, on leakage pathways as important new areas for investigation. GCS projects at brownfield sites have inspired the creation of new workflows that integrate various tools and technologies to manage legacy well leakage risks. GCS implementation has also motivated a push towards scenario-based well modeling that directly informs permit applications. These advances inspire new research questions for the coming decade, particularly around the level of legacy well leakage risk that is environmentally acceptable and tolerable to stakeholders when sequestering millions of tonnes of CO 2 annually.

Carbon capture and storage↗

NASA NTRSDate not supplied

A Review of International Space Station Extravehicular Activity Micrometeoroid and Orbital Debris Risk

Nearly 300 spacewalks have been conducted over the last 25 years to support the construction and maintenance of the International Space Station (ISS). During the nearly 2,000 hours of extra-vehicular activity (EVA), crew wore extra-vehicular mobility unit (EMU) “spacesuits” to help mitigate hazards of the space environment including impacts from micrometeoroid and orbital debris (MMOD) particles. The National Aeronautics and Space Administration (NASA) conducts detailed EMU MMOD risk analyses as part of the ISS EVA review and approval process. This paper provides a general historical overview of the ISS EVA MMOD risks and the associated risk assessment process. The NASA Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) Team produces the ISS EVA MMOD risk analyses using the Bumper MMOD risk analysis code in conjunction with detailed information about the EVA including crew member EVA work sites, crew translation paths, suit orientation, and durations. Detailed physical models of the EMU spacesuit and the ISS are included and tailored for each EVA analysis. Two types of MMOD risk are included: (1) Penetration, and (2) Catastrophic. Penetration risk is for any size leak in the EMU suit. Catastrophic risk is a subset of Penetration Risk and only includes penetrations that cause a 4mm diameter hole (or larger) in the pressure-maintaining “bladder” layer of the EMU suit. This size hole will exceed the ability of the EMU suit to compensate. The MMOD risk analyses also utilize the latest orbital debris environment and meteoroid environment models including additional transient factors to account for recent satellite break-ups and annual meteor storms. These additional factors and the associated increase in EVA MMOD risk are considered when scheduling EVAs to reduce EMU MMOD risk.

Dana M. Lear↗

NASA NTRSDate not supplied

A Review of International Space Station Extravehicular Activity Micrometeoroid and Orbital Debris Risk

Nearly 300 spacewalks have been conducted over the last 25 years to support the construction and maintenance of the International Space Station (ISS). During the nearly 2,000 hours of extra-vehicular activity (EVA), crew wore extra-vehicular mobility unit (EMU) “spacesuits” to help mitigate hazards of the space environment including impacts from micrometeoroid and orbital debris (MMOD) particles. The National Aeronautics and Space Administration (NASA) conducts detailed EMU MMOD risk analyses as part of the ISS EVA review and approval process. This paper provides a general historical overview of the ISS EVA MMOD risks and the associated risk assessment process. The NASA Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) Team produces the ISS EVA MMOD risk analyses using the Bumper MMOD risk analysis code in conjunction with detailed information about the EVA including crew member EVA work sites, crew translation paths, suit orientation, and durations. Detailed physical models of the EMU spacesuit and the ISS are included and tailored for each EVA analysis. Two types of MMOD risk are included: (1) Penetration, and (2) Catastrophic. Penetration risk is for any size leak in the EMU suit. Catastrophic risk is a subset of Penetration Risk and only includes penetrations that cause a 4mm diameter hole (or larger) in the pressure-maintaining “bladder” layer of the EMU suit. This size hole will exceed the ability of the EMU suit to compensate. The MMOD risk analyses also utilize the latest orbital debris environment and meteoroid environment models including additional transient factors to account for recent satellite break-ups and annual meteor storms. These additional factors and the associated increase in EVA MMOD risk are considered when scheduling EVAs to reduce EMU MMOD risk.

Dana M. Lear↗

NASA NTRSDate not supplied

Applying NASA-STD-7009 Standard for Models and Simulations to Surrogate and Other Statistical Models

NASA-STD-7009A, Standard for Models and Simulations, contains a worthy and insightfully-crafted credibility assessment. That set of metrics is intended to accumulate and communicate information that can be critical for users and their customers in evaluating findings due to a model or simulation. Was the model designed for the present use? Is it validated in the domain it’s being applied to? And so on. The standard was originally focused on physics-based models, but it can be applied more generally. It can – and should – be applied to high-impact statistical models, including surrogate models based on larger models and sims and regression models based directly on data. A key issue with many of such models is their relative simplicity compared to most M&S despite their use in high-risk engineering decisions. This report will make a case for using this Standard for a wider class of models, and how it might actually add value in excess of its cost for not only stakeholders, but modelers themselves. A rough example showing compliance for a statistical model is included.

K L Johnson↗

NASA NTRS1989

Dynamic systems-engineering process - The application of concurrent engineering

A system engineering methodology is described which enables users, particulary NASA and DOD, to accommodate changing needs; incorporate emerging technologies; identify, quantify, and manage system risks; manage evolving functional requirements; track the changing environment; and reduce system life-cycle costs. The approach is a concurrent, dynamic one which starts by constructing a performance model defining the required system functions and the interrelationships. A detailed probabilistic risk assessment of the system elements and their interrelationships is performed, and quantitative analysis of the reliability and maintainability of an engineering system allows its different technical and process failure modes to be identified and their probabilities to be computed. Decision makers can choose technical solutions that maximize an objective function and minimize the probability of failure under resource constraints.

Wiskerchen, Michael J.↗

DOE OSTI2023

Virtual Engineering of Low-Temperature Conversion

In this work, we present the development of an overarching software framework and supporting multiphysics models to simulate the end-to-end process of biomass conversion. This virtual engineering (VE) software is designed with the goal of accelerating research and development and reducing risk for market-relevant biomass conversion processes. We currently support multiple models, computing paradigms, and fidelities representing the steps of feedstock pretreatment, enzymatic hydrolysis, and bioconversion. Although this VE approach was developed to support a biomass workflow, we have designed each component in a way that allows us to easily support new domains, unit models, and feedstocks. We begin by presenting the user-facing aspects of the VE software and highlight how simulated elements are defined and linked by VE functions. We then present an overview of the high-fidelity computational fluid dynamics (CFD) models developed to support our target domain before segueing into our efforts to develop accurate and fast surrogate models, capturing the salient outcomes from the CFD simulations in a significantly less computationally demanding manner. We then present how VE calculations interface with a commercial techno-economic analysis software, Aspen Plus. We conclude by presenting VE case studies that leverage these methods and discuss how our methods can be extended to support a wide variety of accelerated biofuel commercialization pathways in the future.

biofuel↗

DOE OSTI2021

A Pathway for the Development of Advanced Reactor Mechanistic Source Term Modeling and Simulation Capabilities

Source term analysis, or the estimation of the potential radionuclide release to the environment during reactor events, is a central focus of the reactor licensing process and a vital part of risk-informed reactor design approaches. A mechanistic source term (MST) analysis is designed to realistically model the release and transport of radionuclides from the source to the environment for specific scenarios, while accounting for retention or transmutation phenomena and associated uncertainties. The objective of MST analyses, in comparison to bounding or conservative source term assessments, is to provide a non-biased representation of reactor risk and improve the information available for siting, emergency planning, and reactor design decisions. In support of the advanced reactor community in its pursuit of MST analysis capabilities, this work outlines a recommended research pathway for the development of MST modeling and simulation (mod/sim) tools.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

DC Arc-Flash Safety for 1,500VDC: Methodology, Verification, and Codifying

This project aimed to address critical gaps in understanding arc flash phenomena in commercially operating photovoltaic (PV) plants. Motivated by the global growth of PV systems and the associated safety risks, the research sought to enhance knowledge, improve incident energy prediction models, and contribute to the overall safety and reliability of PV installations.

14 SOLAR ENERGY↗