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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Using Risk Assessment Methodologies to Meet Management Objectives

Current decision making involves numerous possible combinations of technology elements, safety and health issues, operational aspects and process considerations to satisfy program goals. Identifying potential risk considerations as part of the management decision making process provides additional tools to make more informed management decision. Adapting and using risk assessment methodologies can generate new perspectives on various risk and safety concerns that are not immediately apparent. Safety and operational risks can be identified and final decisions can balance these considerations with cost and schedule risks. Additional assessments can also show likelihood of event occurrence and event consequence to provide a more informed basis for decision making, as well as cost effective mitigation strategies. Methodologies available to perform Risk Assessments range from qualitative identification of risk potential, to detailed assessments where quantitative probabilities are calculated. Methodology used should be based on factors that include: 1) type of industry and industry standards, 2) tasks, tools, and environment 3) type and availability of data and 4) industry views and requirements regarding risk & reliability. Risk Assessments are a tool for decision makers to understand potential consequences and be in a position to reduce, mitigate or eliminate costly mistakes or catastrophic failures.

DeMott, D. L.↗
DOE OSTI2023

Availability of state-level climate change projection resources for use in site-level risk assessment

In recent years, incorporating climate change considerations has become an important focus of organizations’ resilience planning and risk assessment efforts, including United States federal agencies. This has led to an increasing demand for higher-resolution and higher-quality climate projection information that is easy to understand for non-expert users. In particular, there is a demand for information about how climate change may affect high-impact, low-frequency (HILF) hazards that are central to risk assessments focused on infrastructure. While national-level resources like the National Climate Assessment provide information on climate impacts for different sectors and regions in the United States, downscaled information with location-specific context is often required for site-level resilience planning. As higher-resolution and higher-quality climate resources continue to be developed at the state level, it is imperative to understand ongoing and planned efforts, as well as key drivers for developing these state-level resources. Based primarily on stakeholder input from climate experts from 31 states, we identify key state-level climate resources, as well as drivers accelerating the development of these resources. We assess the availability of climate change resources, specifically those with information about HILF events that have been developed at the state level and can support users in conducting site-level resilience planning. We identify three key drivers or predictors for the development of climate change resources at the state level: (1) existence of state laws, mandates, Executive Orders, and other state policies, (2) existence of university partnerships; and (3) the makeup of the stakeholder groups (in terms of dominant discipline/expertise) participating in the effort. The diverse state strategies and resources surveyed in this study could support the incorporation of higher-resolution climate information into site-level planning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Biological Based Risk Assessment for Space Exploration

Exposures from galactic cosmic rays (GCR) - made up of high-energy protons and high-energy and charge (HZE) nuclei, and solar particle events (SPEs) - comprised largely of low- to medium-energy protons are the primary health concern for astronauts for long-term space missions. Experimental studies have shown that HZE nuclei produce both qualitative and quantitative differences in biological effects compared to terrestrial radiation, making risk assessments for cancer and degenerative risks, such as central nervous system effects and heart disease, highly uncertain. The goal for space radiation protection at NASA is to be able to reduce the uncertainties in risk assessments for Mars exploration to be small enough to ensure acceptable levels of risks are not exceeded and to adequately assess the efficacy of mitigation measures such as shielding or biological countermeasures. We review the recent BEIR VII and UNSCEAR-2006 models of cancer risks and their uncertainties. These models are shown to have an inherent 2-fold uncertainty as defined by ratio of the 95% percent confidence level to the mean projection, even before radiation quality is considered. In order to overcome the uncertainties in these models, new approaches to risk assessment are warranted. We consider new computational biology approaches to modeling cancer risks. A basic program of research that includes stochastic descriptions of the physics and chemistry of radiation tracks and biochemistry of metabolic pathways, to emerging biological understanding of cellular and tissue modifications leading to cancer is described.

Cucinotta, Francis A.↗
DOE OSTI2020

Chemical Impacts of Potential CO2 and Brine Leakage on Groundwater Quality with Quantitative Risk Assessment: A Case Study of the Farnsworth Unit

Potential leakage of reservoir fluids is considered a key risk factor for geologic CO2 sequestration (GCS), with concerns of their chemical impacts on the quality of overlying underground sources of drinking water (USDWs). Effective risk assessment provides useful information to guide GCS activities for protecting USDWs. In this study, we present a quantified risk assessment case study of an active commercial-scale CO2-enhanced oil recovery (CO2-EOR) and sequestration field, the Farnsworth Unit (FWU). Specific objectives of this study include: (1) to quantify potential risks of CO2 and brine leakage to the overlying USDW quality with response surface methodology (RSM); and (2) to identify water chemistry indicators for early detection criteria. Results suggest that trace metals (e.g., arsenic and selenium) are less likely to become a risk due to their adsorption onto clay minerals; no-impact thresholds based on site monitoring data could be a preferable reference for early groundwater quality evaluation; and pH is suggested as an indicator for early detection of a leakage. This study may provide quantitative insight for monitoring strategies on GCS sites to enhance the safety of long-term CO2 sequestration.

58 GEOSCIENCES↗
DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2014

Physics-Based Fragment Acceleration Modeling for Pressurized Tank Burst Risk Assessments

As part of comprehensive efforts to develop physics-based risk assessment techniques for space systems at NASA, coupled computational fluid and rigid body dynamic simulations were carried out to investigate the flow mechanisms that accelerate tank fragments in bursting pressurized vessels. Simulations of several configurations were compared to analyses based on the industry-standard Baker explosion model, and were used to formulate an improved version of the model. The standard model, which neglects an external fluid, was found to agree best with simulation results only in configurations where the internal-to-external pressure ratio is very high and fragment curvature is small. The improved model introduces terms that accommodate an external fluid and better account for variations based on circumferential fragment count. Physics-based analysis was critical in increasing the model's range of applicability. The improved tank burst model can be used to produce more accurate risk assessments of space vehicle failure modes that involve high-speed debris, such as exploding propellant tanks and bursting rocket engines.

Acceleration↗
DOE OSTI2020

Risk Assessment of Rare Events in Probabilistic Power Flow via Hybrid Multi-Surrogate Method

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2007

Space Radiation Risk Assessment for Future Lunar Missions

For lunar exploration mission design, radiation risk assessments require the understanding of future space radiation environments in support of resource management decisions, operational planning, and a go/no-go decision. The future GCR flux was estimated as a function of interplanetary deceleration potential, which was coupled with the estimated neutron monitor rate from the Climax monitor using a statistical model. A probability distribution function for solar particle event (SPE) occurrence was formed from proton fluence measurements of SPEs occurred during the past 5 solar cycles (19-23). Large proton SPEs identified from impulsive nitrate enhancements in polar ice for which the fluences are greater than 2 10(exp 9) protons/sq cm for energies greater than 30 MeV, were also combined to extend the probability calculation for high level of proton fluences. The probability with which any given proton fluence level of a SPE will be exceeded during a space mission of defined duration was then calculated. Analytic energy spectra of SPEs at different ranks of the integral fluences were constructed over broad energy ranges extending out to GeV, and representative exposure levels were analyzed at those fluences. For the development of an integrated strategy for radiation protection on lunar exploration missions, effective doses at various points inside a spacecraft were calculated with detailed geometry models representing proposed transfer vehicle and habitat concepts. Preliminary radiation risk assessments from SPE and GCR were compared for various configuration concepts of radiation shelter in exploratory-class spacecrafts.

Kim, Myung-Hee Y.↗
DOE OSTI2025

Failure Analysis–Informed Risk Assessment Framework for Geological Carbon Storage Using Numerical Simulation and Machine Learning

Geological carbon storage (GCS) is recognized as a critical technology for achieving large-scale reductions in anthropogenic carbon dioxide (CO 2 ) emissions. Ensuring long-term containment and safety requires robust risk assessment frameworks that account for geological uncertainty and identify potential failure scenarios. Among various indicators, the area of review (AoR) serves as a key metric for evaluating storage performance, regulatory compliance, and monitoring design, as it delineates the spatial extent impacted by pressure buildup and plume migration. However, conventional AoR-based risk assessments typically perturb parameters within narrow uncertainty bounds, potentially overlooking rare but high-impact events arising from extreme geological conditions. In this study, we present a failure analysis–informed risk assessment framework for large-scale GCS projects to improve site prescreening and monitoring design. A suite of 300 numerical simulations was generated using stochastic geological models that vary five key parameters: net-to-gross ratio, anisotropy azimuth, porosity multiplier, permeability multiplier, and vertical-to-horizontal permeability ratio. Among these, 200 realizations represent normal geological uncertainty, while 100 additional cases explore extreme yet plausible conditions for failure-case analysis. The AoR was simulated and computed from pressure and CO 2 saturation fields, where the baseline AoR boundary, representing the extent predicted under typical geological uncertainty, was defined as the union of 200 normal-range simulations, and failure was identified when extreme-range cases exceeded this baseline. Results show that incorporating broader parameter uncertainty produces significantly larger AoR extents, underscoring the potential underestimation of risk under conventional uncertainty ranges. Furthermore, spatial probability maps derived from failure-induced AoR exceedance identify regions requiring enhanced monitoring attention. Various machine learning (ML)–based classifiers were developed to predict failure occurrence from geological parameters, with the random forest model achieving the highest performance (F1-score of 0.986). Consistent findings from correlation coefficient, feature importance, and Sobol sensitivity analyses reveal that low net-to-gross ratios and permeability multipliers are the dominant risk drivers, reflecting reduced reservoir connectivity and limited pressure dissipation. Altogether, these results provide a novel framework for risk-informed site prescreening and monitoring design that explicitly considers rare but high-impact geological scenarios in GCS projects.

25 ENERGY STORAGE↗
NASA NTRS2007

Risk Assessment: Evidence Base

Human systems PRA (Probabilistic Risk Assessment: a) Provides quantitative measures of probability, consequence, and uncertainty; and b) Communicates risk and informs decision-making. Human health risks rated highest in ISS PRA are based on 1997 assessment of clinical events in analog operational settings. Much work remains to analyze remaining human health risks identified in Bioastronautics Roadmap.

Johnson-Throop, Kathy A.↗
NASA NTRSDate not supplied

Applications and Performance of a Lightning Risk Assessment using Geostationary Lightning Mapper (GLM) Data

Lightning is a hazard globally, particularly in lesser-developed countries. Cloud-to-ground lightning strikes are a threat to human safety, motivating a desire to monitor location-based lightning risk to mitigate harm. A lightning risk assessment for human safety was created that uses a combination of probabilistic risk calculation and spatial lightning mapping data to produce a risk magnitude. This risk magnitude evolves with time and changing conditions and is compared to tolerability thresholds in order to evaluate safety. The risk assessment using lightning mapping array (LMA) flash extent density (FED) data was found to perform comparatively (with respect to issuing lightning warnings) to a more standard method of monitoring lightning safety where National Lightning Detection Network (NLDN) flashes were monitored within a 5 nautical mile radius of a location of interest. This research investigates the replacement of LMA FED with FED from the Geostationary Lightning Mapper (GLM) within the risk assessment framework. Using GLM FED would allow for risk to be calculated outside of LMA domains and anywhere within the GLM field of view, including areas outside of the United States (US). A few applications of the risk method with GLM FED are shown and discussed for locations both in and outside of the US. Additionally, the performance of the risk method is compared based on the type of lightning input source (LMA vs GLM). The end goal of this work is to provide forecasters and end users with a tool to help monitor lightning risk in decision support scenarios.

Kelley Murphy↗
DOE OSTI2024

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This paper presents the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
NASA NTRSDate not supplied

Fault Management Algorithm Risk Assessment for the NASA Space Launch System

This presentation describes the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. The analysis scope, general assumptions and guide rules, and key modeling concepts were discussed to establish the basis of the risk assessments conducted. Initial analyses indicated a dominance in the total risk by software and firmware failures. This paper presents efforts applied to refine the software risks and the overall impact of implementing those modifications. Current analyses conducted on the detection functions implemented for the SLS Artemis II mission indicate primary risk drivers for the individual FM detection functions are flight software failures, firmware design failures, and hardware Common Cause Failures (CCFs). There still remains issues of how to account for time and redundancy in the software risk estimations.

probability risk analysis↗
NASA NTRS2011

Use of Probabilistic Risk Assessment in Shuttle Decision Making Process

This slide presentation reviews the use of Probabilistic Risk Assessment (PRA) to assist in the decision making for the shuttle design and operation. Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and disciplined approach to identifying and analyzing risk in complex systems and/or processes that seeks answers to three basic questions: (i.e., what can go wrong? what is the likelihood of these occurring? and what are the consequences that could result if these occur?) The purpose of the Shuttle PRA (SPRA) is to provide a useful risk management tool for the Space Shuttle Program (SSP) to identify strengths and possible weaknesses in the Shuttle design and operation. SPRA was initially developed to support upgrade decisions, but has evolved into a tool that supports Flight Readiness Reviews (FRR) and near real-time flight decisions. Examples of the use of PRA for the shuttle are reviewed.

Boyer, Roger L.↗
NASA NTRSDate not supplied

Radiation Data Collection and Risk Assessment

In order to advance the approach for parts assurance in the space community to the current decade and to remain nimble for the future, NASA GSFC, in collaboration with the Aerospace Corporation’s Mission Success Improvement Workshop and broad space community participation, has been reviewing and categorizing 40 years’ worth of on-orbit anomaly data to support broad assessments of radiation-related risk and the development of tools to rigorously assess risk in general project situations. Part of the changing approach in parts assurance involves phasing in the use of COTS parts (or as preferred, alternate grade parts). This talk will convey some of the early findings of the effort, including the fact that expanded use of COTS parts does not increase radiation-related risk, while also pointing out that a very small percentage of parts in a typical parts list are actives and thus require radiation assessment. These are important because they remove radiation as a factor in broad consideration in the use of COTS, without diminishing the importance of addressing the effects of radiation in space.

risk↗
DOE OSTI2023

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

42 ENGINEERING↗
NASA NTRS2004

Radiation Risk Assessment of the Individual Astronaut: A Complement to Radiation Interests at the NIH

Predicting human risks following exposure to space radiation is uncertain in part because of unpredictable distribution of high-LET and low-dose-derived damage amongst cells in tissues, unknown synergistic effects of microgravity upon gene- and protein-expression, and inadequately modeled processing of radiation-induced damage within cells to produce rare and late-appearing malignant cancers. Furthermore, estimation of risks of radiogenic outcome within small numbers of astronauts is not possible using classic epidemiologic study. It therefore seems useful to develop strategies of risk-assessment based upon large datasets acquired from correlated biological models useful for resolving radiogenic risk-assessment for irradiated individuals. In this regard, it is suggested that sensitive cellular biodosimeters that simultaneously report 1) the quantity of absorbed dose after exposure to ionizing radiation, 2) the quality of radiation delivering that dose, and 3) the biomolecular risk of malignant transformation be developed in order to resolve these NASA-specific challenges. Multiparametric cellular biodosimeters could be developed using analyses of gene-expression and protein-expression whereby large datasets of cellular response to radiation-induced damage are analyzed for markers predictive for acute response as well as cancer-risk. A new paradigm is accordingly addressed wherein genomic and proteomic datasets are registered and interrogated in order to provide statistically significant dose-dependent risk estimation in individual astronauts. This evaluation of the individual for assessment of radiogenic outcomes connects to NIH program in that such a paradigm also supports assignment of a given patient to a specific therapy, the diagnosis of response of that patient to therapy, and the prediction of risks accumulated by that patient during therapy - such as risks incurred by scatter and neutrons produced during high-energy Intensity-Modulated Radiation Therapy. Value of assessment of radiogenic outcome for individuals exposed to radiation is suggested to be common to both NASA and NIH.

Richmond, Robert C.↗