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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Occupant Protection at NASA

This slide presentation reviews NASA's efforts to arrive at protection of occupants of the ORION space craft on landing. An Abbreviated Injury Scale (AIS) has been developed, it is an anatomically-based, consensus-derived, global severity scoring system that classifies each injury by body region according to its relative importance on a 6-point ordinal scale. It reviews an Operationmally Relevant Injury Scale (ORIS), a classification methodology, and shows charts that detail the results of applying this ORIS to the injury databases. One chart uses NASCAR injury classification. It discusses providing a context for the level of risk inherent in the Orion landings in terms that people understand and have a sense for. For example is the risk of injury during an Orion landing roughly the same, better or worse than: An aircraft carrier landing, a NASCAR crash, or a helicopter crash, etc? The data for NASCAR and Indy Racing league (IRL) racing crash and injury data was reviewed. The risk from the Air Force, Navy, and Army injury data was also reviewed. Past NASA and the Soyuz programs injury risks are also reviewed. The work is an attempt to formulate a recommendation to the Orion Project for an acceptable level of injury risk associated with Nominal and Off-Nominal landing cases. The presentation also discusses the data mining and use of the data to Validate NASA Operationally-Relevant Injury Scale (NORIS) / Military Operationally-Relevant Injury Scale (MORIS), developing injury risk criteria, the types of data that are required, NASCAR modeling techniques and crash data, and comparison with the Brinkley model. The development of injury risk curves for each biodynamic response parameter is discussed. One of the main outcomes of this work is to establish an accurate Automated Test Dummy (ATD) that can be used to measure human tolerances.

Somers, Jeffrey↗
NASA NTRS2019

Towards the Development of Real-Time Normalized Burn Ratio (NBR) and Delta NBR Imagery from GOES-16/17 and S-NPP

Burned landscapes present difficult hydrologic forecasting challenges for National Weather Service Offices. Burned soils and landscapes can be conducive to the development of flash flooding and landslides from heavy precipitation events. The severity of the burn scar can be directly related to the risk for debris flows and flash flooding. Burned Area Reflectance Classification (BARC) map is generated to indicate the degree of burn severity, which is generated initially by high-resolution satellite imagery from sources such as Landsat, and later by labor-intensive efforts conducted at the burn scar by Burned Area Emergency Response (BAER) teams. The challenge for operational meteorologists is that these sources of information are not readily available in near real-time. Landsat imagery, for example, may only be available about once every eight days, and cloudy conditions can obstruct the observation of the burn scar during a single pass. BAER teams cannot conduct assessments until the wildfire has been at least 40 percent contained (up to 80 percent in some regions), and the process itself can take further days to weeks to complete depending on a number of factors. To help remedy this lapse in knowledge, NASA SPoRT has developed the generation of NBR imagery in the Advanced Weather Interactive Processing System (AWIPS) using data from the operational GOES 16 and 17 satellites and S-NPP. This presentation will discuss the development of the GOES- and SNPP-derived NBR and dNBR imagery and their initial evaluation by real-time decision makers.

Berndt, Emily↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
DOE OSTI2014

Risk Prediction Models of Locoregional Failure After Radical Cystectomy for Urothelial Carcinoma: External Validation in a Cohort of Korean Patients

Purpose: To evaluate the predictive accuracy and general applicability of the locoregional failure model in a different cohort of patients treated with radical cystectomy. Methods and Materials: A total of 398 patients were included in the analysis. Death and isolated distant metastasis were considered competing events, and patients without any events were censored at the time of last follow-up. The model included the 3 variables pT classification, the number of lymph nodes identified, and margin status, as follows: low risk (≤pT2), intermediate risk (≥pT3 with ≥10 nodes removed and negative margins), and high risk (≥pT3 with <10 nodes removed or positive margins). Results: The bootstrap-corrected concordance index of the model 5 years after radical cystectomy was 66.2%. When the risk stratification was applied to the validation cohort, the 5-year locoregional failure estimates were 8.3%, 21.2%, and 46.3% for the low-risk, intermediate-risk, and high-risk groups, respectively. The risk of locoregional failure differed significantly between the low-risk and intermediate-risk groups (subhazard ratio [SHR], 2.63; 95% confidence interval [CI], 1.35-5.11; P<.001) and between the low-risk and high-risk groups (SHR, 4.28; 95% CI, 2.17-8.45; P<.001). Although decision curves were appropriately affected by the incidence of the competing risk, decisions about the value of the models are not likely to be affected because the model remains of value over a wide range of threshold probabilities. Conclusions: The model is not completely accurate, but it demonstrates a modest level of discrimination, adequate calibration, and meaningful net benefit gain for prediction of locoregional failure after radical cystectomy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2014

Visual Impairment/lntracranial Pressure Risk Clinical Care Data Tools

Prior to 2010, several ISS crewmembers returned from spaceflight with changes to their vision, ranging from a mild hyperopic shift to frank disc edema. As a result, NASA expanded clinical vision testing to include more comprehensive medical imaging, including Optical Coherence Tomography and 3 Tesla Brain and Orbit MRIs. The Space and Clinical Operations (SCO) Division developed a clinical practice guideline that classified individuals based on their symptoms and diagnoses to facilitate clinical care. For the purposes of clinical surveillance, this classification was applied retrospectively to all crewmembers who had sufficient testing for classification. This classification is also a tool that has been leveraged for researchers to identify potential risk factors. In March 2014, driven in part by a more comprehensive understanding of the imaging data and increased imaging capability on orbit, the SCO Division revised their clinical care guidance to outline in‐flight care and increase post‐flight follow up. The new clinical guidance does not include a classification scheme

Van Baalen, Mary↗
DOE OSTI2019

Panel Session 126: US and International Waste Classifications; Differences and Issues

The panel focused and addressed the existing US waste classification scheme set forth in US 10 CFR Part 61 and DOE Order 435.1 focusing upon the commensurate risk associated with the disposal of radioactive Low-Level (LLW) and High-Level Waste (HLW). The panel provided insights on the ongoing efforts by the NRC in conducting its Very LLW Scoping Study, the waste classification scheme used by the IAEA with a comparison of that system to the waste classification regulatory process in the US and the increasing role of RCRA sites in disposal of LLW in the US. The process used for very low activity waste disposal known as Bulk Survey for Release in the State of Tennessee was discussed as well as various studies conducted by EPRI addressing the relative risks of very low activity waste will be addressed. The panel strived to compare the risks of radioactive LLW and HLW to the operational process used for its disposal in the US and Internationally and determine if changes are needed in the process. The audience heard the perspectives from six panelists that addressed issues ranging from the technical basis used to establish the waste classification system specified in Title 10 of the Code of Federal Regulations (CFR) Part 61 developed by the Nuclear Regulatory Commission; the approaches and impacts for dispositioning materials with Very Low Levels of Radioactive Waste (VLLRW); approaches used by the Department of Energy (DOE) to disposition Low-Level Radioactive Waste (LLW) in accordance with Order 435.1; and methods adopted by the international community to classify and disposition LLW and Intermediate Level Waste (ILW). A summary of each of the presentations given by the panelists is provided herein. Presentations proposed include: 1 - U.S. Low-Level Radioactive Waste Classification System 10 CFR Part 61 - Basis, Updates, and Issues (Rateb (Boby) Abu Eid, US NRC (USA)); 2 - EPRI Research on the Technical Basis for VLLW Classification (Lisa Edwards, EPRI (USA)); 3 - Division of Radiological Health (Anthony Hogan, State of Tennessee (USA)); 4 - Perspectives on VLLW Based on EPA's Low Activity Waste Effort (Thomas Peake, US EPA (USA)); 5 - US Department of Energy Perspectives on Waste Classification (Justin Marble, US DOE (USA))

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy

The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Histologic Subtype in Core Lung Biopsies of Early-Stage Lung Adenocarcinoma is a Prognostic Factor for Treatment Response and Failure Patterns After Stereotactic Body Radiation Therapy

Purpose: Stereotactic body radiation therapy (SBRT) has emerged as an effective treatment for early-stage lung cancer. The histologic subtype of surgically resected lung adenocarcinoma is recognized as a prognostic factor, with the presence of solid or micropapillary patterns predicting poor outcomes. We describe the outcomes after SBRT for early-stage lung adenocarcinoma stratified by histologic subtype. Methods and Materials: We identified 119 consecutive patients (124 lesions) with stage I to IIA lung adenocarcinoma who had undergone definitive SBRT at our institution from August 2008 to August 2015 and had undergone core biopsy. Histologic subtyping was performed according to the 2015 World Health Organization classification. Of the 124 tumors, 37 (30%) were a high-risk subtype, defined as containing a component of solid and/or micropapillary pattern. The cumulative incidences of local, nodal, regional, and distant failure were compared between the high-risk and non–high-risk adenocarcinoma subtypes using Gray's test, and multivariable-adjusted hazard ratios (HRs) were estimated from propensity score–weighted Cox regression models. Results: The median follow-up for the entire cohort was 17 months and for surviving patients was 21 months. The 1-year cumulative incidence of and adjusted HR for local, nodal, regional, and distant failure in high-risk versus non–high-risk lesions was 7.3% versus 2.7% (HR 16.8; 95% confidence interval [CI] 3.5-81.4), 14.8% versus 2.6% (HR 3.8; 95% CI 0.95-15.0), 4.0% versus 1.2% (HR 20.9; 95% CI 2.3-192.3), and 22.7% versus 3.6% (HR 6.9; 95% CI 2.2-21.1), respectively. No significant difference was seen with regard to overall survival. Conclusions: The outcomes after SBRT for early-stage adenocarcinoma of the lung correlate highly with histologic subtype, with micropapillary and solid tumors portending significantly higher rates of locoregional and metastatic progression. In this context, the histologic subtype determined from core biopsies is a prognostic factor and could have important implications for patient selection, adjuvant treatment, biopsy methods, and clinical trial design.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R 2 =0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2017

Classification without labels: learning from mixed samples in high energy physics

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2012

Building a Shared Definitional Model of Long Duration Human Spaceflight

Objective: To establish the need for a shared definitional model of long duration human spaceflight, that would provide a framework and vision to facilitate communication, research and practice In 1956, on the eve of human space travel, Hubertus Strughold first proposed a "simple classification of the present and future stages of manned flight" that identified key factors, risks and developmental stages for the evolutionary journey ahead. As we look to new destinations, we need a current shared working definitional model of long duration human space flight to help guide our path. Here we describe our preliminary findings and outline potential approaches for the future development of a definition and broader classification system

Arias, Diana↗
DOE OSTI2019

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2004

Human Health Risk Assessment Simulations in a Distributed Environment for Shuttle Launch

During the launch of a rocket under prevailing weather conditions, commanders at Cape Canaveral Air Force station evaluate the possibility of whether wind blown toxic emissions might reach civilian and military personnel in the near by area. In our model, we focused mainly on Hydrogen chloride (HCL), Nitrogen oxides (NOx) and Nitric acid (HNO3), which are non-carcinogenic chemicals as per United States Environmental Protection Agency (USEPA) classification. We have used the hazard quotient model to estimate the number of people at risk. It is based on the number of people with exposure above a reference exposure level that is unlikely to cause adverse health effects. The risk to the exposed population is calculated by multiplying the individual risk and the number in exposed population. The risk values are compared against the acceptable risk values and GO or NO-go situation is decided based on risk values for the Shuttle launch. The entire model is simulated over the web and different scenaria can be generated which allows management to choose an optimum decision.

Thirumalainambi, Rajkumar↗
DOE OSTI2018

Variability of urinary pesticide metabolite concentrations during pregnancy in the MARBLES Study

Highlights: • Pyrethroid metabolite, 3-PBA, and organophosphate metabolite, TCPy, were commonly detected in urine samples collected from pregnant women in the MARBLES study, an enriched risk cohort. • Within subject pooling of multiple urine specimens may improve exposure classification for environmental epidemiology studies. • Pesticide metabolite concentrations from multiple pregnancy urine samples had relatively low within-person correlation. Variability of short-lived urinary pesticide metabolites during pregnancy raises challenges for exposure assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2015

Phase-Angle Dependence of Determinations of Diameter, Albedo, and Taxonomy: A Case Study of NEO 3691 Bede

Parameters important for NEO risk assessment and mitigation include Near-Earth Object diameter and taxonomic classification, which translates to surface composition. Diameters of NEOs are derived from the thermal fluxes measured by WISE, NEOWISE, Spitzer Warm Mission and ground-based telescopes including the IRTF and UKIRT. Diameter and its coupled parameters Albedo and IR beaming parameter (a proxy for thermal inertia and/or surface roughness) are dependent upon the phase angle, which is the Sun-target-observer angle. Orbit geometries of NEOs, however, typically provide for observations at phase angles greater than 20 degrees. At higher phase angles, the observed thermal emission is sampling both the day and night sides of the NEO. We compare thermal models for NEOs that exclude (NEATM) and include (NESTM) night-side emission. We present a case study of NEO 3691 Bede, which is a higher albedo object, X (Ec) or Cgh taxonomy, to highlight the range of H magnitudes for this object (depending on the albedo and phase function slope parameter G), and to examine at different phase angles the taxonomy and thermal model fits for this NEO. Observations of 3691 Bede include our observations with IRTF+SpeX and with the 10 micrometer UKIRT+Michelle instrument, as well as WISE and Spitzer Warm mission data. By examining 3691 Bede as a case study, we highlight the interplay between the derivation of basic physical parameters and observing geometry, and we discuss the uncertainties in H magnitude, taxonomy assignment amongst the X-class (P, M, E), and diameter determinations. Systematic dependencies in the derivation of basic characterization parameters of H-magnitude, diameter, albedo and taxonomy with observing geometry are important to understand. These basic characterization parameters affect the statistical assessments of the NEO population, which in turn, affects the assignment of statistically-assessed basic parameters to discovered but yet-to-be-fully-characterized NEOs.

Albedo and taxonomy↗
DOE OSTI2017

QUANTIFICATION OF FUNCTIONAL IMPACT CLASSIFICATION ON THE CURRENT US NUCLEAR FLEET

Many plants experience multiple failure events each year, and while none of these events result in core damage further consideration for nuclear power plant (NPP) type and its role in an event needs to be considered. Specifically herein initiating events (IE) are considered these are unplanned events that occur while a nuclear plant is in critical operation, and requires that the plant be shut down to achieve a stable state. Breaking IE failure events into groups results in the classification of functional impact (FI) groups. A FI group is a risk-significant event category that could impact the ability of a NPP to remove decay heat from the reactor. For an event to be considered a FI, it must be associated with a manual or automatic reactor trip, irrespective of the order of events. In addition, the plant must be at or above the point of adding heat and the event must happen before or shortly after a reactor trip. One or more FIs may be identified in any individual reactor trip sequence. An example of a FIs is the loss of offsite power (LOOP) from a bird interaction with the power lines. This results in a failure to transfer power to an emergency source, such as a generator, producing a turbine and reactor trip and then a closure of main steam isolation valves (MSIVS). The FI applicable to this reactor trip sequence would be loss of offsite power and closure of at least one MSIV in each main steam line. The results presented are focused on the analysis and evaluation of FI event categories that can be found in IE studies [1], [2] and the Initialing Events Spreadsheet [3] and is a historical review of Licensee.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Markers and Beyond: Categorizing Human Intrusion Situations to be Addressed in Sub-surface Marking Concepts - 16378

Concepts for the use of markers in the post-closure phase of deep geological repositories (DGRs), are often designed to prevent 'inadvertent' human intrusion (HI), while it is common practice not to protect against deliberate intrusion (i.e. 'advertent' HI). In the literature, inadvertent HI prevention studies usually combine future human activity scenarios with the assumption that all memory of the DGR has been lost, and analyze what can be done now to make the possible future intruder 'aware' of the associated risks. In this paper, we show that the classical 'advertent'/'inadvertent' duality classification of HI, as used in the performance assessment context, is not ideal to characterize potential HI situations in the RK and M preservation context. We suggest that the relevant HI categories are expanded and refined to 'un/necessary HI', 'un/intentional HI' and 'un/friendly HI'. Along with refining the taxonomy of HI that way, we refine the assumption relating to the loss of memory of the DGR. Different HI situations are likely to arise in different 'loss-of-memory situations'. We analyze the resulting 5 classes of the proposed HI classification and find that 2 hold the classic 'advertent' and 'inadvertent' HI scenarios while 3 are 'new'. We apply the proposed categories to sub-surface marking concepts from the literature and suggest that, in particular, sub-surface marking concepts would benefit from this new approach to HI and loss-of-memory categorization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗