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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Root Cause Correlation Analysis of Software Failures via Orthogonal Defect Classification and Natural Language Processing

Systems theoretic process analysis (STPA) is becoming an increasingly popular technique to assess how complex digital software systems can fail. Rather than defining failures by their observable failure events, which may be sparse especially for safety rated nuclear digital instrumentation and control systems (DI&C), failures are defined as postulated unsafe actions under specific contextual conditions. This permits a top-down analysis of system hazards and identifies whether imposed constraints and requirements can sufficiently address undesirable hazards. However, STPA is a qualitative approach at identifying inadequacies in the development process and cannot currently be used to quantify unsafe action likelihoods for probabilistic risk assessment. Therefore, in this work, we examine the root causes of software failure and explore whether a consistent correlation can be linked to specific unsafe action classes. We implement Lbl2Vec, an unsupervised document classification and retrieval algorithm, on a database of 4,096 software defect reports acquired from various open-source software systems. By analyzing sentence structure, embedded labels, and word vectors, we show that certain defect types positively correlate to specific unsafe action classes over others. The correlations developed can be used to estimate the failure probability of safety intended DI&C systems which provides a licensing basis for nuclear plant modernization efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1975

Present and potential land use mapping in Mexico

The Mexican Water Plan (MWP) conducted studies of present and potential land use in Mexico using LANDSAT-1 satellite imagery. Present land use studies were carried out all over the country (197 million hectares); nine soil uses were mapped according to the first classification level recommended by the U.S. Geological Survey. Also 6.3 million hectares of land with advanced erosion were detected. Work was executed at a rate of 8 million hectares per month; reliability was 90% and the cost of only 0.1 cents/hectare. The potential land use study was performed in 45 million hectares at a rate of 4 million hectares per month and at a cost of 0.33 cents/hectare. Soil units according to FAO classification were delineated scale 1:1 million; interpretative maps were also prepared dealing with potential agricultural productivity carrying capacity for cattle, water, erosion risk, and slope ranges.

Garduno, H.↗
NASA NTRS1996

Advanced Refrigerator/Freezer Technology Development. Technology Assessment

The NASA Lewis Research Center, through contract with Oceaneering Space Systems, is engaged in a project to develop advanced refrigerator/freezer (R/F) technologies for future Life and Biomedical Sciences space flight missions. The first phase of this project, a technology assessment, has been completed to identify the advanced R/F technologies needed and best suited to meet the requirements for the five R/F classifications specified by Life and Biomedical Science researchers. Additional objectives of the technology assessment were to rank those technologies based on benefit and risk, and to recommend technology development activities that can be accomplished within this project. This report presents the basis, the methodology, and results of the R/F technology assessment, along with technology development recommendations.

Gaseor, Thomas↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
NASA NTRS2006

An Experimental Global Monitoring System for Rainfall-triggered Landslides using Satellite Remote Sensing Information

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.

Hong, Yang↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
NASA NTRS2011

GLM Proxy Data Generation: Methods for Stroke/Pulse Level Inter-Comparison of Ground-Based Lightning Reference Networks

In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala s Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

Cummins, Kenneth L.↗
NASA NTRS2000

A Reliable Earth Return System for Safe Recovery of Mars Samples

The objective of a Mars sample return mission is to bring selected Mars surface materials to Earth. Numerous approaches for the Earth-return segment have been analyzed including propulsive or aerocapture return to low-Earth orbit followed by Space Shuttle rendezvous and direct entry. Of these approaches, ballistic entry of a small capsule terminating in a ground landing has been shown to be the lowest risk strategy. Over the past two years, significant work has been performed towards development of a robust direct entry vehicle for Mars sample return. In June 1999, the NASA Planetary Protection Officer provided initial guidance to the former Mars Sample Return Project. The sample return phase of the mission was assigned a restricted Earth return planetary protection classification. The draft mission requirement states that the total mean probability of release of unsterilized Mars material into the Earth;s biosphere must be less than 1.0E-06 (1 in a million). This strict requirement drives the approach and design of the Earth return system. To meet this requirement, selection of the Earth return strategy and development of the Earth return system must be guided by risk, not performance, based decisions. An initial Probabilistic Risk Assessment (PRA) was performed to address the direct entry Earth return system containment assurance reliability and to identify high-risk elements of this system. The results of this PRA identified risk elements that include thermal protection system performance during entry, spin-eject orientation and aerodynamic stability during entry, structural integrity under atmospheric deceleration and impact loads, and tracking/recovery of this system. This initial probabilistic risk quantification demonstrates that, with the proper development program, a prototypical direct entry design can satisfy the containment assurance reliability requirement. Through the current Mars Sample Return Advanced Technology Development effort, an extensive design, analysis, and test program is presently proceeding with the aim of reducing the containment assurance risk of this system. This technology development effort, guided by a continuing PRA, focuses on key risk areas of a direct entry Earth return system including: the thermal protection system, impact dynamics, structural performance, aerodynamic stability, and ground recovery. This development program will culminate in a system validation flight test, 1-2 years prior to launch of the flight system. This flight test would include the launch, entry, and recovery of a full-scale Earth return system, as a scientific validation of the key risk elements to verify nominal design performance. The results of the initial PRA suggested several dominant failure sequences that can be validated in a flight test. These include: demonstrating the thermal protection system reliability and performance during entry, demonstrating the spin-eject orientation and aero-dynamic stability during entry, demonstrating the structural integrity under atmospheric deceleration and impact loads, and demonstrating tracking and recovery of the Earth return system. This single test will directly address over 50% of the total containment assurance risk elements. This presentation will begin by presenting the relative risk of various Earth return strategies. The results of the initial probabilistic risk assessment will be presented followed by a discussion of the development accomplishments and plans for demonstration of a highly reliable direct entry Earth return system.

Braun, R.↗
NASA NTRSDate not supplied

Developing Approaches for Certification of Uninspectable Fracture Critical AM Components

Introduction - Human-rated spaceflight hardware is subject to the Fracture Control requirements in NASA-STD5019, which are intended to control the risk of component failure due to undetected cracks or defects. - All hardware in this mission class is classified for Fracture Control – Exempt, Non-fracture Critical, or Fracture Critical - Fracture Critical: A classification that identifies a part where failure due to the presence of a crack is a catastrophic hazard ➔ Pressure vessels, engine components, primary structure, etc. - Axiom 1: Material and manufacturing process produce structures and components with cracks or defects. - Axiom 2: The presence of a crack or defect of sufficient size reduces the strength and life of the structure. - Damage Tolerance: demonstration that a component can survive the service life (with a safety factor) in the presence of an undetected flaw or damage. - Components are manufactured from aerospace quality materials using controlled processes. - Components are inspected for damage (cracks, flaws, defects, etc.). - Components are shown by test or analysis to be tolerant to undetected flaws.

Will Tilson↗
NASA NTRS2016

A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice Simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

model parameterisation↗
NASA NTRS2024

Synthesis Report of the IPCC Sixth Assessment Report (AR6)

This Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6) summarises the state of knowledge of climate change, its widespread impacts and risks, and climate change mitigation and adaptation, based on the peer-reviewed scientific, technical and socio-economic literature since the publication of the IPCC’s Fifth Assessment Report (AR5) in 2014. he assessment is undertaken within the context of the evolving international landscape, in particular, developments in the UN Framework Convention on Climate Change (UNFCCC) process, including the outcomes of the Kyoto Protocol and the adoption of the Paris Agreement. It reflects the increasing diversity of those involved in climate action. This report integrates the main findings of the AR6 Working Group reports and the three AR6 Special Reports. It recognizes the interdependence of climate, ecosystems and biodiversity, and human societies; the value of diverse forms of knowledge; and the close linkages between climate change adaptation, mitigation, ecosystem health, human well-being and sustainable development. Building on multiple analytical frameworks, including those from the physical and social sciences, this report identifies opportunities for transformative action which are effective, feasible, just and equitable using concepts of systems transitions and resilient development pathways. Different regional classification schemes4 are used for physical, social and economic aspects, reflecting the underlying literature.

climate impacts↗
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↗
DOE OSTI2025

Risk of longer-term endocrine and metabolic conditions in the Deepwater Horizon Oil Spill Coast Guard cohort study – five years of follow-up

Abstract Introduction Long-term endocrine and metabolic health risks associated with oil spill cleanup exposures are largely unknown, despite the endocrine-disrupting potential of crude oil and oil dispersant constituents. We aimed to investigate risks of longer-term endocrine and metabolic conditions among U.S. Coast Guard (USCG) responders to the Deepwater Horizon (DWH) oil spill. Methods Our study population included all active duty DWH Oil Spill Coast Guard Cohort members ( N = 45,224). Self-reported spill exposures were ascertained from post-deployment surveys. Incident endocrine and metabolic outcomes were defined using International Classification of Diseases (9th Revision) diagnostic codes from military health encounter records up to 5.5 years post-DWH. Using Cox proportional hazards regression, we estimated adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for various incident endocrine and metabolic diagnoses (2010–2015, and separately during 2010–2012 and 2013–2015). Results The mean baseline age was 30 years (~ 77% white, ~ 86% male). Compared to non-responders ( n = 39,260), spill responders ( n = 5,964) had elevated risks for simple and unspecified goiter (aHR = 2.09, 95% CI: 1.29–3.38) and disorders of lipid metabolism (aHR = 1.09, 95% CI: 1.00–1.18), including its subcategory other and unspecified hyperlipidemia (aHR = 1.10, 95% CI: 1.01–1.21). The dysmetabolic syndrome X risk was elevated only during 2010–2012 (aHR = 2.07, 95% CI: 1.22–3.51). Responders reporting ever ( n = 1,068) vs. never ( n = 2,424) crude oil inhalation exposure had elevated risks for disorders of lipid metabolism (aHR = 1.24, 95% CI: 1.00–1.53), including its subcategory pure hypercholesterolemia (aHR = 1.71, 95% CI: 1.08–2.72), the overweight, obesity and other hyperalimentation subcategory of unspecified obesity (aHR = 1.52, 95% CI: 1.09–2.13), and abnormal weight gain (aHR = 2.60, 95% CI: 1.04–6.55). Risk estimates for endocrine/metabolic conditions were generally stronger among responders reporting exposure to both crude oil and dispersants (vs. neither) than among responders reporting only oil exposure (vs. neither). Conclusion In this large cohort of active duty USCG responders to the DWH disaster, oil spill cleanup exposures were associated with elevated risks for longer-term endocrine and metabolic conditions.

Denic-Roberts, Hristina↗
NASA NTRS2009

Developing New Coastal Forest Restoration Products Based on Landsat, ASTER, and MODIS Data

This paper discusses an ongoing effort to develop new geospatial information products for aiding coastal forest restoration and conservation efforts in coastal Louisiana and Mississippi. This project employs Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data in conjunction with airborne elevation data to compute coastal forest cover type maps and change detection products. Improved forest mapping products are needed to aid coastal forest restoration and management efforts of State and Federal agencies in the Northern Gulf of Mexico (NGOM) region. In particular, such products may aid coastal forest land acquisition and conservation easement procurements. This region's forests are often disturbed and subjected to multiple biotic and abiotic threats, including subsidence, salt water intrusion, hurricanes, sea-level rise, insect-induced defoliation and mortality, altered hydrology, wildfire, and conversion to non-forest land use. In some cases, such forest disturbance has led to forest loss or loss of regeneration capacity. In response, a case study was conducted to assess and demonstrate the potential of satellite remote sensing products for improving forest type maps and for assessing forest change over the last 25 years. Change detection products are needed for assessing risks for specific priority coastal forest types, such as live oak and baldcypress-dominated forest. Preliminary results indicate Landsat time series data are capable of generating the needed forest type and change detection products. Useful classifications were obtained using 2 strategies: 1) general forest classification based on use of 3 seasons of Landsat data from the same year; and 2) classification of specific forest types of concern using a single date of Landsat data in which a given targeted type is spectrally distinct compared to adjacent forested cover. When available, ASTER data was useful as a complement to Landsat data. Elevation data helped to define areas in which targeted forest types occur, such as live oak forests on natural levees. MODIS Normalized Difference Vegetation Index time series data aided visual assessments of coastal forest damage and recovery from hurricanes. Landsat change detection products enabled change to be identified at the stand level and at 10- year intervals with the earliest date preceding available change detection products from the National Oceanic and Atmospheric Administration and from the U.S. Geological Survey. Additional work is being done in collaboration with State and Federal agency partners in a follow-on NASA ROSES project to refine and validate these new, promising products. The products from the ROSES project will be available for aiding NGOM coastal forest restoration and conservation.

Spruce, Joseph P.↗
NASA NTRS2024

San Joaquin Valley Health & Air Quality II: Assessing Urban Heat Island Distribution and its Intersections with Air Quality to Understand Converging Vulnerabilities

The city of Stockton, California, located within the San Joaquin Valley (SJV), is a major hub for agricultural production and has endured the continuous threat to community health from nitrogen dioxide (NO 2 ) and increasing temperatures. The convergence of these issues occurs within historically segregated communities that are disproportionately facing health risks related to heat and air quality. Little Manila Rising (LMR), a social and environmental justice (EJ) advocacy non-profit, partnered with NASA DEVELOP for a second term project to evaluate county wide urban heat islands, sociodemographic vulnerability, landcover classification, and the convergence of these variables. We utilized Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI) data to produce a land surface temperature (LST) and Normalized Difference Vegetation Index map. They added Centers for Disease Control (CDC) socioeconomic data from 2020 to identify which communities in Stockton were more susceptible to these environmental factors. Additionally, we used NAIP imagery to create a landcover map differentiating developed infrastructure from tree canopy cover. We discovered that south Stockton, where LMR resides, had the worst convergence of heat, air pollution, low canopy coverage and sociodemographic vulnerability compared to northern and rural parts of the city. This was further substantiated by statistical analysis showing a strong positive relationship between areas of high LST and low vegetation. The results provided LMR with compelling evidence to use in their EJ advocacy, and in their efforts to inform state officials of the discriminatory issues they face.

Urban Heat Islands↗
NASA NTRS2002

Interpretation and classification of microvolt T wave alternans tests

Measurement of microvolt-level T wave alternans (TWA) during routine exercise stress testing now is possible as a result of sophisticated noise reduction techniques and analytic methods that have become commercially available. Even though this technology is new, the available data suggest that microvolt TWA is a potent predictor of arrhythmia risk in diverse disease states. As this technology becomes more widely available, physicians will be called upon to interpret microvolt TWA tracings. This review seeks to establish uniform standards for the clinical interpretation of microvolt TWA tracings.

NASA Discipline Cardiopulmonary↗
NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗
NASA NTRS2022

Dusty Environment Classification and Testing: Dust Mitigation Slide Wipes & Tapes

During the Apollo missions many mechanisms, equipment, and surfaces were contaminated by lunar dust and often negatively affected. The methods used to clear these devices of dust proved ineffective, which led to premature failure in some cases. Systems being designed for upcoming missions to the Moon will need to address the performance degradation risk of dust contamination. Surfaces at risk include thermal control surfaces, solar cells, camera and sensor optics, seals, metal joints and tools, and space suit assemblies. To improve state of the art dust mitigation approaches, the dust mitigation team at NASA Glenn Research Center (GRC) explored the effects imposed on substrates during cleaning activities in the presence of lunar dust. The Uniform Dust Deposition System (UDDS) was used to conduct a study of various cleaning techniques on different substrates. Test substrates and cleaning media were selected to represent a wide range of structural and functional materials. Substrates included orthofabric, thermal radiator coating, aluminum, quartz glass, silicone rubber, aluminized Mylar, and FEP. Cleaning materials included cloth wipes, tapes, a brush, and a pliable cleaner concept. Cleaning material / substrate pairs were rated on cleaning efficiency, substrate damage, cleaning material longevity, and ease of use. The best performing cleaning method varied based on application.

lunar simulant↗