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At least 559 records · Page 31

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment.

Stamatelatos,Michael↗

Assessment of TLE-Based Orbit Determination and Prediction for Cubesats

The IceCube satellite is a 3U cubesat which was deployed from the International Space Station Nanoracks in May 2017. IceCube uses onboard Global Positioning System (GPS) measurements for both science data processing and orbit determination; additionally, IceCube uses Two Line Element (TLE) sets provided by USSTRATCOM through Space Track for ground station acquisition. Once acquired, GPS measurements are downlinked and can be verified against nearby TLE states.TLE sets are typically used for cubesat acquisition data, however the accuracy of their propagation has a limited lifespan which can cause issues when using an older TLE to attempt spacecraft acquisition. This presentation details the merits of TLE-based orbit determination and prediction and discusses techniques for improving both. These refinements allow for improved cubesat acquisition as well as calibration and on-orbit checkout of the GPS. Using IceCube data as representative of a typical cubesat, TLE states are simulated as tracking data and processed through an Extended Kalman Filter. This filtered solution provides not only an ongoing estimate of spacecraft drag state parameters, but also improves the definitive orbit determination estimation as well as the propagation accuracy and therefore spacecraft acquisition. Next, using the definitive TLE-based solution, raw GPS measurements are introduced into the filter where the combined measurement types allow the GPS measurements to be subsequently calibrated with respect to an unknown timing bias. These analyses provide a more accurate state estimation and prediction of cubesats during early orbit operations. Additionally, these techniques showcase methods for TLE-based state estimation and GPS calibration when other methods of tracking are unavailable.

Smith, Alexander↗

Heat Stress to Jeopardize Crop Production in the US Corn Belt Based on Downscaled CMIP5 Projections

CONTEXT Global food security faces increasing challenges from the changing climate. Changes of agricultural output from some of the most productive regions such as the US Corn Belt can largely affect the world's food market. Developing predictive understanding of the agricultural risk of climate change and potential mitigation strategies is critical for the global food security. OBJECTIVE The objective of this study is to assess the responses of maize and soybean yield to projected climate changes in the Corn Belt, identify the shifting environment stressors on crop yield, and tackle potential climate adaptation strategies. METHODS We drive a process-based model, the Decision Support System for Agrotechnology Transfer, with high-resolution statistically downscaled and bias-corrected historical and future climates from ten CMIP5 models in the MACA-2 database. RESULTS AND CONCLUSIONS The multi-model ensemble mean suggests a 12% decrease of maize yield by mid-century and 40% by late century, with a high degree of model consensus in the direction of changes; for individual models, the projected decrease of maize yield by late century ranges from <5% to over 80%, with the worst crop outcome corresponding to the most sensitive climate models. Soybean yield is projected to increase by midcentury with a high degree of model consensus, but such consensus is lost by late century as some projections shift to significant decreases. Crop yield in the Corn Belt is currently limited by water stress, but is projected to be increasingly limited by heat stress as well after the midcentury. The mounting heat stress will drive the most productive zone for maize to shift from central to northern part of the Corn Belt, but the projected increase in the northern states cannot fully compensate for the decrease in the south, causing the total production to decrease if agricultural practice stays the same. Earlier planting can alleviate only a small fraction of the heat-induced crop loss in a warmer climate. Climate change will (at least partially) offset the yield boost caused by agricultural technology and intensification. SIGNIFICANCE This study advances our predictive understanding of crop yield responses to climate change, and suggests that a multitude of strategies will be needed to address the climate change challenges for the U.S. agriculture.

Crop yield↗

Biospecimen Culling: Temporal RNA Integrity Analysis Across Spaceflight Missions Dating from 1985 to 2011

The Ames Life Science Data Archive (ALSDA) at NASA Ames Research Center is managed by the Space Biosciences Division and has been operational since 1993. The ALSDA is responsible for archiving information and biospecimens collected from life science spaceflight experiments and matching ground control experiments. They are stored in the Ames biobank, which is located in the Biospecimen Storage Facility (BSF). The ALSDA also manages a Biospecimen Sharing Program, performs curation and long-term storage operations, and makes biospecimens available to the scientific community for research purposes via the Life Science Data Archive public website (https:lsda.jsc.nasa.gov). The BSF maintains both fixed and frozen spaceflight and ground tissues, collected from recent and past spaceflight missions. Due to the ever increasing demand for space to preserve current and future flight biospecimens, the ALSDA has initiated the development of a culling plan for biospecimens currently stored in the BSF. Culling enables the ALSDA to assess the quality of archived samples, and supports the development of standardized culling procedures that improve the operational efficiency of the BSF. The culling plan focuses on generating disposition recommendations for samples in the BSF, and currently is based on measuring ribonucleic acid (RNA) integrity number (RIN). The culling process includes (1) sorting and identification of candidate samples for RIN analysis, (2) completion of RIN analysis on select samples, and (3) development of disposition recommendations for specimens based on the RIN values. Furthermore, our approach allows for unique scientific opportunities, including development of a RIN-based methodology for culling, and temporal assessment of the quality of the tissues that have been stored in BSF since the 1980s. Results of this work will also support NASA open science initiatives.

biospecimen↗

A research agenda for the science of actionable knowledge: Drawing from a review of the most misguided to the most enlightened claims in the science-policy interface literature

Linking science with action affords a prime opportunity to leverage greater societal impact from research and increase the use of evidence in decision-making. Success in these areas depends critically upon processes of producing and mobilizing knowledge, as well as supporting and making decisions. For decades, scholars have idealized and described these social processes in different ways, resulting in numerous assumptions that now variously guide engagements at the interface of science and society. We systematically catalog these assumptions based on prior research on the science-policy interface, and further distill them into a set of 26 claims. We then elicit expert perspectives (n = 16) about these claims to assess the extent to which they are accurate or merit further examination. Out of this process, we construct a research agenda to motivate future scientific research on actionable knowledge, prioritizing areas that experts identified as critical gaps in understanding of the science-society interface. The resulting agenda focuses on how to define success, support intermediaries, build trust, and evaluate the importance of consensus and its alternatives – all in the diverse contexts of science-society-decision-making interactions. We further raise questions about the centrality of knowledge in these interactions, discussing how a governance lens might be generative of efforts to support more equitable processes and outcomes. We offer these suggestions with hopes of furthering the science of actionable knowledge as a transdisciplinary area of inquiry.

99 GENERAL AND MISCELLANEOUS↗

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗

Assessing atomically thin delta-doping of silicon using mid-infrared ellipsometry

Hydrogen lithography has been used to template phosphine-based surface chemistry to fabricate atomic-scale devices, a process we abbreviate as atomic precision advanced manufacturing (APAM). In this work, we use mid-infrared variable angle spectroscopic ellipsometry (IR-VASE) to characterize single-nanometer thickness phosphorus dopant layers (δ-layers) in silicon made using APAM compatible processes. A large Drude response is directly attributable to theδ-layer and can be used for nondestructive monitoring of the condition of the APAM layer when integrating additional processing steps. Furthermore, the carrier density and mobility extracted from our room temperature IR-VASE measurements are consistent with cryogenic magneto-transport measurements, showing that APAM δ-layers function at room temperature. Finally, the permittivity extracted from these measurements shows that the doping in the APAM δ-layers is so large that their low-frequency in-plane response is reminiscent of a silicide. However, there is no indication of a plasma resonance, likely due to reduced dimensionality and/or low scattering lifetime.

2D material↗

Aircraft Loss of Control: Problem Analysis for the Development and Validation of Technology Solutions

Aircraft loss of control (LOC) is a leading cause of fatal accidents across all transport airplane and operational classes. LOC can result from a wide spectrum of precursors (or hazards), often occurring in combination. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of conditions and uncertainties, including multiple hazards, and the validation process must provide a means of assessing system effectiveness and coverage of these hazards. This paper provides a detailed description of a methodology for analyzing LOC as a dynamics and control problem for the purpose of developing effective technology solutions. The paper includes a definition of LOC based on several recent publications, a detailed description of a refined LOC accident analysis process that is illustrated via selected example cases, and a description of planned follow-on activities for identifying future potential LOC risks and the development of LOC test scenarios. Some preliminary considerations for LOC of Unmanned Aircraft Systems (UAS) and for their safe integration into the National Airspace System (NAS) are also discussed.

Belcastro, Christine M.↗

Launch commit criteria performance trending analysis, phase 1, revision A. SRM and QA mission services

An assessment of quantitative methods and measures for measuring launch commit criteria (LCC) performance measurement trends is made. A statistical performance trending analysis pilot study was processed and compared to STS-26 mission data. This study used four selected shuttle measurement types (solid rocket booster, external tank, space shuttle main engine, and range safety switch safe and arm device) from the five missions prior to mission 51-L. After obtaining raw data coordinates, each set of measurements was processed to obtain statistical confidence bounds and mean data profiles for each of the selected measurement types. STS-26 measurements were compared to the statistical data base profiles to verify the statistical capability of assessing occurrences of data trend anomalies and abnormal time-varying operational conditions associated with data amplitude and phase shifts.

Source record↗

Mathematical Programming Models for Shale Oil & Gas Development: A Review and Perspective

Here, in this paper, we provide a comprehensive review of mathematical programming models for shale oil & gas development, and we offer a perspective on outstanding research opportunities. We distinguish contributions in five major topic areas, namely: (1) development planning, (2) water management, (3) production optimization, (4) supplies, gathering & processing, and (5) life cycle analysis & sustainability. We highlight how various types of mathematical programming models (i.e., linear programs, nonlinear programs, mixed-integer linear programs, mixed-integer nonlinear programs) have been proposed primarily by the Process Systems Engineering community to address the respective decision-making problems, and we highlight instances of successful deployment in industry. Finally, based on a critical assessment of the existing body of work, we identify opportunities for future research across the major topic areas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Secure boot, trusted boot and remote attestation for ARM TrustZone-based IoT Nodes

With the extensive application of IoT techniques, IoT devices have become ubiquitous in daily lives. Meanwhile, attacks against IoT devices have emerged to compromise IoT devices by tampering with system pre-installed programs or injecting new malware. To mitigate these attacks, integrity enforcement of IoT systems has been proposed. The integrity of an IoT device system includes load-time integrity and runtime integrity. In this paper, we design an IoT system based on ARM TrustZone to enforce the system integrity. First, we establish the root of trust and propose a hybrid booting approach consisting of both secure boot and trusted boot to enforce the system load-time integrity. Second, we investigate a paging-based process integrity measurement method to measure the NW processes and conduct remote attestation based on the measurement results ensuring the NW runtime process integrity. We implement an IoT prototype system on a NXP i.MX6Q SABRE SD development board to assess its feasibility. Finally, real-world experiment results demonstrate that our prototype introduces negligible performance overhead to the original system.

97 MATHEMATICS AND COMPUTING↗

Multi‐Decadal Dynamics of Wetland Methane Emissions Revealed by Knowledge‐Guided Machine Learning

Measurement of methane fluxes (FCH 4 ) from natural systems, such as wetlands, has lagged far behind carbon dioxide fluxes. Short and fragmented wetland FCH 4 data limit our ability to assess its long-term dynamics and potential climate feedbacks. Extrapolating short-term FCH 4 records to recent decades remains challenging for both process-based models and data-driven machine learning (ML) approaches. Here, we develop a knowledge-guided ML framework that integrates eddy covariance (EC) FCH 4 observations, field warming experiments, and biogeochemical knowledge to reconstruct the long-term FCH 4 budgets and trends. Focusing on the 11 longest EC monitoring sites in the AmeriFlux network, we found considerable variability in multi-decadal trends of wetland FCH 4 , with increases up to 14% per decade from 2000 to 2024. We also found that the strength of these increasing trends declines from high to low latitudes, highlighting the vulnerability of northern wetlands. This work presents novel and robust reconstructions of long-term wetland FCH 4 , offering critical benchmark datasets for bottom-up ecosystem models and advancing fundamental understanding of wetland biogeochemistry.

AmeriFlux site↗

A Novel use of Direct Simulation Monte-Carlo to Model Dynamics of COVID-19 Pandemic Spread

In this report, we evaluate a novel method for modeling the spread of COVID-19 pandemic. In this new approach we leverage methods and algorithms developed for fully-kinetic plasma physics simulations using Particle-In-Cell (PIC) Direct Simulation Monte-Carlo (DSMC) models. This approach then leverages Sandia-unique simulation capabilities, and High-Performance Computer (HPC) resources and expertise in particle-particle interactions using stochastic processes. Our hypothesis is that this approach would provide a more efficient platform with assumptions based on physical data that would then enable the user to assess the impact of mitigation strategies and forecast different phases of infection. This work addresses key scientific questions related to the assumptions this new approach must make to model the interactions of people using algorithms typically used for modeling particle interactions in physics codes (kinetic plasma, gas dynamics). The model developed uses rational/physical inputs while also providing critical insight; the results could serve as inputs to, or alternatives for, existing models. The model work presented was developed over a four-week time frame, thus far showing promising results and many ways in which this model/approach could be improved. This work is aimed at providing a proof-of-concept for this new pandemic modeling approach, which could have an immediate impact on the COVID-19 pandemic modeling, while laying a basis to model future pandemic scenarios in a manner that is timely and efficient. Additionally, this new approach provides new visualization tools to help epidemiologists comprehend and articulate the spread of this and other pandemics as well as a more general tool to determine key parameters needed in order to better predict pandemic modeling in the future. In the report we describe our model for pandemic modeling, apply this model to COVID-19 data for New York City (NYC), assess model sensitivities to different inputs and parameters and , finally, propagate the model forward under different conditions to assess the effects of mitigation and associated timing. Finally, our approach will help understand the role of asymptomatic cases, and could be extended to elucidate the role of recovered individuals in the second round of the infection, which is currently being ignored.

59 BASIC BIOLOGICAL SCIENCES↗

Development of Advanced Solid Sorbents for Direct Air Capture (Final Report)

RTI International is partnering with Creare, and Mohammed VI Polytechnic University (UM6P) successfully met all major technical objectives as outlined below: Identified one MOF adsorbent for DAC; Identified one amine-P-dendrimer adsorbent for DAC; Performed Computational fluid dynamics simulations of the MOF and amine-P-dendrimer adsorbents and validate them with experimental data; Selected one adsorbent for DAC; Demonstrated the scale-up of selected candidate and perform cost review; Performed a preliminary process design; Performed technology maturation plan and environmental health and safety risk assessment. After a thorough comparison between both adsorbents (MOFs and P-dendrimers) and based on their technical merits, the amine-based P-dendrimers sorbent was selected as the best performing adsorbent for DAC. In fact, the amine-based P-dendrimers have demonstrated superior performance over the MOF sorbents under relevant DAC conditions. The selected sorbent has been used in subsequent projects (DE-FE0032099 and DE-AR0001412) and has demonstrated similar superior performance with no loss of activity after long-term sorption-desorption testing (e.g., 100 cycles). This sorbent has demonstrated a very high and stable sorption performance, excellent kinetics, and fast regeneration at 80 °C, specifically at 75% relative humidity (RH). The incorporation of RTI’s high-performance, high-durability amine-based P-dendrimers sorbent and Creare’s hybrid additive manufacturing (H-AM) technology that will produce high performance, compact heat and mass exchange structures at low cost using methods, will lead to a contactor that is optimized for wind-driven operation. This contactor technology provides a technically viable pathway for reducing the cost of DAC to <$100/tonne of CO 2 .

36 MATERIALS SCIENCE↗

Application of Electrochemical Methods to Molten Salt Reactors: Draft TLR Documenting Assessment of Electrochemical Monitoring

Nuclear Regulatory Commission (NRC) is developing the regulatory framework and technical expertise to support regulatory review of advanced non-water reactors, including molten salt reactors (MSRs). In an MSR, it is essential that the salt chemistry be maintained in a desired range in terms of redox potential for reliable operation and mitigation of corrosion to structural materials in the reactor, particularly the reactor vessel and heater exchanger. Measuring the redox potential of the salt in the reactor would also allow for the material lifetimes to be predicted more accurately, and chemical issues to be diagnosed more quickly. In addition to the chemical composition analysis by ICP-MS, electrochemical methods including potentiometry and linear wave scanning (LSC) were also used in molten salt reactor experiment (MSRE) for redox potential monitoring purposes. Electrochemcial methods offers unique advantages such as quick turnaround in results and unique capability of in-line monitoring of redox potential, and are considered popular electroanalytical techniques that can be used to monitor redox potentials and salt chemistry including impurities. The last two decades have seen significant advances in science and engineering of electrochemical methods for potential application to molten salts. The primary goal of this report is to assist NRC in understanding the monitoring of the salt chemistry by electrochemical methods and provide NRC reviewers with necessary information and tools to support regulatory review of MSR designs. This reports consists of two majors parts—Part 1, chemical potential of molten salts and effects by fission process in MSR; Part 2, assessment of electrochemical methods for application to MSRs. The TRLs of the typical relevant electrochemical methods for molten salts were evaluated based on the DOE TRL guidelines and upon a review of the current status of the electrochemical methods for two typical salt systems, fluoride and chloride, for MSRs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗

David Florida Laboratory Thermal Vacuum Data Processing System

During 1991, the Space Simulation Facility conducted a survey to assess the requirements and analyze the merits for purchasing a new thermal vacuum data processing system for its facilities. A new, integrated, cost effective PC-based system was purchased which uses commercial off-the-shelf software for operation and control. This system can be easily reconfigured and allows its users to access a local area network. In addition, it provides superior performance compared to that of the former system which used an outdated mini-computer and peripheral hardware. This paper provides essential background on the old data processing system's features, capabilities, and the performance criteria that drove the genesis of its successor. This paper concludes with a detailed discussion of the thermal vacuum data processing system's components, features, and its important role in supporting our space-simulation environment and our capabilities for spacecraft testing. The new system was tested during the ANIK E spacecraft test, and was fully operational in November 1991.

Choueiry, Elie↗