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At least 775 records · Page 43

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NASA NTRS2008

Planning for the Future, a Look from Apollo to the Present

Future missions out of low Earth orbit, returning to the moon and Mars, will be some of the most complicated endeavors ever attempted by mankind. It will require the wealth of nations and the dedicated efforts of thousand of individuals working in a concerted effort to take man to the moon, Mars and beyond. These missions will require new equipment and new approaches to optimize our limited resources and time in space. This daily planning and optimization which currently is being performed by scores of people in MCC Houston and around the world will need to adapt to the challenges faced far from Earth. By studying the processes, methodologies, and tools employed from Apollo, Skylab, Shuttle, ISS, and other programs such as NEEMO, we can learn from the past to plan for the future. This paper will explore the planning process used from Apollo onward and will discuss their relevancy in future applications.

Segrera, David↗

NASA NTRS2011

A Hyperbolic Ontology Visualization Tool for Model Application Programming Interface Documentation

Spacecraft modeling, a critically important portion in validating planned spacecraft activities, is currently carried out using a time consuming method of mission to mission model implementations and integration. A current project in early development, Integrated Spacecraft Analysis (ISCA), aims to remedy this hindrance by providing reusable architectures and reducing time spent integrating models with planning and sequencing tools. The principle objective of this internship was to develop a user interface for an experimental ontology-based structure visualization of navigation and attitude control system modeling software. To satisfy this, a number of tree and graph visualization tools were researched and a Java based hyperbolic graph viewer was selected for experimental adaptation. Early results show promise in the ability to organize and display large amounts of spacecraft model documentation efficiently and effectively through a web browser. This viewer serves as a conceptual implementation for future development but trials with both ISCA developers and end users should be performed to truly evaluate the effectiveness of continued development of such visualizations.

mission operations system (MOS)↗

NASA NTRS2016

Applied Time Domain Stability Margin Assessment for Nonlinear Time-Varying Systems

The baseline stability margins for NASA's Space Launch System (SLS) launch vehicle were generated via the classical approach of linearizing the system equations of motion and determining the gain and phase margins from the resulting frequency domain model. To improve the fidelity of the classical methods, the linear frequency domain approach can be extended by replacing static, memoryless nonlinearities with describing functions. This technique, however, does not address the time varying nature of the dynamics of a launch vehicle in flight. An alternative technique for the evaluation of the stability of the nonlinear launch vehicle dynamics along its trajectory is to incrementally adjust the gain and/or time delay in the time domain simulation until the system exhibits unstable behavior. This technique has the added benefit of providing a direct comparison between the time domain and frequency domain tools in support of simulation validation. This technique was implemented by using the Stability Aerospace Vehicle Analysis Tool (SAVANT) computer simulation to evaluate the stability of the SLS system with the Adaptive Augmenting Control (AAC) active and inactive along its ascent trajectory. The gains for which the vehicle maintains apparent time-domain stability defines the gain margins, and the time delay similarly defines the phase margin. This method of extracting the control stability margins from the time-domain simulation is relatively straightforward and the resultant margins can be compared to the linearized system results. The sections herein describe the techniques employed to extract the time-domain margins, compare the results between these nonlinear and the linear methods, and provide explanations for observed discrepancies. The SLS ascent trajectory was simulated with SAVANT and the classical linear stability margins were evaluated at one second intervals. The linear analysis was performed with the AAC algorithm disabled to attain baseline stability margins. At each time point, the system was linearized about the current operating point using Simulink's built-in solver. Each linearized system in time was evaluated for its rigid-body gain margin (high frequency gain margin), rigid-body phase margin, and aero gain margin (low frequency gain margin) for each control axis. Using the stability margins derived from the baseline linearization approach, the time domain derived stability margins were determined by executing time domain simulations in which axis-specific incremental gain and phase adjustments were made to the nominal system about the expected neutral stability point at specific flight times. The baseline stability margin time histories were used to shift the system gain to various values around the zero margin point such that a precise amount of expected gain margin was maintained throughout flight. When assessing the gain margins, the gain was applied starting at the time point under consideration, thereafter following the variation in the margin found in the linear analysis. When assessing the rigid-body phase margin, a constant time delay was applied to the system starting at the time point under consideration. If the baseline stability margins were correctly determined via the linear analysis, the time domain simulation results should contain unstable behavior at certain gain and phase values. Examples will be shown from repeated simulations with variable added gain and phase lag. Faithfulness of margins calculated from the linear analysis to the nonlinear system will be demonstrated.

Kiefer, J. M.↗

NASA NTRS2019

Deep Learning and Uncertainty Quantification for Climate Resilience

Modeling and monitoring of earth’s processes through physical models and satellite observations at high resolutions is crucial for ensuring society’s ability to adapt to climate change. Deep learning (DL) has been shown to be a valuable tool for generating high resolution data, emulating physical models, and detecting weather patterns which can then be used to inform stakeholders and decision makers. However, both the data and model parameters contain substantial uncertainties that may alter users’ decisions. In this work we present two DL applications on high-resolution climate and satellite datasets using Bayesian neural networks to generate well calibrated uncertainty estimates.

Vandal, Thomas↗

NASA NTRSDate not supplied

Results from NASA Agile Teams Study

In order to meet the demands of the current global economy, a study of agile teams at NASA was conducted by the NASA Engineering and Safety Center (NESC) to explore how the Agency can become more adaptable and flexible due to several influences, including the maturity and availability of digital tools and NASA’s increased reliance on commercial providers. NESC Technical Discipline Teams (TDTs) perform annual studies to understand the needs of their stakeholders, establish a state of the discipline, and inform strategic planning efforts. As part of the 2022 interview series, the Systems Engineering TDT utilized the dissertation work of Dr. Jackelynne Silva-Martinez that focused on NASA agile teams. The Systems Engineering TDT partnered with the Software and Human Factors TDTs on the formulation of interview questions and subsequent solicitation of customer inputs, which helped attract diverse agile teams across the Agency. 34 participants were interviewed, including civil servants and contractors, with open-ended questions related to their teams’ adoption and transition to agile. Participants were also questioned about their perceptions of the implementation of agile approaches across the Agency. Results showed a consistent framing of agile as incremental knowledge growth, a way of showing progress, and as the incorporation of frequent customer feedback. However, participants emphasized a lack of a common understanding of agile across the Agency, which sometimes caused managers and engineers to be reluctant to accept and support them. Results also showed the most successful agile teams had formal training with over five years of agile experience, used retrospectives, and tailored their agile processes for their specific situation and needs. This paper provides details of the interview responses gathered in the study and suggests actions for NASA to become more agile.

NASA↗

DOE OSTI2021

Cybersecurity for Electric Vehicle Fast-Charging Infrastructure: Preprint

The integration of electric vehicles (EVs) into electric grid operations can potentially leave the grid vulnerable to cyberattacks from both legacy and new equipment and protocols, including extreme fast-charging infrastructure. This paper introduces a co-simulation platform to perform cyber vulnerability analysis of EV charging infrastructure and its dependencies on communications and control systems. Grid impact scenarios through linkages to power system simulation tools such as OpenDSS and vehicle infrastructure-specific attack paths are discussed. An adaptive platform that assists with predicting and solving evolving cybersecurity challenges is demonstrated with a cyber-energy emulation that accelerates the analysis of cyberattacks and system behavior.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Cybersecurity for Fast Charging EV Infrastructure

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2026

Automated Generation of Graph-based Cyber Threat Intel

With the advancement of AI technology and tools, specifically in the cybersecurity domain, both cyber defenders and threat actors are continuously adapting the use of these capabilities to expedite their operations. With this phenomenon, threat intelligence that is up to date, refreshable, and has relevant context to a specific threat becomes more and more important as it enables cybersecurity professionals to gain insight into relevant data and relationships to guide their operations. This project enables users to frequently aggregate threat intelligence from various sources, such as vendor vulnerability advisories affecting critical infrastructure, malware reports, and adversary writeups into a centralized, standardized database. The project utilizes the Structured Threat Intelligence eXpression (STIX) for a standardized, shareable threat intelligence data format and Neo4j as a graph database solution to store STIX nodes and relationships. Initial results of the project include datasets of over 8,000 nodes and 20,000 relationships extracted from over 500 data sources that have been released within the past month.

Threat Intelligence↗

NASA NTRSDate not supplied

Validation of Image-Based Neural Network Controllersthrough Adaptive Stress Testing

Neural networks have become state-of-the-art for computer vision problems because of their ability to efficiently model complex functions from large amounts of data. While neural networks can be shown to perform well empirically fora variety of tasks, their performance is difficult to guarantee.Neural network verification tools have been developed that can certify robustness with respect to a given input image; however,for neural network systems used in closed-loop controllers,robustness with respect to individual images does not address multi-step properties of the neural network controller and itsenvironment. Furthermore, neural network systems interacting in the physical world and using natural images are operating in a black-box environment, making formal verification in-tractable. This work combines the adaptive stress testing (AST)framework with neural network verification tools to search for the most likely sequence of image disturbances that cause the neural network controlled system to reach a failure. Anautonomous aircraft taxi application is presented, and results show that the AST method finds failures with more likely image disturbances than baseline methods. Further analysis of AST results revealed an explainable cause of the failure, giving insight into the problematic scenarios that should be addressed.

Adaptive Stress Testing, Marabou, Deep Neural Netw↗

DOE OSTI2021

Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass

Long-term climate change and periodic environmental extremes threaten food and fuel security and global crop productivity. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation—knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass ( Panicum virgatum ). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate–gene–biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene–trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.

09 BIOMASS FUELS↗

DOE OSTI2022

Optimization of Canister Loading Patterns in Dual Purpose Canisters for Criticality Suppression

This report documents work performed in support of the US Department of Energy Office of Nuclear Energy (NE) Spent Fuel and Waste Disposition, Spent Fuel and Waste Science and Technology, under work breakdown structure element 1.08.01.03.05, “Direct Disposal of Dual Purpose Canisters.” In particular, this report fulfills milestone M3SF-22PN010305094, “Application of AI/ML techniques to optimize DPC loading,” within work package SF-22PN01030509, “Direct Disposal of Dual Purpose Canisters - PNNL.” This report continues the process of examining the potential for using loading optimization as a disposal criticality suppression technique. This latest update to the report added: 1. A validation of the artificial neural network (ANN) reactivity prediction tool against as-loaded dual-purpose canisters from the UNF-ST&DARDS database 2. Refinement of the algorithm to incorporate an additional DPC design to improve the predictions 3. Investigation of a storage and transportation focused loading optimization algorithm. A key result from this year’s work is that the ANN performance was significantly improved by including the additional model for the NUHOMS canisters and it is apparent that fuel type specific modeling considerations should be incorporated into the ANN in future work. The GRASP-enabled adaptive multi-objective memetic algorithm with partial clustering (GAMMA-PC) algorithm was evaluated as a tool for storage and transportation oriented optimization. The GAMMA-PC optimization routine considers decay heat at loading, the time between loading and when the canister is eligible for transportation and minimizes the number of casks loaded. Future work will develop a dose minimization optimization routine.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2002

Molecular aspects of stress-gene regulation during spaceflight

Spaceflight-associated stress has been the topic of investigation since the first terrestrial organisms were exposed to this unique environment. Organisms that evolved under the selection pressures of earth-normal environments can perceive spaceflight as a stress, either directly because gravity influences an intrinsic biological process, or indirectly because of secondary effects imparted by spaceflight upon environmental conditions. Different organisms and even different organs within an organism adapt to a spaceflight environment with a diversity of tactics. Plants are keenly sensitive to gravity for directed development, and are also sensitive to other stresses associated with closed-system spaceflight environments. Within the past decade, the tools of molecular biology have begun to provide a sophisticated evaluation of spaceflight-associated stress and the genetic responses that accompany metabolic adaptation to spaceflight.

manned↗

DOE OSTI2013

Timing is everything :

People save for retirement throughout their career because it is virtually impossible to save all you ll need in retirement the year before you retire. Similarly, without installing incremental amounts of clean fossil, renewable or transformative energy technologies throughout the coming decades, a radical and immediate change will be near impossible the year before a policy goal is set to be in place. Therefore, our research question is, To meet our desired technical and policy goals, what are the factors that affect the rate we must install technology to achieve these goals in the coming decades? Existing models do not include full regulatory constraints due to their often complex, and inflexible approaches to solve for optimal engineering instead of robust and multidisciplinary solutions. This project outlines the theory and then develops an applied software tool to model the laboratory-to-market transition using the traditional technology readiness level (TRL) framework, but develops subsequent and a novel regulatory readiness level (RRL) and market readiness level (MRL). This tool uses the ideally-suited system dynamics framework to incorporate feedbacks and time delays. Future energy-economic-environment models, regardless of their programming platform, may adapt this software model component framework or module to further vet the likelihood of new or innovative technology moving through the laboratory, regulatory and market space. The prototype analytical framework and tool, called the Technology, Regulatory and Market Readiness Level simulation model (TRMsim) illustrates the interaction between technology research, application, policy and market dynamics as they relate to a new or innovative technology moving from the theoretical stage to full market deployment. The initial results that illustrate the model s capabilities indicate for a hypothetical technology, that increasing the key driver behind each of the TRL, RRL and MRL components individually decreases the time required for the technology to progress through each component by 63, 68 and 64%, respectively. Therefore, under the current working assumptions, to decrease the time it may take for a technology to move from the conceptual stage to full scale market adoption one might consider expending additional effort to secure regulatory approval and reducing the uncertainty of the technology s demand in the marketplace.

Kobos, Peter Holmes↗

NASA NTRS2006

Biologically Inspired Technology Using Electroactive Polymers (EAP)

Evolution allowed nature to introduce highly effective biological mechanisms that are incredible inspiration for innovation. Humans have always made efforts to imitate nature's inventions and we are increasingly making advances that it becomes significantly easier to imitate, copy, and adapt biological methods, processes and systems. This brought us to the ability to create technology that is far beyond the simple mimicking of nature. Having better tools to understand and to implement nature's principles we are now equipped like never before to be inspired by nature and to employ our tools in far superior ways. Effectively, by bio-inspiration we can have a better view and value of nature capability while studying its models to learn what can be extracted, copied or adapted. Using electroactive polymers (EAP) as artificial muscles is adding an important element to the development of biologically inspired technologies.

robotics↗

DOE OSTI2015

The auxin-inducible degradation (AID) system enables versatile conditional protein depletion in C. elegans

Experimental manipulation of protein abundance in living cells or organisms is an essential strategy for investigation of biological regulatory mechanisms. Whereas powerful techniques for protein expression have been developed in Caenorhabditis elegans, existing tools for conditional disruption of protein function are far more limited. To address this, we have adapted the auxin-inducible degradation (AID) system discovered in plants to enable conditional protein depletion in C. elegans. We report that expression of a modified Arabidopsis TIR1 F-box protein mediates robust auxin-dependent depletion of degron-tagged targets. We document the effectiveness of this system for depletion of nuclear and cytoplasmic proteins in diverse somatic and germline tissues throughout development. Target proteins were depleted in as little as 20-30 min, and their expression could be re-established upon auxin removal. We have engineered strains expressing TIR1 under the control of various promoter and 3' UTR sequences to drive tissue-specific or temporally regulated expression. The degron tag can be efficiently introduced by CRISPR/Cas9-based genome editing. We have harnessed this system to explore the roles of dynamically expressed nuclear hormone receptors in molting, and to analyze meiosis-specific roles for proteins required for germ line proliferation. Together, our results demonstrate that the AID system provides a powerful new tool for spatiotemporal regulation and analysis of protein function in a metazoan model organism.

Auxin-inducible degradation↗

DOE OSTI2015

Human–environment interactions in urban green spaces — A systematic review of contemporary issues and prospects for future research

Scientific papers on landscape planning underline the importance of maintaining and developing green spaces because of their multiple environmental and social benefits for city residents. However, a general understanding of contemporary human–environment interaction issues in urban green space is still incomplete and lacks orientation for urban planners. This review examines 219 publications to (1) provide an overview of the current state of research on the relationship between humans and urban green space, (2) group the different research approaches by identifying the main research areas, methods, and target groups, and (3) highlight important future prospects in urban green space research. - Highlights: • Reviewed literature on urban green pins down a dearth of comparative studies. • Case studies in Africa and Russia are marginalized – the Europe and US dominate. • Questionnaires are used as major tool followed by GIS and quantitative approaches. • Developing countries should contribute in building an urban green space agenda. • Interdisciplinary, adaptable and pluralistic approaches can satiate a knowledge gap.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Introduction to ALERT: A User-Interface Tool for Resilient Optimization

As the cyber-physical systems grow in complexity, there is a need for proactive resilience strategies that involve online, adaptive control actions to best prepare for any impending adversarial events. In this technical effort, supported by RD2C LDRD initiative, the project team designed and demonstrated online strategies – referred to as ALERT controls – for proactive and adaptive tuning of existing optimal controls in a microgrid, with quantifiably assured margins of resilience to various cyber-physical adversarial events. This ALERT functionality is made available to the end-users, e.g., the system operators, via an interactive user-interface. The end-users will not only be able to use the interface to visualize the system’s operation under various cyber-physical adversarial scenarios, but also evaluate the amount of tolerance the system has against selected adversarial perturbations of interest (e.g., malfunctioning sensors, suspected attacked measurements) via the adversarial plots. In this technical report, we briefly outline the algorithmic modules of the developed ALERT control technology, and introduce the user-interface tool that allows end-users (e.g., microgrid operators) to enter their system description, specify various operational and resilience requirements, and evaluate the impact of the control decisions via illustrative plots.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

Working at the Speed of Innovation: Impedance Mismatch in Rapid and Innovation Projects

In this paper we report on the results of an ethnographic study of a rapid design innovation (RDI) experiment in NASA Aeronautics. This work is based on the study of the Aeronautics Autonomy Testbed Capability (AATC) team in the Convergent Aeronautics Solutions (CAS) project. This paper focuses on and summarizes one of the key over-arching findings from the study: there is a significant mismatch in the organizational culture in the rest of the organization compared with that required for RDI. And, if this organizational cultural mismatch is not addressed, the likelihood of any organization being able to advance a new and different type of work (such as RDI) will be jeopardized. We delineate several aspects of the two different cultures identified in order to enable leaders and practitioners to better understand what contributes to the cultural dissonance and the implications of the differences in the cultures. As well, we identify ways in which those differences can be addressed. Research from organization and other social sciences are incorporated to highlight the differences. The implications of the research suggest that the significant cultural differences trigger a strong and resistive response from the dominant culture that may negate leadership’s strategy to build the new capacity for RDI. While the research results noted the pervasive nature of innovation throughout the workplace, the type of innovation envisioned in RDI is a rare type of innovation that requires significantly new methods, work processes, tools, and skills such that approaches used in the dominate culture cannot be adopted by expediting the existing approaches. Examples include: innovation teaming and leadership; the need for adaptive leadership that changes the relationship of a research leader to the other researchers; and, an interdisciplinary teaming approach which shapes team relationships and activities. Each of these aspects requires new teaming, tools, and skills in order to succeed. Thus, when introducing RDI activities where there is a different dominant culture, teams need to be: well trained; protected; recognized and rewarded. And, team leaders must also be trained in the unique types of teaming and innovation tools used in RDI. Both RDI teaming and team leadership must be sanctioned, supported, and rewarded by leadership. Because of the cultural mismatch, in some organizations looking to add RDI to their existing and established organizations, sep to avoid or mitigate the negative impact of the culture mismatch.

Crain, Cathleen↗