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DOE OSTI2024

Multidimensional perspectives of geo-epidemiology: from interdisciplinary learning and research to cost–benefit oriented decision-making

Research typically promotes two types of outcomes (inventions and discoveries), which induce a virtuous cycle: something suspected or desired (not previously demonstrated) may become known or feasible once a new tool or procedure is invented and, later, the use of this invention may discover new knowledge. Research also promotes the opposite sequence—from new knowledge to new inventions. This bidirectional process is observed in geo-referenced epidemiology—a field that relates to but may also differ from spatial epidemiology. Geo-epidemiology encompasses several theories and technologies that promote inter/transdisciplinary knowledge integration, education, and research in population health. Based on visual examples derived from geo-referenced studies on epidemics and epizootics, this report demonstrates that this field may extract more (geographically related) information than simple spatial analyses, which then supports more effective and/or less costly interventions. Actual (not simulated) bio-geo-temporal interactions (never captured before the emergence of technologies that analyze geo-referenced data, such as geographical information systems) can now address research questions that relate to several fields, such as Network Theory. Thus, a new opportunity arises before us, which exceeds research: it also demands knowledge integration across disciplines as well as novel educational programs which, to be biomedically and socially justified, should demonstrate cost-effectiveness. Grounded on many bio-temporal-georeferenced examples, this report reviews the literature that supports this hypothesis: novel educational programs that focus on geo-referenced epidemic data may help generate cost-effective policies that prevent or control disease dissemination.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2003

The VISAR Process

The Video Image Stabilization And Registration (VISAR) process is an award winning video image processing software developed at NASA's Marshall Space Flight Center. VISAR has a wide variety of application areas where the refinement of digital video is needed. It is used to correct jitter, rotation, and zoom effects by registering and processing on individual image captures that are a part of normal video capturing. Its most prominent uses were the 1996 Olympic Bombing case and in identifying Saddam Hussein during the Iraq war. Based on first-hand knowledge, this paper describes the VISAR process, which consists of several steps designed to refine digital video using VISAR software. The process determines the differences between two video images so that one, or both, of the images can be changed in ways that make them match as well as possible. Corrections include changes in position (horizontal and vertical image shifts), changes in orientation (image rotation), and changes in magnification (image zoom). While much of the VISAR process is automated, in its current embodiment it requires the user to initially identify the area of interest and to reset a threshold parameter if the default gives unacceptable results. The basic process that is used is an old tried and true method that determines how well the two images match. This process is called cross-correlation. It gives a single number, the correlation coefficient, that is equal to 1.0 if the images are perfectly matched, is equal to 0.0 if the images have nothing in common, and is equal to -1.0 if one image is the negative of the other. This basic process is used by many image stabilization methods. With VISAR we use it in a manner that provides statistical information needed to best determine orientation and magnification.

Hathaway, David↗

NASA NTRS2024

Universal Coating by 3D Hybrid Programmable Matter

Motivated by the prospect of nano-robots that assist human physiological functions at the nanoscale, we investigate the coating problem in the three-dimensional model for hybrid programmable matter. In this model, a single agent with strictly limited viewing range and the computational capability of a deterministic finite automaton can act on passive tiles by picking up a tile, moving, and placing it at some spot. The goal of the coating problem is to fill each node of some surface graph of size n with a tile. We first solve the problem on a restricted class of graphs with a single tile type, and then use constantly many tile types to encode this graph in certain surface graphs capturing the surface of 3D objects. Our algorithm requires O(n^2) steps, which is worst-case optimal compared to an agent with global knowledge and no memory restrictions.

programmable matter↗

NASA NTRS2015

Developing a Fault Management Guidebook for Nasa's Deep Space Robotic Missions

NASA designs and builds systems that achieve incredibly ambitious goals, as evidenced by the Curiosity rover traversing on Mars, the highly complex International Space Station orbiting our Earth, and the compelling plans for capturing, retrieving and redirecting an asteroid into a lunar orbit to create a nearby a target to be investigated by astronauts. In order to accomplish these feats, the missions must be imbued with sufficient knowledge and capability not only to realize the goals, but also to identify and respond to off-nominal conditions. Fault Management (FM) is the discipline of establishing how a system will respond to preserve its ability to function even in the presence of faults. In 2012, NASA released a draft FM Handbook in an attempt to coalesce the field by establishing a unified terminology and a common process for designing FM mechanisms. However, FM approaches are very diverse across NASA, especially between the different mission types such as Earth orbiters, launch vehicles, deep space robotic vehicles and human spaceflight missions, and the authors were challenged to capture and represent all of these views. The authors recognized that a necessary precursor step is for each sub-community to codify its FM policies, practices and approaches in individual, focused guidebooks. Then, the sub-communities can look across NASA to better understand the different ways off-nominal conditions are addressed, and to seek commonality or at least an understanding of the multitude of FM approaches. This paper describes the development of the "Deep Space Robotic Fault Management Guidebook," which is intended to be the first of NASA's FM guidebooks. Its purpose is to be a field-guide for FM practitioners working on deep space robotic missions, as well as a planning tool for project managers. Publication of this Deep Space Robotic FM Guidebook is expected in early 2015. The guidebook will be posted on NASA's Engineering Network on the FM Community of Practice website so that it will be available to all NASA projects. Future plans for subsequent guidebooks for the other NASA sub-communities are proposed.

Fesq, Lorraine M.↗

DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗

NASA NTRS1990

Model authoring system for fail safe analysis

The Model Authoring System is a prototype software application for generating fault tree analyses and failure mode and effects analyses for circuit designs. Utilizing established artificial intelligence and expert system techniques, the circuits are modeled as a frame-based knowledge base in an expert system shell, which allows the use of object oriented programming and an inference engine. The behavior of the circuit is then captured through IF-THEN rules, which then are searched to generate either a graphical fault tree analysis or failure modes and effects analysis. Sophisticated authoring techniques allow the circuit to be easily modeled, permit its behavior to be quickly defined, and provide abstraction features to deal with complexity.

Sikora, Scott E.↗

NASA NTRS2016

Exposing Hidden Parts of the SE Process: MBSE Patterns and Tools for Tracking and Traceability

An interesting benefit of applying Model-Based Systems Engineering (MBSE) is that the rigor and coordination intrinsic to MBSE forces us to apply Systems Engineering to our own traditional activities, processes, and products, which results in richer, more expressive models, more powerful reasoning, and a clearer and more effective Systems Engineering (SE) process. Our MBSE frameworks and languages contain semantic richness sufficient to describe our systems at any particular point in time, often with an emphasis on the description of the system at major milestones. This is unarguably a real asset. However, when we apply MBSE in service of missions that are in development, rapidly evolving, of a larger scale, and where interpersonal communication is a critical part of the design process, we discover that our frameworks and languages are still not quite rich enough to enable us to ask the kinds of questions and get the kinds of answers we want in order to address the concerns of day to day work. This paper will discuss some patterns and tools we have developed to help address some of the not-always-explicit SE concerns that we have identified through our MBSE work. Particularly, this paper will discuss flexible yet practical methods for defining and capturing maturity, workflow, and agreement traceability within our system models, extensible ways to perform and track model audits, and ways to report and interact with this knowledge in the context of MBSE applied to support NASA’s Europa Project.

Jackson, Maddalena↗

NASA NTRS2016

Exposing Hidden Parts of the SE Process: MBSE Patterns and Tools for Tracking and Traceability

Jackson, Maddalena↗

NASA NTRSDate not supplied

How Is the Solar Wind Shocked? A Distribution-Function Perspective From the MMS Mission

In this talk, we will discuss how plasma distribution functions from the Magnetospheric Multiscale (MMS) mission transformed the picture on how the solar wind is shocked. For quasi-parallel shocks with typical Alfven Mach numbers, we will examine how ion deceleration, reflection, and thermalization take place, and illustrate how the new picture expands on the framework envisioned by Schwartz and Burgess in 1991. For quasi-perpendicular shocks with the range of Alfven Mach numbers (~1-60) captured by MMS, we will examine examples showing the processes of ion and electron thermalization go beyond the existing paradigm and call for future investigation. The knowledge advanced by MMS measurements provides a ground to understand planetary shocks in our solar system as well as astrophysical shocks arising from stellar wind interaction with exoplanets and supernova remnants.

Li-Jen Chen↗

NASA NTRS2022

Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural Networks

Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.

Fabric↗

DOE OSTI2024

Flux REaction TArget Prioritization (Flux RETAP) v1

Metabolic engineering is evolving rapidly as a result of new advances in synthetic biology and automation, as well as the irruption of machine learning (ML). ML has been shown to provide the predictive power synthetic biology lacked and needed, and to be able to effectively guide the metabolic engineering process. However, current technical limitations prevent the independent application of ML approaches to metabolic engineering without the use of previous biological knowledge in the form of a prioritized list of desirable engineering targets. Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models (GSMs) for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing metabolite production. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production in the literature accessible to us, 50% of targets that experimentally improved taxadiene production in E. coli and ~60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets which can also be utilized in ML pipelines.

Czajka, Jeffrey [Battelle Memorial Institute, Paci↗

NASA NTRS2018

Educación Global

The International Space Station has a unique ability to capture the imaginations of both students and teachers worldwide. The presence of humans aboard the station provides a foundation for numerous educational activities aimed at capturing interest and motivating children towards the study of science, technology, engineering and mathematics (STEM). Projects such as the Amateur Radio on International Space Station (ARISS); Earth Knowledge Acquired by Middle School Students (EarthKAM); and Synchronized Position Hold, Engage, Reorient Experimental Satellites (SPHERES) Zero Robotics competition, among others, have allowed for global student, teacher and public access to space through student image acquisition and radio contacts with crew members. Projects like these and their accompanying educational materials are distributed to students around the world. Through the continued use of the station, we will challenge and inspire the next generation of scientists, engineers, writers, artists, politicians and explorers.

Lopez, Erik↗

NASA NTRS1996

Knowledge Engineering for Preservation and Future use of Institutional Knowledge

This Project has two main thrusts-preservation of special knowledge and its useful representation via computers. NASA is losing the expertise of its engineers and scientists who put together the great missions of the past. We no longer are landing men on the moon. Some of the equipment still used today (such as the RL-10 rocket) was designed decades ago by people who are now retiring. Furthermore, there has been a lack, in some areas of technology, of new projects that overlap with the old and that would have provided opportunities for monitoring by senior engineers of the young ones. We are studying this problem and trying out a couple of methods of soliciting and recording rare knowledge from experts. One method is that of Concept Maps which produces a graphical interface to knowledge even as it helps solicit that knowledge. We arranged for experienced help in this method from John Coffey of the Institute of Human and Machine Technology at the University of West Florida. A second method which we plan to try out in May, is a video-taped review of selected failed missions (e.g., the craft tumbled and blew up). Five senior engineers (most already retired from NASA) will, as a team, analyze available data, illustrating their thought processes as they try to solve the problem of why a space craft failed to complete its mission. The session will be captured in high quality audio and with at least two video cameras. The video can later be used to plan future concept mapping interviews and, in edited form, be a product in itself. Our computer representations of the amassed knowledge may eventually, via the methods of expert systems, be joined with other software being prepared as a suite of tools to aid future engineers designing rocket engines. In addition to representation by multimedia concept maps, we plan to consider linking vast bodies of text (and other media) by hypertexting methods.

Moreman, Douglas↗

DOE OSTI2024

In-plane Wilson loop for measurement of quantized non-Abelian Berry flux

Band topology of anomalous quantum Hall insulators can be precisely addressed by computing the Chern numbers of constituent nondegenerate bands, describing the presence of quantized, Abelian Berry flux through the two-dimensional Brillouin zone. Can Berry flux be captured for the SU (2) Berry connection of two -fold degenerate bands in spinful materials preserving space -inversion ($\mathscr{P}$) and time -reversal ($\mathscr{T}$) symmetries without detailed knowledge of underlying basis? We address this question by investigating the correspondence between a non -Abelian generalization of Stokes' theorem and the manifestly gauge -invariant eigenvalues of Wilson loops computed along in -plane contours which preserve the underlying crystalline symmetry. The importance of this correspondence is elucidated by performing natural number resolved classification of ab initio band structures of three-dimensional, Dirac materials. Further, our work underscores how identification of quantized Berry flux, both Abelian and non -Abelian, offers a unified framework for addressing first -order and higher -order topology of insulators and semimetals.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS2021

Global Daytime Variability of Clouds from DSCOVR/EPIC Observations

Knowledge of the daytime variability of cloud fraction is pivotal for the accurate determination of the atmosphere's energy balance. Yet, polar orbiting satellites are unable to fully capture daytime variability and individual geostationary satellites only provide measurements for portions of the Earth. In addition, diurnal cloud fraction variations analyzed with General Circulation Models provide results that cannot be reconciled with observations in many situations. Here we show that the Earth Polychromatic Imaging Camera (EPIC) aboard the DSCOVR satellite presents a unique opportunity to examine changes in daytime global cloud fraction resolved at one-hour frequency. For the first time, we characterize the global daytime variability of clouds with a single sensor, presenting evidence that the fraction of liquid clouds reaches its maximum over continental areas and its minimum over ocean around noon. We compare our results with previous global and regional studies and examine implications for the Earth's energy budget.

cloud fraction↗

NASA NTRS2005

Learning Lessons from the X-37 Project

The X-37 was planned as an automated vehicle capable of flight-testing new aerospace technologies in combined environments that are beyond the capability of existing ground or flight platforms. Flight demonstration with the X-37 architecture and configuration in relevant environments was planned to reduce the risk of developing launch vehicle technologies for sustainable, affordable exploration and other aerospace applications. Current plans are for the X-37 Approach and Landing Test Vehicle (ALTV) to be atmospheric tested in 2005 from Scaled Composite's White Knight carrier aircraft at up to 40,000 feet over California's Mojave Spaceport, with and turnaround maintenance performed. The fight Operations Control Center will conduct the mission, using a streamlined operations concept. Taxi-tow and captive-carry tests will be conducted prior to the atmospheric-test series. Sponsored by the Defense Advanced Research Projects Agency (DARPA) with NASA participation, technical objectives are to: (1) mature Computed Air Data System/Remote Pressure Sensor technology, (2) manage energy during Terminal Area Energy Management/Heading Alignment Cone maneuvers, and (3) validate the aerodatabase. The X-37 Project began in 1999 under a cooperative agreement as an element of NASA's Future X Program and transitioned to a NASA Research Announcement under the Space Launch Initiative. In mid-2004, NASA transferred ownership to DARPA, with its heritage of performing high-risk, high-payoff research and development. NASA contributes technical expertise, including risk analysis and system integration. The Boeing Company is the prime contractor, with nationwide suppliers. This recent partnership exemplifies the synergy attainable when NASA Centers, other Government agencies, and industry work together toward a common goal - contributing to the knowledge base for U.S. exploration and other aerospace endeavors. The X-37 team represents a range of space transportation disciplines - from engineering to management. Some members have been with the project since its inception. All have gained priceless experience during the design, manufacturing, and testing of the ALTV, as well as through developing advanced orbital flight technologies, such as state-of-the-art Thermal Protection Systems and hot structures. Throughout this process, the X-37 Project team captures lessons that are directly applicable to other such efforts. The upcoming ALTV flights offer another dimension of data and first-hand experience that will prove invaluable to those designing new generations of reusable spacecraft. And ongoing technology developments will expand the aerospace knowledge base. Delivering prototype hardware is always a risky proposition. During the course of the X-37 effort, the team has experienced many challenging opportunities, delivering significant accomplishments and learning numerous lessons in the process. The ability to manage the risk landscape is key to overcoming obstacles, especially technical hurdles that are encountered in progressing hardware from design to flight. The approach to managing risk under this partnership is evolving but, in general, the team allocates resources to reduce the likelihood of severe-consequence risks, thus maximizing mission success and ensuring that the X-37 Project delivers value to its stakeholders. As the team sharpens its focus on operations, it continues to contribute knowledge to those who would undertake high-risk, high-payoff research and development and provides valuable experience to implement the exploration vision.

Turner, Susan↗

NASA NTRS2005

Learning Lessons from the X-37 Project

The X-37 was planned as an automated vehicle capable of flight-testing new aerospace technologies in combined environments that are beyond the capability of existing ground or flight platforms. Flight demonstration with the X-37 architecture and configuration in relevant environments was planned to reduce the risk of developing launch vehicle technologies for sustainable, affordable exploration and other aerospace applications. Current plans are for the X-37 Approach and Landing Test Vehicle (ALTV) to be atmospheric tested in 2005 from Scaled Composite s White Knight carrier aircraft at up to 40,000 feet over California's Mojave Spaceport, with landing and turnaround maintenance performed. The Flight Operations Control Center will conduct the mission, using a streamlined operations concept. Taxi-tow and captive-carry tests will be conducted prior to the atmospheric-test series. Sponsored by the Defense Advanced Research Projects Agency (DARPA) with NASA participation, technical objectives are to: (1) mature Computed Air Data System/Remote Pressure Sensor technology, (2) manage energy during Terminal Area Energy Management/Heading Alignment Cone maneuvers, and (3) validate the aerodatabase. The X-37 Project began in 1999 under a cooperative agreement as an element of NASA's Future X Program and transitioned to a NASA Research Announcement under the Space Launch Initiative. In mid-2004, NASA transferred ownership to DARPA, with its heritage of performing high-risk, high-payoff research and development (R&D). NASA contributes technical expertise, including risk analysis and system integration. The Boeing Company is the prime contractor, with nationwide suppliers. This partnership exemplifies the synergy attainable when NASA Centers, other Government agencies, and industry work together toward a common goal - contributing to the knowledge base for U.S. exploration and other aerospace endeavors. The X-37 team represents a range of space transportation disciplines - from engineering to management. Some members have been with the project since its inception. All have gained priceless experience during the design, manufacturing, and testing of the ALTV, as well as through developing advanced orbital flight technologies, such as state-of-the-art Thermal Protection Systems and hot structures. Throughout this process, the X-37 Project team captures lessons that are directly applicable to other such efforts. The upcoming ALTV flights offer another dimension of data and first-hand experience that will prove invaluable to those designing new generations of reusable spacecraft. And ongoing technology developments will expand the aerospace knowledge base. Delivering prototype hardware is always a risky proposition. During the course of this effort, the X-37 team has experienced many challenging opportunities, delivering significant accomplishments and learning numerous lessons in the process. The ability to manage the risk landscape is key to overcoming obstacles, especially technical hurdles that are encountered in progressing hardware from design to flight. The approach to managing risk under this partnership is evolving but, in general, the team allocates resources to reduce the likelihood of severe-consequence risks, thus maximizing mission success and ensuring that the X-37 Project delivers value to its stakeholders. As the team sharpens its focus on operations, it continues to contribute knowledge to those who would undertake high-risk, high-payoff R&D and provides valuable experience to implement the Vision for Space Exploration.

Turner, Susan↗

DOE OSTI2024

Integration of the fundamental knowledge on solvent-packing interactions into the multiscale framework for column scale design and optimization

The interfacial area, also known as the effective mass transfer area, is a key factor for determining the mass transfer for carbon dioxide (CO 2 ) capture via the chemical absorption process in a packed column, and thus the overall capture efficiency of the packed column. Most of the widely used empirical and semi-empirical models for interfacial area were derived indirectly through absorption mass transfer with simplifications based on fast chemical kinetics. This report presents the comprehensive unique multiscale approach to develop a surrogate model for effective mass transfer area in structured packed columns that accounts local hydrodynamics as well as variation in physical properties, and changes in solid surface characteristics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗