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NASA NTRSDate not supplied

LIS-Hydro: Authoritative Source for OCONUS Hydro-Intelligence

U.S. military forces are often tasked to participate in a variety of transboundary water-related decision-making activities, including humanitarian assistance operations through Department of State tasking, support of in-country infrastructure development activities that help develop or improve diplomatic relationships, and support of transboundary water treaty negotiations or disputes to reduce risk of conflict caused by water security issues. The U.S. intelligence communities have identified the coordination over shared water resources as an area of significant concern to U.S. national security (U.S. National Defense Strategy, 2018). Such transboundary water issues are projected to intensify in the future under increasingly complex population dynamics, political tensions due to parallel issues, and a changing climate. A 2017 joint NASA, USACE/RDC, and U.S. Air Force co-sponsored workshop revealed a lack of sufficient decision support tools and access to timely technical and contextual information needed to assess and respond to potential water-related threats around the world. The need for an integrated operational service, with the capacity to combine and synthesize hydrological modeling, assimilation, forecasting, and visualization capabilities across the U.S. Government, was highlighted as a key recommendation. In direct response, a subset of the U.S. Department of Defense, National Intelligence Community, and Oak Ridge National Laboratory are collaborating on the development of a fully integrated hydro-modeling and streamflow prediction system (i.e., LIS-Hydro). Completion of the project and sustainment of the operational capability by Air Force Weather will establish a national asset to assist federal agencies implement government-wide strategies around water resources (U.S. Global Water Strategy, 2017). The hydrological products and services will, for the very first time, establish a routinely available authoritative source of global water intelligence information supporting war-fighters, planners, and decision makers at all echelons and services of the U.S. military, Federal government, and intelligence community. A summary of the interagency scientific collaboration in addressing some of the key gaps and needs identified during the 2017 workshop will be presented.

Land Information System↗

NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗

NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

RASSOR↗

NASA NTRS2024

Applying Generative-AI to NASA Documentation and Processes

This research and development project leverages generative-AI to assist in the generation of software process documentation based on NASA standards. By utilizing fine-tuned AI models, the proposed system will analyze NASA's software guidelines, helping to translate them into well-structured, compliant process documents. This assistance can reduce the manual effort required to produce such documentation, enhance consistency, and assure alignment with NASA's stringent software development and operational requirements. In addition to assisting in the generation of software process documentation, the project explores how generative-AI can help create audit checklists as well as assess the compliance of NASA provider documentation against applicable NASA standards. This approach would support the compliance auditing process, providing real-time insights and assessments. The intended result will be a streamlined process, potentially including a Python-based tool and database, that improves audit efficiency, reduces human error, lowers manpower costs and required manhours, and assures continuous compliance with NASA and industry evolving standards for safety-critical software development. Future task might be to investigate the software industry approach and standards for potential collaboration.

NASA Standards↗

DOE OSTI2018

Implementing Concurrent Non-Event Transitionsin CMS

Available on CMS information serverCMS CR -2018/277The Compact Muon Solenoid ExperimentMailing address: CMS CERN, CH-1211 GENEVA 23, SwitzerlandConference Report15 October 2018 (v2, 21 November 2018)Implementing Concurrent Non-Event Transitionsin CMSChristopher Jones for the CMS CollaborationAbstractSince the beginning of the LHC Run 2 in 2016 the CMS data processing framework, CMSSW, hasbeen running with multiple threads during production of data and simulation via the use of IntelsThread Building Blocks (TBB) library. The TBB library utilizes tasks as concurrent units of work.CMS used these tasks to allow both concurrent processing of events as well as concurrent running ofmodules processing the same event. This design has served CMS well and has allowed jobs to utilizeless memory per core as well as reduce the number of jobs that must be tracked by CMSs worflowmanagement system. As CMS has begun to utilize greater number of threads in a job, the effect ofserialization points in the framework has decreased jobs CPU efficiency. One major serialization pointoccurs when the framework processes a non-Event transition. These transitions occur when a new Runor LuminosityBlock is to be processed. In this talk we will discuss how the different transitions definethe processing model for CMSSW and how we were able to successfully allow concurrent process-ing of those transitions using TBB via task queues. We will also show CPU efficiency comparisonsbetween the same work being done with and without the additional concurrency.

Jones, Christopher↗

NASA NTRS1987

Fire-related medical science

Spacecraft fire safety may be improved by the use of a fire-retardant atmosphere in occupied spaces. Low concentrations of oxygen can protect humans from fire damage by reducing the rate and spread of combustion, but care must be taken to avoid the hypoxic effects of oxygen-lean atmospheres. Crews can live and work in 11 percent oxygen if barometric pressure were adjusted to maintain the partial pressure of oxygen above 16 kPa. Eleven percent oxygen should prevent most types of fires, since 15 percent oxygen retards the combustion of paper and 13 percent oxygen extinguishes pentane flames. Test results indicate that seated humans can perform mental tasks in atmospheres containing 11.5 percent oxygen. Although this strategy of fire safety is under consideration for submarines, it could be adapted to spacecraft once operational procedures define a maximum hyperbaric pressure and fire research defines the effects of reduced oxygen concentrations on combustion in low gravity environments.

Knight, Douglas R.↗

DOE OSTI2025

Optimizers for stabilizing likelihood-free inference

A growing number of applications in particle physics and beyond use neural networks as unbinned likelihood ratio estimators applied to real or simulated data. Precision requirements on the inference tasks demand a high-level of stability from these networks, which are affected by the stochastic nature of training. We show how physics concepts can be used to stabilize network training through a physics-inspired optimizer. In particular, the energy conserving descent (ECD) optimization framework uses classical Hamiltonian dynamics on the space of network parameters to reduce the dependence on the initial conditions while also stabilizing the result near the minimum of the loss function. We develop a version of this optimizer known as , which has few free hyperparameters with limited ranges guided by physical reasoning. We apply to representative likelihood-ratio estimation tasks in particle physics and find on average that it out-performs the widely used Adam optimizer. We expect that ECD will be a useful tool for wide array of data-limited problems, where it is computationally expensive to exhaustively optimize hyperparameters and mitigate fluctuations with ensembling.

Monte Carlo methods↗

DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1977

The effect of a visual/motion display mismatch in a single axis compensatory tracking task

The frequency response of visual systems is typically unity from 0 to 20 rad/sec, while that of motion systems typically falls off in the vicinity of 6 rad/sec. The question arises as to what effect, if any, such a difference in servomechanism performance has on the simulation. Is pilot performance reduced by the conflict between displays? Would a more realistic simulation occur if the visual servomechanisms were degraded to match the motion servomechanisms? Does the pilot need and use the higher frequency information present in the visual display? The purpose of the experiment is to take a step forward toward answering these questions. Work already in the literature which bears on these questions is outlined. A description is then given of an experiment used to check for the effects of a difference in the performance of the visual and motion servomechanisms (the experiment uses a single-axis, compensatory, roll-tracking task). The results of the experiment are then presented and analyzed.

Shirachi, D. K.↗

NASA NTRS1992

Task-based configuration control of redundant manipulators

This article establishes new goals for redundancy resolution based on manipulator dynamics and end-effector characteristics. These goals can be accomplished by employing the recently developed configuration control approach. Redundancy resolution is achieved by controlling the joint inertia matrix or the end-effector mass matrix that affect the inertial torques or by reducing the joint torques due to gravity loading and payload. The manipulator mechanical advantages and velocity ratio are also used as performance measurements to be improved by proper utilization of redundancy. Furthermore, end-effector compliance, sensitivity, and impulsive force at impact are introduced as redundancy-resolution criteria. The new goals for redundancy resolution presented in this article allow a more efficient utilization of the redundant joints based on the desired task requirements. Simple case studies using computer simulations are described for illustration.

Seraji, Homayoun↗

DOE OSTI2015

Evaluation of Hanford Tank Supernatant Availability for Technetium Management Project Studies in FY16

This report examines the need for actual Hanford tank waste solutions to support tasks in the Technetium Management Program in fiscal year (FY) 2016. One key need is to identify both samples where a majority of the soluble technetium is present as pertechnetate and samples where it is not. The total amount of tank supernatant needed from any given tank waste supernatant was determined by polling the tasks leaders for their technology testing needs in FY16 and then arbitrarily ascribing a 10% process loss associated with consolidation and the Cs-137 removal needed to reduce the dose to a level suitable for testing in radiological fumehoods. These polling results identified a need for approximately 2.1 to 3.6 kg of any particular targeted Hanford tank waste supernatant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Learning quantum computers' errors using interpretable neural networks

Learning and reducing the errors and noise in quantum computing systems is necessary for achieving quantum computation’s promise. However, rapid advances in experimental quantum computing are making this task increasingly difficult, because state-of-the-art systems now contain hundreds of qubits and many characterization techniques are hard to apply at this scale. Furthermore, complex kinds of errors in these systems, such as crosstalk and non-Markovian effects, must be understood and decreased, but these errors are challenging to study with most existing methods. In this project, we explored using neural networks for scalable characterization of complex errors in quantum computers. We proposed and demonstrated characterizing a quantum computer’s errors with neural networks that have interpretable parameters corresponding to the rates of different kinds of errors, within a sparse Lindbladian parameterization for errors. To enable scaling to many qubit systems, these networks then predict how these errors combine within quantum circuits and impact their outcomes using an efficient approximations. We demonstrated these networks ability to learn coherent crosstalk errors and context-dependent errors in a simulated 4-qubit system.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1973

A description of a general use automated data reduction system

A data system developed for use in tests in arc jet facilities has the flexibility to accommodate various types of data, time resolution, and number of data signals. Data are tape-recorded, because a direct link to the IBM 360/67 computer is prohibited for safety reasons. The system is analogous to manual data reduction to promote user understanding and acceptance. Data computations may be performed in an interactive ('conversational') mode, for minimum delay in data reduction, or by card input. Reduced data are listed and also stored for plotting of further computational use. Operational costs of the system are reasonable, and both large and small data reduction tasks are handled readily.

Wakefield, R. M.↗

NASA NTRS1980

Cost savings in LSI fabrication

One year study program was divided into three tasks: to identify costs factors involved in packaged electronic subsystems as function of LSI density and reliability; to select most promising factors that might be modified to reduce costs in high-density packaging; and to investigate cost-saving beam tape technology for producing high-volume discrete-device packages. Results are published in 67 page report.

Himmel, R. P.↗

NASA NTRS1990

Simulator comparison of thumball, thumb switch, and touch screen input concepts for interaction with a large screen cockpit display format

A piloted simulation study was conducted comparing three different input methods for interfacing to a large screen, multiwindow, whole flight deck display for management of transport aircraft systems. The thumball concept utilized a miniature trackball embedded in a conventional side arm controller. The multifunction control throttle and stick (MCTAS) concept employed a thumb switch located in the throttle handle. The touch screen concept provided data entry through a capacitive touch screen installed on the display surface. The objective and subjective results obtained indicate that, with present implementations, the thumball concept was the most appropriate for interfacing with aircraft systems/subsystems presented on a large screen display. Not unexpectedly, the completion time differences between the three concepts varied with the task being performed, although the thumball implementation consistently outperformed the other two concepts. However, pilot suggestions for improved implementations of the MCTAS and touch screen concepts could reduce some of these differences.

Jones, Denise R.↗

NASA NTRS1993

Expert System for UNIX System Reliability and Availability Enhancement

Highly reliable and available systems are critical to the airline industry. However, most off-the-shelf computer operating systems and hardware do not have built-in fault tolerant mechanisms, the UNIX workstation is one example. In this research effort, we have developed a rule-based Expert System (ES) to monitor, command, and control a UNIX workstation system with hot-standby redundancy. The ES on each workstation acts as an on-line system administrator to diagnose, report, correct, and prevent certain types of hardware and software failures. If a primary station is approaching failure, the ES coordinates the switch-over to a hot-standby secondary workstation. The goal is to discover and solve certain fatal problems early enough to prevent complete system failure from occurring and therefore to enhance system reliability and availability. Test results show that the ES can diagnose all targeted faulty scenarios and take desired actions in a consistent manner regardless of the sequence of the faults. The ES can perform designated system administration tasks about ten times faster than an experienced human operator. Compared with a single workstation system, our hot-standby redundancy system downtime is predicted to be reduced by more than 50 percent by using the ES to command and control the system.

Xu, Catherine Q.↗

NASA NTRS1992

Development of the quasi-procedural method for use in aircraft configuration optimization

The performance of the quasi-procedural analysis system in optimization tasks is investigated. In particular, the quasi-procedural method is applied to the complete mission optimization of a medium-sized transport aircraft using a vortex-lattice aerodynamic model and finite element structural analysis of the wing and the tail. Some modifications and improvements to the system, required in order to complete this task, are described. The performance of the system is compared with that of the standard procedural system, and it is shown that the quasi-procedural approach reduces the analysis required to reach the optimum. The overall cost is shown to be strongly dependent on the analysis architecture and data structure.

Gage, P.↗

NASA NTRS1996

Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.

Alexander, June↗