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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Absolute Measurement of Tilts via Fourier Analysis of Interferograms

The Fourier method of interferogram analysis requires the introduction of a constant tilt into the inteferogram to serve as a 'carrier signal' for information on the figure of the surface under test. This tilt is usually removed in the first steps of analysis and ignored thereafter. However, in the problem of aligning optical components and systems, knowledge of part orientation is crucial to proper instrument performance. This paper outlines an algorithm which uses the normally ignored carrier signal in Fourier analysis to compute an absolute tilt (orientation) of the test surface. We also provide a brief outline of how this technique, incorporated in a rotating Twyman-Green interferometer, can be used in alignment and metrology of optical systems.

Toland, Ronald W.↗
NASA NTRS2004

Absolute Measurement of Tilts via Fourier Analysis of Interferograms

The Fourier method of interferogram analysis requires the introduction of a constant tilt into the interferogram to serve as a carrier signal for information on the figure of the surface under test. This tilt is usually removed in the first steps of analysis and ignored thereafter. However, in the problem of aligning optical components and systems, knowledge of part orientation is crucial to proper instrument performance. This paper outlines an algorithm which uses the normally ignored carrier signal in Fourier analysis to compute an absolute tilt (orientation) of the test surface. We also provide a brief outline of how this technique, incorporated in a rotating Twyman-Green interferometer, can be used in alignment and metrology of optical systems.

Toland, Ronald W.↗
NASA NTRS1986

A knowledge-based expert system for scheduling of airborne astronomical observations

KAOS (Kuiper Airborne Observatory Scheduler) is a knowledge-based expert system developed at NASA Ames Research Center to assist in route planning of a C-141 flying astronomical observatory. This program determines a sequence of flight legs that enables sequential observations of a set of heavenly bodies derived from a list of desirable objects. The possible flight legs are constrained by problems of observability, avoiding flyovers of warning and restricted military zones, and running out of fuel. A significant contribution of the KAOS program is that it couples computational capability with a reasoning system.

Nachtsheim, P. R.↗
NASA NTRS2017

Next Generation Agricultural System Models and Knowledge Products: Synthesis and Strategy

The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. In the Introduction to this Special Issue, we described a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. In this concluding article of the NextGen Special Issue we synthesize insights and formulate a strategy to advance data, models, and knowledge products that are consistent with this vision. This strategy is designed to facilitate a transition from the current, primarily supply-driven approach toward a more demand-driven approach that would address key Use Cases where better data, models and knowledge products are seen by end-users as essential to meet their needs.

Next generation↗
DOE OSTI2024

Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.

14 SOLAR ENERGY↗
DOE OSTI2024

Sustainable Port Operations: Powered by NREL

Seaports are vital economic hubs that allow the United States to compete on a global scale. But the heavy vehicles and cargo equipment that enable their operations also emit harmful air pollutants and greenhouse gas emissions. For nearly two decades, National Renewable Energy Laboratory (NREL) researchers have worked toward comprehensive seaport decarbonization. They fuse world-class analysis with deep vehicle and transportation systems knowledge to guide strategic deployment of low- and zero-emissions vehicles, charging and refueling infrastructure, and grid improvements. Together, these capabilities can enable sustainable port operations. This fact sheet outlines major seaport and airport decarbonization capabilities across the laboratory, including: fleet research, energy data, and insights for decarbonization; comprehensive hydrogen infrastructure deployment; optimized charging through grid integration; strategic blueprinting for clean, optimized technology deployment; and integrating diversity, equity, inclusion, and accessibility considerations into decarbonization efforts.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
NASA NTRS1987

A formal approach to validation and verification for knowledge-based control systems

As control systems become more complex in response to desires for greater system flexibility, performance and reliability, the promise is held out that artificial intelligence might provide the means for building such systems. An obstacle to the use of symbolic processing constructs in this domain is the need for verification and validation (V and V) of the systems. Techniques currently in use do not seem appropriate for knowledge-based software. An outline of a formal approach to V and V for knowledge-based control systems is presented.

Castore, Glen↗
NASA NTRS2005

High Altitude Long Endurance (HALE) Unmanned Aircraft System (UAS): Pilot Knowledge, Skills and Abilities

This report summarizes the initial work accomplished by the ACCESS 5 Human System Integration (HSI) team to identify Unmanned Aircraft System (UAS) Pilot Knowledge, Skill and Ability (KSA), Training and Medical requirements. To derive this information the following tasks were accomplished: a) Mission and Function analyses were performed; b) Applicable FARs and FAA Advisory Circulars (ACs) were reviewed; c) Meetings were conducted with NASA and FAA Human Factors personnel; d) Surveys were completed by ACCESS 5 HSI Working group UA Pilots; e) Coordination meetings were conducted with the ACCESS 5 Policy IPT. The results of these efforts were used to develop a summary of the current qualifications. for an individual to function as a Pilot In Command (PIC) for UAs currently flown by UNITE companies, to develop preliminary Pilot KSAs for each phase of flight, and to delineate preliminary Pilot Training and Medical requirements. These results are to be provided to the Policy IPT to support their development of recommendations for UA Pilot Rating Criteria, training and medical qualifications. It is expected that the initially an instrument rated pilot will be required to serve as the PIC. However, as operational experience is gained, and automation is applied to accomplish various system functions, it is expected that pilot rating criteria could be lessened.

Source record↗
NASA NTRS2006

Momentum Transfer in a Spinning Fuel Tank Filled with Xenon

Transient spin-up and spin-down flows inside of spacecraft fuel tanks need to be analyzed in order to properly design spacecraft control systems. Knowledge of the characteristics of angular momentum transfer to and from the fuel is used to size the de-spin mechanism that places the spacecraft in a controllable in-orbit state. In previous studies, several analytical models of the spin-up process were developed. However, none have accurately predicted all of the flow dynamics. Several studies have also been conducted using Navier-Stokes based methods. These approaches have been much more successful at simulating the dynamic processes in a cylindrical container, but have not addressed the issue of momentum transfer. In the current study, the spin-up and spin-down of a fuel tank filled with gaseous xenon has been investigated using a three-dimensional unsteady Navier-Stokes code. Primary interests have been concentrated on the spin-up/spin-down time constants and the initial torque imparted on the system. Additional focus was given to the relationship between the dominant flow dynamics and the trends in momentum transfer. Through the simulation of both a cylindrical and a spherical tank, it was revealed that the transfer of angular momentum is nonlinear at early times and tends toward a linear pattern at later times. Further investigation suggests that the nonlinear spin up is controlled by the turbulent transport of momentum, while the linear phase is controlled by a Coriolis driven (Ekman) flow along the outer wall. These results indicate that the spinup and spin-down processes occur more quickly in tanks with curved surfaces than those with defined top, bottom, and side walls. The results also provide insights for the design of spacecraft de-spin mechanisms.

Peugeot, John W.↗
NASA NTRS2010

SMART: The Future of Spaceflight Avionics

A novel avionics approach is necessary to meet the future needs of low cost space and lunar missions that require low mass and low power electronics. The current state of the art for avionics systems are centralized electronic units that perform the required spacecraft functions. These electronic units are usually custom-designed for each application and the approach compels avionics designers to have in-depth system knowledge before design can commence. The overall design, development, test and evaluation (DDT&E) cycle for this conventional approach requires long delivery times for space flight electronics and is very expensive. The Small Multi-purpose Advanced Reconfigurable Technology (SMART) concept is currently being developed to overcome the limitations of traditional avionics design. The SMART concept is based upon two multi-functional modules that can be reconfigured to drive and sense a variety of mechanical and electrical components. The SMART units are key to a distributed avionics architecture whereby the modules are located close to or right at the desired application point. The drive module, SMART-D, receives commands from the main computer and controls the spacecraft mechanisms and devices with localized feedback. The sensor module, SMART-S, is used to sense the environmental sensors and offload local limit checking from the main computer. There are numerous benefits that are realized by implementing the SMART system. Localized sensor signal conditioning electronics reduces signal loss and overall wiring mass. Localized drive electronics increase control bandwidth and minimize time lags for critical functions. These benefits in-turn reduce the main processor overhead functions. Since SMART units are standard flight qualified units, DDT&E is reduced and system design can commence much earlier in the design cycle. Increased production scale lowers individual piece part cost and using standard modules also reduces non-recurring costs. The benefit list continues, but the overall message is already evident: the SMART concept is an evolution in spacecraft avionics. SMART devices have the potential to change the design paradigm for future satellites, spacecraft and even commercial applications.

Alhorn, Dean C.↗
DOE OSTI2025

Energy Scheduling-based Operating Envelopes including a Distribution System Branch Screening Algorithm

This paper presents an energy scheduling-based formulation for computing operating envelopes including a distribution branch screening algorithm, termed DBS-ES. The contribution of the paper is two-fold: firstly, it presents an innovative methodology for calculating operating envelopes using energy scheduling (baseline), and secondly, it enhances this methodology by incorporating a custom distribution branch screening algorithm (DBS-ES). The custom algorithm leverages power system knowledge to reduce both model build time and total processing time while maintaining the same scheduling results as the baseline. The effectiveness of the proposed approach is demonstrated through experiments on the IEEE13, IEEE123, and EPRI Secondary test feeders. Results highlight a 24.5% decrease in model build time and an 8.17% decrease in total processing time when using DBS-ES compared to the baseline, specifically for the IEEE123 test feeder. Additionally, the paper briefly discusses the influence of utility-controlled storage on computing operating envelopes, noting a general incre

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1985

A knowledge-based expert system for scheduling of airborne astronomical observations

The Kuiper Airborne Observatory Scheduler (KAOS) is a knowledge-based expert system developed at NASA Ames Research Center to assist in route planning of a C-141 flying astronomical observatory. This program determines a sequence of flight legs that enables sequential observations of a set of heavenly bodies derived from a list of desirable objects. The possible flight legs are constrained by problems of observability, avoiding flyovers of warning and restricted military zones, and running out of fuel. A significant contribution of the KAOS program is that it couples computational capability with a reasoning system.

Nachtsheim, P. R.↗
NASA NTRS2006

Automated Activation and Deactivation of a System Under Test

The MPLM Automated Activation/Deactivation application (MPLM means Multi-Purpose Logistic Module) was created with a three-fold purpose in mind: 1. To reduce the possibility of human error in issuing commands to, or interpreting telemetry from, the MPLM power, computer, and environmental control systems; 2. To reduce the amount of test time required for the repetitive activation/deactivation processes; and 3. To reduce the number of on-console personnel required for activation/ deactivation. All of these have been demonstrated with the release of the software. While some degree of automated end-item commanding had previously been performed for space-station hardware in the test environment, none approached the functionality and flexibility of this application. For MPLM activation, it provides mouse-click selection of the hardware complement to be activated, activates the desired hardware and verifies proper feedbacks, and alerts the user when telemetry indicates an error condition or manual intervention is required. For MPLM deactivation, the product senses which end items are active and deactivates them in the proper sequence. For historical purposes, an on-line log is maintained of commands issued and telemetry points monitored. The benefits of the MPLM Automated Activation/ Deactivation application were demonstrated with its first use in December 2002, when it flawlessly performed MPLM activation in 8 minutes (versus as much as 2.4 hours for previous manual activations), and performed MPLM deactivation in 3 minutes (versus 66 minutes for previous manual deactivations). The number of test team members required has dropped from eight to four, and in actuality the software can be operated by a sole (knowledgeable) system engineer.

Poff, Mark A.↗
DOE OSTI2020

Contaminant Transport Modeling for Technology Evaluation and Long-Term Monitoring in the Tims Branch Testbed, SC - 20343

Studies conducted at U.S. DOE sites have shown the presence of heavy metals, radionuclides, and volatile organic compounds in surface water, groundwater, and soil as a result of nuclear activity in the Cold War era. Since the 1990's, innovative cleanup methods have been implemented in the Tims Branch watershed at Savannah River Site (SRS) to limit the contaminant flux to the stream that have reduced the contaminant concentrations to acceptable regulatory levels in the dissolved phase. A tin-based treatment which effectively eliminated all local anthropogenic mercury inputs to this ecosystem resulted in a known step function addition of inert tin oxide particles which now serve as a potential tracer for sedimentation and particle transport processes in the stream. The long-term effectiveness of this and other remediation techniques and the potential for remobilization of adsorbed contaminant in sediment during extreme hydrologic conditions however remains unclear. It is therefore important to understand not only the fate and transport of dissolved contaminants, but also the movement of sediment and the relevant interactions with dissolved contaminant. To narrow this knowledge gap, a study is being conducted using the Tims Branch watershed as a stream-scale ecosystem test-bed to identify the primary transport processes of major contaminants of concern (such as mercury, nickel and uranium) with an emphasis on interactions with sediment transport. This involves the development of a fully distributed hydrologic watershed model of the Tims Branch watershed to predict streamflow under extreme weather conditions, as well as the development of a comprehensive contaminant transport model that can properly account for coupled contaminant and sediment transport. Review of relevant research reports and peer-reviewed journals revealed that aside from advection-dispersion transport of dissolved contaminants, adsorption and desorption with suspended solids and bed sediment also play an important role in the transport of those contaminants of concern. To develop the fully distributed hydrologic watershed model, the MIKE SHE 2-dimensional (2D) land surface/3D groundwater model that simulates surface/subsurface hydrologic processes (such as overland flow, evapotranspiration, and infiltration) was coupled with a 1D streamflow model that accounts for stream water hydraulics (such as hydraulic structures, cross-sections, and network). To model contaminant transport, the MIKE 11 streamflow component was coupled with the MIKE 11 AD module that simulates solute transport through advection and dispersion, and MIKE ECO Lab module that accounts for both sediment transport and interactions with dissolved contaminant. At this stage, the development and optimization of the fully distributed hydrologic model has been completed, achieving satisfactory statistical results between observed and predicted discharge as indicated by a root mean square error (RMSE) of 0.039 cms and a Nash-Sutcliffe efficiency coefficient (NSE) of 0.764. The ongoing development of the contaminant transport model has also yielded realistic results from preliminary tests. Results from this study are a key to evaluating the effectiveness of tin (II)-based mercury treatment of wetlands at the SRS site, and are also relevant to evaluating the potential of using this type of novel remediation technology in other mercury-contaminated stream systems. Knowledge acquired from this research will also support interpretation of historical data on the trends of contaminant concentration distribution in Tims Branch, particularly considering the effect of extreme hydrological events on the stream flow and pollutant transport. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics↗
DOE OSTI2024

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Novel Multiagent Resource Sharing Algorithm for Control of Advanced Energy Systems

This paper implements a novel resource sharing control strategy on a fuel cell–gas turbine hybrid power system at the National Energy Technology Laboratory’s Hybrid Performance Facility (Hyper). In a fuel cell–gas turbine hybrid power system, the simultaneous interaction of the gas turbine and the fuel cell creates a tightly coupled environment characterized by conflicting dynamics. In this paper, a model-free control approach is applied to solve the tightly coupled control problem posed by this challenging environment. Specifically, this control problem is presented as a resource sharing problem that can be solved using a resource sharing algorithm that is defined based on the distribution construction concept. This algorithm creates computational agents and solves the problem through the distribution and redistribution of shared resources defined as blocks. Furthermore, two agents were created; the first agent (agent 1) controls the gas turbine speed by adjusting the electric load, and the second agent (agent 2) controls the cathode mass flow through the fuel cell using the cold-air bypass valve. A parametric study was performed over the course of 15 experimental tests for both agents 1 and 2 through an evaluation of the responses based on setpoint changes. The algorithm was shown to have behavior comparable to a previously implemented multi-input multioutput state-space controller, which was designed through a model-based control approach. The resource sharing algorithm was able to find stable performance during run-time operations without any prior system knowledge identification on the power plant and without creating models used in traditional control strategies.

25 ENERGY STORAGE↗