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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Formation and reconfiguration of tight multi-lane platoons

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives.

42 ENGINEERING↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗
DOE OSTI2025

Unconventional nonlinear Hall effects in twisted multilayer 2D materials

We present the first investigation of unusual nonlinear Hall effects in twisted multilayer 2D materials. Contrary to expectations, our study shows that these nonlinear effects are not merely extensions of their monolayer counterparts. Instead, we find that stacking order and pairwise interactions between neighboring layers, mediated by Berry curvatures, play a pivotal role in shaping their collective nonlinear optical response. By combining large-scale Real-Time Time-Dependent Density Functional Theory (RT-TDDFT) simulations with model Hamiltonian analyses, we demonstrate a remarkable second-harmonic transverse response in hexagonal boron nitride four-layers, even in cases where the total Berry curvature cancels out. Furthermore, our symmetry analysis of the layered structures provides a simplified framework for predicting nonlinear responses in multilayer materials in general. Our investigation challenges the prevailing understanding of nonlinear optical responses in layered materials and opens new avenues for the design and development of advanced materials with tailored optical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic short-range correlation in photoinduced disorder phase transitions

Ultrafast photoexcitation can induce a nonequilibrium dynamic with electron-lattice interaction, offering an effective way to study photoinduced phase transitions (PIPTs) in solids. The issue that atomic displacements after photoexcitation belong to a coherent change or disordered process has become controversial in the PIPT community. Using real-time, time-dependent density functional theory (rt-TDDFT) simulations, we were able to obtained both the coherent and the disordered PIPTs (dimer dissociation) in IrTe 2 with different electronic occupations. More importantly, we found that in the disordered phase transition there exists a local correlation between different dimers regarding their dissociation status. We define these Ir-Ir dimers directly connected by Te atoms, including intralayer and vertically across the layers, as a group (group I). Other Ir-Ir dimers separated by five Ir atoms from Ir-Ir dimers in group I are divided into another group (group II). The dimers in the same group will dissociate in a correlated fashion; they either all dissociate or all do not dissociate. On the other hand, the dimers in neighboring groups will have an anticorrelation: If the dimers in one group dissociate, the dimers in the neighboring group tend not to be dissociated, and vice versa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Enabling discovery data science through cross-facility workflows

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.

Antypas, Katerina B.↗
DOE OSTI2024

Data and figures for "Integrated modeling of boron powder injection for real-time plasma-facing component conditioning"

This dataset contains raw and processed data, as well as supplementary figures used in the paper titled "Integrated modeling of boron powder injection for real-time plasma-facing component conditioning." The data includes simulation results for boron transport and deposition in DIII-D tokamak scenarios, and processed plots. It provides insights into the effects of boron powder injection on plasma-facing component conditioning and surface composition.

ablative particle injection↗
NASA NTRS1976

Multiplane binocular visual display system

Electro-optic system is interfaced with digital computer in flight simulator to generate simultaneous multiple-image planes in real time. System may have applications with other display and remote-control systems.

Chase, W. D.↗
NASA NTRS1990

A tool for modeling concurrent real-time computation

Real-time computation is a significant area of research in general, and in AI in particular. The complexity of practical real-time problems demands use of knowledge-based problem solving techniques while satisfying real-time performance constraints. Since the demands of a complex real-time problem cannot be predicted (owing to the dynamic nature of the environment) powerful dynamic resource control techniques are needed to monitor and control the performance. A real-time computation model for a real-time tool, an implementation of the QP-Net simulator on a Symbolics machine, and an implementation on a Butterfly multiprocessor machine are briefly described.

Sharma, D. D.↗
NASA NTRS1989

Validation and verification of expert systems using evidence flow graphs

This paper describes an ongoing investigation into the use of evidence flow graph techniques for performing V&V of expert systems. This method involves translating a rule-base into an evidence flow graph, a representation originally developed for real-time intelligent systems in distributed environments, and then running simulations of the evidence flow graph. Certain errors can be found during the translation process. The simulations can detect output sensitivity to rule firing order, to order of presentation of inputs, and to small changes in input values.

Becker, Lee A.↗
NASA NTRS1990

Simulation technology - A key to effective man-machine integration for future combat rotorcraft systems

The utilization of advanced simulation technology in the development of the non-real-time MANPRINT design tools in the Army/NASA Aircrew-Aircraft Integration (A3I) program is described. A description is then given of the Crew Station Research and Development Facilities, the primary tool for the application of MANPRINT principles. The purpose of the A3I program is to develop a rational, predictive methodology for helicopter cockpit system design that integrates human factors engineering with other principles at an early stage in the development process, avoiding the high cost of previous system design methods. Enabling technologies such as the MIDAS work station are examined, and the potential of low-cost parallel-processing systems is indicated.

Kerr, Andrew W.↗
NASA NTRS1992

Active vibration control using a modal-domain fiber optic sensor

A closed-loop control experiment is described in which vibrations of a cantilevered beam are suppressed using measurements from a modal-domain fiber optic sensor. Modal-domain sensors are interference between the modes of a few-mode optical waveguide to detect strain. The fiber is bonded along the length of the beam and provides a measurement related to the strain distribution on the surface of the beam. A model for the fiber optic sensor is derived, and this model is integrated with the dynamic model of the beam. A piezoelectric actuator is also bonded to the beam and used to provide control forces. Control forces are obtained through dynamic compensation of the signal from the fiber optic sensor. The compensator is implemented with a real-time digital controller. Analytical models are verified by comparing simulations to experimental results for both open-loop and closed-loop configurations.

Cox, David E.↗
NASA NTRS1993

A PC-based simulation of the National Transonic Facitity's safety microprocessor

A brief study was undertaken to demonstrate the feasibility of using a state-of-the-art off-the-shelf high speed personal computer for simulating a microprocessor presently used for wind tunnel safety purposes at Langley Research Center's National Transonic Facility (NTF). Currently, there is no active display of tunnel alarm/alert safety information provided to the tunnel operators, but rather such information is periodically recorded on a process monitoring computer printout. This does not provide on-line situational information nor permit rapid identification of safety operational violations which are able to halt tunnel operations. It was therefore decided to simulate the existing algorithms and briefly evaluate a real-time display which could provide both position and trouble shooting information.

Thibodeaux, J. J.↗
NASA NTRS1993

Development of a system for transition characterization

The present system for real-time boundary layer transition detection and characterization encompasses aerodynamic simulations, sensor characteristics, and a hybrid analog/digital system for signal analysis, verification, and compression. By using artificial, intermittently laminar/turbulent signals, in conjunction with trajectory simulation, it becomes possible to conduct algorithm development which is independent of, and in advance of, sensor buildup. Attention is given to the illustrative case of the Pegasus launch vehicle's trajectory.

Bertelrud, Arild↗
NASA NTRS1996

Rapid Assessment of Agility for Conceptual Design Synthesis

This project consists of designing and implementing a real-time graphical interface for a workstation-based flight simulator. It is capable of creating a three-dimensional out-the-window scene of the aircraft's flying environment, with extensive information about the aircraft's state displayed in the form of a heads-up-display (HUD) overlay. The code, written in the C programming language, makes calls to Silicon Graphics' Graphics Library (GL) to draw the graphics primitives. Included in this report is a detailed description of the capabilities of the code, including graphical examples, as well as a printout of the code itself

Biezad, Daniel J.↗
NASA NTRS2008

Integrated Docking Simulation and Testing with the Johnson Space Center Six-Degree of Freedom Dynamic Test System

The Exploration Systems Architecture defines missions that require rendezvous, proximity operations, and docking (RPOD) of two spacecraft both in Low Earth Orbit (LEO) and in Low Lunar Orbit (LLO). Uncrewed spacecraft must perform automated and/or autonomous rendezvous, proximity operations and docking operations (commonly known as Automated Rendezvous and Docking, AR&D). The crewed versions may also perform AR&D, possibly with a different level of automation and/or autonomy, and must also provide the crew with relative navigation information for manual piloting. The capabilities of the RPOD sensors are critical to the success of the Constellation Program; this is carried as one of the CEV Project top risks. The Exploration Technology Development Program (ETDP) AR&D Sensor Technology Project seeks to reduce this risk by increasing technology maturation of selected relative navigation sensor technologies through testing and simulation. One of the project activities is a series of "pathfinder" testing and simulation activities to integrate relative navigation sensors with the Johnson Space Center Six-Degree-of-Freedom Test System (SDTS). The SDTS will be the primary testing location for the Orion spacecraft s Low Impact Docking System (LIDS). Project team members have integrated the Orion simulation with the SDTS computer system so that real-time closed loop testing can be performed with relative navigation sensors and the docking system in the loop during docking and undocking scenarios. Two relative navigation sensors are being used as part of a "pathfinder" activity in order to pave the way for future testing with the actual Orion sensors. This paper describes the test configuration and test results.

Mitchell, Jennifer D.↗
NASA NTRS2012

Near real-time traffic routing

A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

Yang, Chaowei↗