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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of a Nearly Autonomous Management and Control (NAMAC) System for Advanced Reactors (Final Report for ARPA-E Project)

The project formulates a technical basis for, and preliminary development of, a Nearly Autonomous Management and Control (NAMAC) system in advanced reactors. The system is intended to provide recommendations to operators during plant operation. The NAMAC system utilizes simulation-informed, data/knowledge-driven, AI-guided, plant-data-assimilated real-time operating procedures to effectively support the operator in risk-informed management of plant operations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2007

Broken Symmetry and Coherent Structure in MHD Turbulence

Absolute equilibrium ensemble theory for ideal homogeneous magnetohydrodynamic (MHD) turbulence is fairly well developed. Theory and Simulation indicate that ideal MHD turbulence non-ergodic and contains coherent structure. The question of applicability real (i.e., dissipative) MHD turbulence is examined. Results from several very long time numerical simulations on a 64(exp 3) grid are presented. It is seen that coherent structure begins to form before decay dominates over nonlinearity. The connection with inverse spectral cascades and selective decay will also be discussed.

Shebalin, John V.↗
DOE OSTI2025

Conversion of CO 2 from power plant into CaCO 3 nanoparticles

Carbon dioxide (CO 2 ), a main composition of flue gas, represents a significant and largely untapped carbon resource. Herein, mediated by glycine (Gly), we captured and converted CO 2 into CaCO 3 nanoparticles using real flue gas from a power plant, demonstrating for the first time the feasibility of using amino acid to convert CO 2 from power plant flue gasses. The method did not require extraneous energy and CaCO 3 nanoparticles with a size of ∼25 nm were obtained. Moreover, the potential toxicity of CO 2 -converted nanoparticles was investigated. It appeared that both the initial CO 2 loading and the carbamate percentage significantly influence the shape and size of the CaCO 3 particles. Our method was also proven effective for flue gas with varying CO 2 concentrations (4 %, 12 %, and 20 %). By tuning flue gas bubbling time and flow rate to achieve consistent CO 2 loading and carbamate levels, we produced CaCO 3 nanoparticles with similar shapes and sizes across all CO 2 concentrations studied. In addition, our data indicated that although real flue gas contains small amounts of gases like oxygen and CO, they insignificantly influence the shape and size of our nanoparticles but did impact the phase component of CaCO 3 . In conclusion, the toxicity experiments found that CaCO 3 nanoparticles produced from both real flue gas and simulated flue gas exhibited concentration- and time-dependent effects on cell viability.

36 MATERIALS SCIENCE↗
NASA NTRS1981

Flight test of a full authority Digital Electronic Engine Control system in an F-15 aircraft

The Digital Electronic Engine Control (DEEC) system considered is a relatively low cost digital full authority control system containing selectively redundant components and fault detection logic with capability for accommodating faults to various levels of operational capability. The DEEC digital control system is built around a 16-bit, 1.2 microsecond cycle time, CMOS microprocessor, microcomputer system with approximately 14 K of available memory. Attention is given to the control mode, component bench testing, closed loop bench testing, a failure mode and effects analysis, sea-level engine testing, simulated altitude engine testing, flight testing, the data system, cockpit, and real time display.

Barrett, W. J.↗
DOE OSTI2020

Influence of tungsten doping on nonradiative electron–hole recombination in monolayer MoSe 2 with Se vacancies

Two-dimensional transition metal dichalcogenides (TMDs) are receiving significant attention due to their excellent electronic and optoelectronic properties. The material quality is greatly affected by defects that are inevitably generated during material synthesis. Focusing on chalcogenide vacancies, which constitute the most common defect, we use the state-of-the-art simulation methodology developed in our group to demonstrate that W doping of MoSe 2 with Se vacancies reduces charge carrier losses by two mechanisms. First, W doping makes the formation of double Se vacancies unfavorable, while it is favorable in undoped MoSe 2 . Second, if a Se vacancy is present, the charge carrier lifetimes are extended in the W-doped MoSe 2 . Combining ab initio real-time time-dependent density functional theory with nonadiabatic molecular dynamics, the simulations show that the nonradiative carrier losses in the presence of Se vacancies proceed by sub-10 ps electron trapping and relaxation down the manifold of trap states, followed by a 100 ps recombination of trapped electrons with free holes. The electron–vibrational energy exchange is driven by both in-plane and out-of-plane vibrational motions of the MoSe 2 layer. Additionally, the atomistic studies advance our understanding of the influence of defects on charge carrier properties in TMDs and guide improvements of material quality and development of TMD applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2008

Virtual Shaker Testing: Simulation Technology Improves Vibration Test Performance

In the field of vibration testing, the interaction between the structure being tested and the instrumentation hardware used to perform the test is a critical issue. This is particularly true when testing massive structures (e.g. satellites), because due to physical design and manufacturing limits, the dynamics of the testing facility often couples with the test specimen one in the frequency range of interest. A further issue in this field is the standard use of a closed loop real-time vibration control scheme, which could potentially shift poles and change damping of the aforementioned coupled system. Virtual shaker testing is a novel approach to deal with these issues. It means performing a simulation which closely represents the real vibration test on the specific facility by taking into account all parameters which might impact the dynamic behavior of the specimen. In this paper, such a virtual shaker testing approach is developed. It consists of the following components: (1) Either a physical-based or an equation-based coupled electro-mechanical lumped parameter shaker model is created. The model parameters are obtained from manufacturer's specifications or by carrying out some dedicated experiments; (2) Existing real-time vibration control algorithm are ported to the virtual simulation environment; and (3) A structural model of the test object is created and after defining proper interface conditions structural modes are computed by means of the well-established Craig-Bampton CMS technique. At this stage, a virtual shaker test has been run, by coupling the three described models (shaker, control loop, structure) in a co-simulation routine. Numerical results have eventually been correlated with experimental ones in order to assess the robustness of the proposed methodology.

Ricci, Stefano↗
DOE OSTI2024

c-HAND: near real-time coastal flood mapping

The Texas Gulf Coast region contains significant centers of population, infrastructure, and economy and is threatened by intensifying tropical storms. The flooding from these tropical storms often has multiple compounding drivers. This characteristic presents a complex numerical problem where a simulation must consider multiple hydrologic forcings. While several procedures exist for addressing this problem numerically, they tend to be resource-intensive and cannot be conducted in near real-time. We extend GeoFlood, a reduced physics approach for fluvial flood forecasting, to rapidly predict coastal and compound fluvial-coastal inundation. This method is validated against a numerical ocean circulation model (ADCIRC) simulation of Hurricane Ike, a major coastal flooding event that happened on the Texas Gulf Coast in 2008. We show that the inundation map generated by coastal HAND (c-HAND) has reasonable agreement with the ADCIRC simulation while taking about 1.7% of the time currently needed to run ADCIRC on a supercomputer. While our model correctly predicts 99% of ADCIRC-inundated DEM cells, it also overpredicts inundated area by a factor of approximately 27%. We combine c-HAND with the GeoFlood framework for fluvial flood forecasting to create a compound fluvial-coastal inundation mapping workflow that can be run in near real-time. c-HAND's fast wall-clock time and low CPU requirements can support decision making by first response personnel. The method provides timely and convenient access to crucial information, such as the locations of flooded roads and inundated coastal areas.

42 ENGINEERING↗
NASA NTRS1992

Real-time diagnostics for a reusable rocket engine

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Guo, T. H.↗
DOE OSTI2021

Parallel transport dynamics for mixed quantum states with applications to time-dependent density functional theory

Direct simulation of the von Neumann dynamics for a general (pure or mixed) quantum state can often be expensive. One prominent example is the real-time time-dependent density functional theory (rt-TDDFT), a widely used framework for the first principle description of many-electron dynamics in chemical and materials systems. Practical rt-TDDFT calculations often avoid the direct simulation of the von Neumann equation, and solve instead a set of Schrödinger equations, of which the dynamics is equivalent to that of the von Neumann equation. However, the time step size employed by the Schrödinger dynamics is often much smaller. Here, in order to improve the time step size and the overall efficiency of the simulation, we generalize a recent work of the parallel transport (PT) dynamics for simulating pure states [An, Lin, Multiscale Model. Simul. 18, 612, 2020] to general quantum states. The PT dynamics provides the optimal gauge choice, and can employ a time step size comparable to that of the von Neumann dynamics. Going beyond the linear and near adiabatic regime in previous studies, we find that the error of the PT dynamics can be bounded by certain commutators between Hamiltonians, density matrices, and their derived quantities. Such a commutator structure is not present in the Schrödinger dynamics. We demonstrate that the parallel transport-implicit midpoint (PT-IM) method is a suitable method for simulating the PT dynamics, especially when the spectral radius of the Hamiltonian is large. The commutator structure of the error bound, and numerical results for model rt-TDDFT calculations in both linear and nonlinear regimes, confirm the advantage of the PT dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Multi-timescale operations of nuclear-renewable hybrid energy systems for reserve and thermal product provision

In this paper, an optimal operation strategy of a nuclear-renewable hybrid energy system (N-R HES), in conjunction with a district heating network, is developed within a comprehensive multi-timescale electricity market framework. The grid-connected N-R HES is simulated to explore the capabilities and benefits of N-R HES of providing energy products, different reserve products, and thermal products. An N-R HES optimization and control strategy is formulated to exploit the benefits from the hybrid energy system in terms of both energy and ancillary services. A case study is performed on the customized NREL-118 bus test system with high renewable penetrations, based on a multi-timescale (i.e., three-cycle) production cost model. Both day-ahead and real-time market clearing prices are determined from the market model simulation. In conclusion, the results show that the N-R HES can contribute to the reserve requirements and also meet the thermal load, thereby increasing the economic efficiency of N-R HES (with increased revenue ranging from 1.55% to 35.25% at certain cases) compared to the baseline case where reserve and thermal power exports are not optimized.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

DERMS-RT (Distributed Energy Resource Management Solution using Real-Time Optimization) [SWR-20-46]

DERMS-RT provides a distributed energy resource management solution using real-time optimization to control different types of distributed energy resources (DERs) — such as rooftop PVs, battery energy storage systems, HVAC loads, electric water heater loads, and EV charging loads, and use these DERs to provide distribution voltage regulation and peak demand management services. The control approach implemented by DERM-RT is developed based on the real-time optimal power flow and distributed control previously developed by NREL researchers. The software codes are designed to provide modular DER management solution that can be easily applied to control heterogeneous DERs in distribution grids in coordination with centralized utility management systems. Also, DERMS-RT provides application programming interface (API) to extract useful grid model information from an open-source power system simulation software, use the information to solve the optimization problem, and apply the optimal set-points to the controlled DERs. In this way, DERMS-RT provides a plug-and-play function for implementing the real-time DER control on different utility system models, and it can be used as a simulation testbed to demonstrate the impact of real-time DER optimization on improving grid operations.

Ding, Fei↗
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.↗