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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Solving differential equations with unknown constitutive relations as recurrent neural networks

We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term. We assume that the measurements (time series) of state variables are partially available, and use a recurrent neural network to “learn” the reaction rate from this data. This is achieved by including discretized ordinary differential equations as part of a recurrent neural network training problem. We extend TensorFlow’s recurrent neural network architecture to create a simple but scalable and effective solver for the unknown functions, and apply it to a fedbatch bioreactor simulation problem. Use of techniques from recent deep learning literature enables training of functions with behavior manifesting over thousands of time steps. Our networks are structurally similar to recurrent neural networks, but differ in purpose, and require modified training strategies.

deep learning↗
DOE OSTI2019

Integrated solver for fluid driven fracture and fragmentation

An integrated solver may be used for both a fluid phase and a solid phase. The integrated solver may use constitutive equations for the fluid phase and the solid phase, and calculate the stress in the fluid and solid using the same integration point. The integrated solver may also calculate other state variables using the same integration point.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Adaptive Neuro Fuzzy Inference System for Cyber-Intrusion Detection in a Smart Grid

The evolution of the power grid has brought increasing deployment of advance metering infrastructure, penetration of intelligent electronic devices, and integration of physical power system components with information and communications technologies. With the fast-expanding connectivity, cyber vulnerabilities arise due to the use of internet-based communication systems. These systems are targets of cyber-intrusions which attempt to disturb the normal power system functions. Traditional intrusion detection algorithms have been developed without an explicit model of the cyber components. In this paper, an algorithm to detect false data injections in the power system is proposed considering both cyber and physical models of the power system. The algorithm is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) which collects information from state variables of the cyber-physical system to meet the performance requirements of the grid. Simulations of the proposed approach using the IEEE 13-bus test system validate the effectiveness of this artificial intelligence-based algorithm.

Adaptive Neuro Fuzzy Inference System (ANFIS), cyb↗
DOE OSTI2020

On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System

In recent years, the autonomous control system has been encouraged in advanced reactors for restoring economic viability, simplifying the operation and maintenance, and enabling remote-site power generations [1]. Since the reactor is expected to be operated for a long period of time with a limited number of individuals onsite, it is recommended that the autonomous control system should have access to very realistic models of the state of processes in the whole lifecycle, together with these process behaviors in interaction with their environment in the real world. As a result, digital twin (DT) technology is suggested in autonomous control systems. DT is defined as a digital representation of a physical object or system, which contains a record for the histories of loads, operation and maintenance status, predictions for the near-term transient of important state variables, and decision-making process [2]. Since machine learning (ML) can recognize patterns within a complex system in real-time applications, it has been used to build DTs in the autonomous control systems for advanced reactors. Meanwhile, due to the rareness of operation data in accident scenarios, the development and assessment of DTs is expected to be mainly driven by simulations. Although the capability and feasibility of ML-based DTs are recognized in improving the safety and efficiency of reactor control, a major concern from the regulatory commission and the nuclear industry is whether the information from a DT is developed and assessed in accordance with expectation and requirements by the target decision. Such concerns not only affect the acceptance criteria for DTs, but also values that can be extracted from DTs and autonomous control system during operations. Inspired by the success of formal methods in improving the reliability and robustness of computer programming and software development, it is suggested that the development and assessment process (DAP) for both separate DTs and integral control system should be formalized in a transparent, consistent, and improvable manner. In this study, a digital-twin development and assessment process (DT-DAP) is proposed by adapting the evaluation model development and assessment process (EMDAP) [3] to requirements by the autonomous control system, ML algorithms, and DT technology. To demonstrate the framework, a baseline nearly autonomous management and control (NAMAC) system with ML-based DTs for diagnosis and prognosis is developed and assessed based on the framework. It is found that with selected testing methods and techniques, the DT-DAP can help identify errors in DTs and NAMAC which would otherwise be left unverified. Meanwhile, it is found that the DT-DAP can improve the DTs and NAMAC by continuously learning and iterating through different elements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Visualization of the Oscillatory Dynamics of an Island Power System: Preprint

In this work, we discuss the design of visualizations for understanding the complex oscillatory dynamics of an island power system with renewable generation sources after the loss of a large oil power plant. As more renewable generation sources are added to power systems, the oscillatory dynamics will change, which requires new visualization techniques to determine causes and strategies to avoid unwanted behaviors in the future. Our approach integrates geographic views, time-series plots, and novel oscillatory-trajectory curves, providing unique insights into the interdependent oscillatory behaviors of multiple state variables and generators over time. By enabling multi-node and multivariate comparisons over time, users can qualitatively determine drivers of oscillations and differences in generator dynamics, which is not possible with other commonly used visualization techniques.

inverters↗
DOE OSTI2023

Applying Quantum Computing to Simulate Power System Dynamics

Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations due to a large number of generators, loads, and transmission lines. Thus, its computational complexity grows exponentially with the system size. This paper demonstrates the potential use of quantum computing algorithms to model the power system dynamics. Leveraging a symbolic programming framework, we equivalently convert the power system dynamics’ differential algebraic equations (DAEs) into ordinary differential equations (ODEs), where the data of the state vector can be encoded into quantum computers via amplitude encoding. The system's nonlinearity is captured by Taylor polynomial expansion, the quantum state tensor, and Hamiltonian simulation, whereas state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can simulate the dynamics of the power system with high accuracy, whereas its complexity is polynomial in the logarithm of the system dimension. Our work also illustrates the use of scientific machine learning tools for implementing scientific computing concepts, e.g., Taylor expansion, DAEs/ODEs transform, and quantum computing solver, in the field of power engineering.

Tran, Huynh↗
DOE OSTI2026

Microstructural Topology as a Prescriptor for Quantum Coherence: Towards A Unified Framework for Decoherence in Superconducting Qubits

In superconducting quantum circuits, decoherence improvements are frequently obtained through process interventions that simultaneously modify surface chemistry, microstructural topology, and device geometry, leaving mechanistic attribution structurally underdetermined. Predictive materials engineering requires measurable structural statistics to be separated from geometry-dependent coupling coefficients into independently testable factors. We introduce the concept of classical and quantum microstructure. In that context, we formulate a channel-wise separable framework for decoherence in superconducting transmon qubits in which each loss channel is described by a reduced prescriptor. Here, a channel-specific microstructural state variable is determined independently of device geometry, and a geometry-dependent coupling functional is computable from field solutions without reference to surface chemistry. We derive this product form from a spatially resolved kernel representation and establish a perturbative separability criterion that defines the regime where independent variation of the variables is valid. The framework specifies five prescriptor classes for dominant loss pathways in transmon-class devices. Falsifiability is operationalized through a pre-committed 2x2 experimental protocol in which the variables must satisfy independent ratio checks within propagated uncertainty. A Minimum-Dataset Specification standardizes reporting for cross-laboratory inference. Part I establishes the conceptual and mathematical architecture; coordinated experimental validation is reserved for Part II.

Dravid, Vinayak P. [Northwestern U.]↗
DOE OSTI2025

Evaluation of historical precipitation interannual variability in CMIP6 over the United States

Interannual precipitation variability profoundly influences society via its effects on agriculture, water resources, infrastructure, and disaster risks. In this study, we use daily in situ precipitation observations from the global historical climatology network-daily (GHCN-D) to assess the ability of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, including the 50-member fifth-generation Canadian Earth System Model single model initial-condition large ensemble (CanESM5_SMILE), to realistically simulate historical interannual precipitation variability trends within 17 regions of the contiguous United States (CONUS). We assess how accurately the CMIP6 simulations align with observational data across annual, summer, and winter periods, focusing on four key hydrometeorological metrics, including interannual precipitation variability, relative interannual precipitation variability (coefficient of variation), annual mean precipitation, and annual wet day frequency. Our findings reveal that CMIP6 ensemble members generally reproduce the spatial patterns of observed trends in annual mean precipitation. In most regions, models agree well with the signs of observed changes in annual mean precipitation, though discrepancies in trend magnitude are evident. Further, observed trends in winter mean precipitation broadly exhibit a spatial pattern similar to that of the observed annual mean. However, analysis of the CanESM5_SMILE shows that trends in precipitation variability may primarily be the result of model-simulated internal variability, suggesting caution in interpreting multi-model single-realization ensemble results. Challenges in accurately simulating interannual precipitation variability underscore the need for ongoing model refinement and validation to enhance climate projections, especially in regions vulnerable to extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

The Pattern Across the Continental United States of Evapotranspiration Variability Associated with Water Availability

The spatial pattern across the continental United States of the interannual variance of warm season water-dependent evapotranspiration, a pattern of relevance to land-atmosphere feedback, cannot be measured directly. Alternative and indirect approaches to estimating the pattern, however, do exist, and given the uncertainty of each, we use several such approaches here. We first quantify the water dependent evapotranspiration variance pattern inherent in two derived evapotranspiration datasets available from the literature. We then search for the pattern in proxy geophysical variables (air temperature, stream flow, and NDVI) known to have strong ties to evapotranspiration. The variances inherent in all of the different (and mostly independent) data sources show some differences but are generally strongly consistent they all show a large variance signal down the center of the U.S., with lower variances toward the east and (for the most part) toward the west. The robustness of the pattern across the datasets suggests that it indeed represents the pattern operating in nature. Using Budykos hydroclimatic framework, we show that the pattern can largely be explained by the relative strength of water and energy controls on evapotranspiration across the continent.

Air Temperature↗
DOE OSTI2014

Origin of the current discretization in deep reset states of an Al{sub 2}O{sub 3}/Cu-based conductive-bridging memory, and impact on state level and variability

In this paper, we develop a Quantum-Point-Contact (QPC) model describing the state conduction in a W/Al{sub 2}O{sub 3}/TiW/Cu Conductive-Bridging Memory cell (CBRAM). The model allows describing both the voltage- and the temperature-dependence of the conduction. For deep current levels, a resistance component is added in series to the point-contact constriction to account for electron scattering in the residual filament. The fitting of single-particle perturbation also allowed to estimate the number and effective size of the conduction-controlling particles in the QPC constriction. The results clearly point to smaller particles for CBRAM (Cu particles) as compared to oxide-based resistive RAM involving oxygen-vacancy defects, which is discussed as a possible origin of deeper reset level obtained in CBRAM. We also evidence a beneficial impact of this smaller particle size on lower Random-Telegraph-Noise amplitude measured on CBRAM devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Geometric and Material Variability Influences Stress States Relevant to Coastal Permafrost Bluff Failure

Scientific knowledge and engineering tools for predicting coastal erosion are largely confined to temperate climate zones that are dominated by non-cohesive sediments. The pattern of erosion exhibited by the ice-bonded permafrost bluffs in Arctic Alaska, however, is not well-explained by these tools. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is needed. We conducted physics-based numerical simulations of mechanical response that focus on the impact of geometric and material variability on permafrost bluff stress states for a coastal setting in Arctic Alaska that is prone to toppling mode block failure. Our three-dimensional geomechanical boundary-value problems output static realizations of compressive and tensile stresses. We use these results to quantify variability in the loci of potential instability. We observe that niche dimension affects the location and magnitude of the simulated maximum tensile stress more strongly than the bluff height, ice wedge polygon size, ice wedge geometry, bulk density, Young's Modulus, and Poisson's Ratio. Our simulations indicate that variations in niche dimension can produce radically different potential failure areas and that even relatively shallow vertical cracks can concentrate displacement within ice-bonded permafrost bluffs. These findings suggest that stability assessment approaches, for which the geometry of the failure plane is delineated a priori, may not be ideal for coastlines similar to our study area and could hamper predictions of erosion rates and nearshore sediment/biogeochemical loading.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

On mathematical and physical approaches to constructing a quantum cluster state in continuous variables, or is it possible to construct a cluster from different modes?

We consider the problems of constructing a cluster state from a set of orthogonal modes. It is shown that the commonly used unitary transformation is not reduced in this case to a set of standard operations of linear optics performed over fields in a squeezed state, but should be supplemented by a more complicated operation of a noise-free transformation of the quantum signal profile. At the same time, if it is possible to form a cluster whose node amplitudes have different time profiles, this property does not interfere with the performance of calculations on the cluster. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Assessing the Influence of Background State and Climate Variability on Tropical Cyclones Using Initialized Ensembles and Mesh Refinement in E3SM

The scientific goal of this project is to assess the influence of the background state and horizontal resolution of climate models on the simulation of extreme events like intense precipitation, tropical cyclones, atmospheric rivers, and heatwaves. The main results are: - Initialized ensembles of 4-week forecasts using a climate model (E3SM) show that dynamic (flow) and thermodynamic (moisture) biases asymptote to their climatological values at different rates—dynamic bias grows rapidly and approaches its climatological value within about 2 weeks whereas the thermodynamic bias takes about 4 weeks. - Moisture bias has a bigger impact on extreme events like tropical cyclones and intense precipitation than flow bias, and this impact is non-monotonic over bias evolution time. - Climate models are capable of simulating the flow features associated with unprecedented extreme events like the Western North America heatwave of June 2021, but moisture biases can weaken the surface manifestation of extreme heatwaves. Reducing model bias may be more important than increasing model resolution in improving the fidelity of heatwave simulations in climate models. - Climate models are capable of simulating weather patterns associated with “power droughts”, i.e., periods with low solar and wind power generation, provided model biases in the simulation of the background state can be corrected. - Regional mesh refinement can improve the simulation of extreme weather events but model biases may still persist.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

The Causal Connection Between Disc and Power-Law Variability in Hard State Black Hole X-Ray Binaries

We use the XMM-Newton EPIC-pn instrument in timing mode to extend spectral time-lag studies of hard state black hole X-ray binaries into the soft X-ray band. \Ve show that variations of the disc blackbody emission substantially lead variations in the power-law emission, by tenths of a second on variability time-scales of seconds or longer. The large lags cannot be explained by Compton scattering but are consistent with time-delays due to viscous propagation of mass accretion fluctuations in the disc. However, on time-scales less than a second the disc lags the power-law variations by a few ms, consistent with the disc variations being dominated by X-ray heating by the power-law, with the short lag corresponding to the light-travel time between the power-law emitting region and the disc. Our results indicate that instabilities in the accretion disc are responsible for continuum variability on time-scales of seconds or longer and probably also on shorter time-scales.

Uttley, P.↗
NASA NTRS1989

Solid-state non-volatile electronically programmable reversible variable resistance device

A solid-state variable resistance device (10) whose resistance can be repeatedly altered by a control signal over a wide range, and which will remain stable after the signal is removed, is formed on an insulated layer (14), supported on a substrate (12) and comprises a set of electrodes (16a, 16b) connected by a layer (18) of material, which changes from an insulator to a conductor upon the injection of ions, covered by a layer (22) of material with insulating properties which permit the passage of ions, overlaid by an ion donor material (20). The ion donor material is overlaid by an insulating layer (24) upon which is deposited a control gate (26) located above the contacts. In a preferred embodiment, the variable resistance material comprises WO.sub.3, the ion donor layer comprises Cr.sub.2 O.sub.3, and the layers sandwiching the ion donor layer comprise silicon monoxide. When a voltage is applied to the gate, the resistance between the electrode contacts changes, decreasing with positive voltage and increasing with negative voltage.

Ramesham, Rajeshuni↗
DOE OSTI2018

Variable charge transfer state energies at nanostructured pentacene/C 60 interfaces

While it has recently been recognized that organic donor-acceptor charge transfer (CT) state energies can vary substantially under different interfacial morphologies, this behavior is under-appreciated in the context of organic singlet fission solar cells where a specific alignment between the triplet state of the fission material and the CT state of the donor-acceptor interface is necessary to the function of the device. Here, in this work, we demonstrate that the CT state energy of a prototypical pentacene-C 60 singlet fission system is around 1 eV in most systems, but can vary over 300 meV depending on the composition and morphology of the interface. Moreover, we show that the inclusion of a poly(3-hexylthiophene-2,5-diyl) underlayer, which commonly serves as a triplet blocker and hole collector in pentacene/C 60 solar cells, helps promote active layer morphologies with stabilized, low energy CT states. Lastly, these trends in the interfacial energetics are correlated with structural characterization of the films by atomic force microscopy and x-ray diffraction.

36 MATERIALS SCIENCE↗