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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Structure- and Physics-Preserving Reductions of Power Grid Models

The large size of multiscale, distribution and transmission, power grids hinders fast systemwide estimation and real-time control and optimization of operations. This paper studies graph reduction methods of power grids that are favorable for fast simulations and follow-up applications. While the classical Kron reduction has been successful in reduced order modeling of power grids with traditional, hierarchical design, the selection of reference nodes for the reduced model in a multiscale, distribution and transmission, network becomes ambiguous. In this work we extend the use of the iterative Kron reduction by utilizing the electric grid's graph topology for the selection of reference nodes, consistent with the design features of multiscale networks. Additionally, we propose further reductions by aggregation of coherent subnetworks of triangular meshes, based on the graph topology and network characteristics, in order to preserve currents and build another power-flow equivalent network. Our reductions are achieved through the use of iterative aggregation of subgraphs that include general tree structures, lines, and triangles. Important features of our reduction algorithms include that (i) the reductions are either equivalent to the Kron reduction or otherwise produce a power-flow equivalent network; (ii) due to the mentioned power-flow equivalence, the reduced network can model the dynamic of the swing equations for a lossless, inductive, steady state network; (iii) the algorithms efficiently utilize hash tables to store the sequential reduction steps. The third feature allows for easy reintroduction of detailed models into the reduced, conceptual network and makes the final reduced order model backward compatible with a sequence of intermediate, partially reduced networks with varying resolution---the ordered sequence of iterative reductions corresponds to a sequence of reduced order models. The performance of our graph reduction algorithms and features of the reduced grids are discussed on a real-word transmission and distribution grid. We produce visualizations of the reduced models through open source libraries and release our reduction algorithms with example code and toy data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Integrated Models for Electrical Distribution Network Planning and District-Scale Building Energy Use

The increase of greenhouse emissions caused by a rise in global energy consumption is pushing the scientific community to develop tools that address key environmental, techno-economic and social issues. One specific area of focus is to create tools that enable the design of high-performance energy districts, including grid-interactive efficient buildings and electrical distribution infrastructure. This paper outlines the approach of integrating a synthetic distribution network tool (RNM-US) with URBANopt™, a modular and customizable software development kit for thermal and electrical modelling of buildings and energy systems at a district scale. First, we describe the algorithms implemented to integrate RNM-US with URBANopt to automatically generate the distribution network for the district considered. Following, we present a case study for the potential uses of the RNM-US integrations with URBANopt, highlighting the capabilities for economic and technical analysis of the distribution network built in relation to the electricity needs of the modelled buildings.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1986

An optimization model for the US Air-Traffic System

A systematic approach for monitoring U.S. air traffic was developed in the context of system-wide planning and control. Towards this end, a network optimization model with nonlinear objectives was chosen as the central element in the planning/control system. The network representation was selected because: (1) it provides a comprehensive structure for depicting essential aspects of the air traffic system, (2) it can be solved efficiently for large scale problems, and (3) the design can be easily communicated to non-technical users through computer graphics. Briefly, the network planning models consider the flow of traffic through a graph as the basic structure. Nodes depict locations and time periods for either individual planes or for aggregated groups of airplanes. Arcs define variables as actual airplanes flying through space or as delays across time periods. As such, a special case of the network can be used to model the so called flow control problem. Due to the large number of interacting variables and the difficulty in subdividing the problem into relatively independent subproblems, an integrated model was designed which will depict the entire high level (above 29000 feet) jet route system for the 48 contiguous states in the U.S. As a first step in demonstrating the concept's feasibility a nonlinear risk/cost model was developed for the Indianapolis Airspace. The nonlinear network program --NLPNETG-- was employed in solving the resulting test cases. This optimization program uses the Truncated-Newton method (quadratic approximation) for determining the search direction at each iteration in the nonlinear algorithm. It was shown that aircraft could be re-routed in an optimal fashion whenever traffic congestion increased beyond an acceptable level, as measured by the nonlinear risk function.

Mulvey, J. M.↗
DOE OSTI2019

A Novel Equivalent Model of Active Distribution Networks Based on LSTM

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. Here, it reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

42 ENGINEERING↗
DOE OSTI2024

An Efficient Annual-Performance Model of a Geothermal Network for Improved System Design, Operation, and Control: Preprint

Geothermal district energy systems (DES), and specifically geothermal networks, provide a viable solution for decarbonizing residential and commercial heating and cooling loads. District energy systems of all kinds enable a thermal resource with a relatively high capital cost (such as a geothermal borehole field) to be shared among a large number of users. While district heating and cooling has been studied for many years, geothermal networks, fifth generation DES that utilize water-source heat pumps and an ambient temperature loop to meet heating and cooling loads, have not been implemented extensively and thus require additional technical and economic optimization to obtain maximum benefits. This paper presents a newly developed reduced-order model that captures the flow of energy within the network, including the commercial and residential users' electrical usage, at an hourly rate over a year. The model includes building loads, heat pumps, borehole fields, and auxiliary heat/cool input, all connected with an ambient-temperature thermal loop model. In the model, operational control is possible for the borehole fields, circulation pump, and auxiliary system. For a given system, the model can output the complete state parameters for each component, the thermal loop, and the collective system, such as flow rate over time, average thermal loop temperature over time, and total electricity usage. The model can also be used to optimize the system control for maximizing system efficiency or minimizing system operational cost. For example, one initial assessment of the borehole controller for an example system showed that a controller with on/off operation of the borehole field reduces annual electrical usage by 33%, compared with continuous operation mode. Hence, the model can assist in optimizing a given system's operation to get the most value out of a geothermal network installation. Future work will consider the model's application to a demonstration project, including the model validation against operational data and system operation optimization.

ambient-temperature loop↗
DOE OSTI2023

Statistical analysis and degradation pathway modeling of photovoltaic minimodules with varied packaging strategies

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems.

electrical measurements↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2025

Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks

Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores. This method makes it easier to interpret the movement of the protein complex from heterogeneity analysis and to compare the structural dynamics observed from CryoEM data with results from other experimental and simulation techniques.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Experimental demonstration of real-time electron temperature profile control in DIII-D

Future tokamak reactor operation will require the ability to maintain a given plasma scenario for extended periods of time. This will necessitate the capability to react to changes in the plasma state and return the plasma to the target scenario; the principal method to achieve this is through feedback control. Thus, it is necessary to develop and test feedback controllers for the plasma profiles that define a target scenario. In this work, a feedback controller for the electron temperature (Te) profile is tested experimentally in DIII-D. This experiment relied on the ability to ascertain the electron temperature profile in real time, which was achieved using an observer algorithm. The observer relies on both diagnostic data and a predictive model of the electron temperature profile evolution; this predictive model includes contributions from neural network surrogate models. Because of these dependencies, a number of capabilities needed to be added to the real-time PCS for DIII-D in order to support the Te profile control experiment. The neural network surrogates needed to be integrated into the PCS to be called in real time. An observer algorithm for the Te profile needed to be added and connected to the Thomson scattering system to allow access to the current state of the profile in real time. When tested, the observer was shown to produce Te profiles that are consistent with the shape of the Thomson scattering data while rejecting much of the noise in the diagnostic data. Finally, the controller itself was tested in real time. This experiment showed that the controller is capable of tracking the electron temperature target at locations across the spatial profile.

Morosohk, Shira [Oak Ridge Associated Universities↗
DOE OSTI2024

POPSnet-SGP: A Pilot Aerosol Microphysics Network for Targeting Climate Model Uncertainty Interim Field Campaign Report

Aerosols mediate the radiative fluxes in clear and cloudy skies and dominate the uncertainty in the radiative forcing of climate. Relatively dense networks of aerosol optical depth measurements have been used to effectively constrain simulated aerosol optical properties, but model diversity of aerosol microphysical properties is much larger (Mann et al. 2014, Myhre et al. 2009). A better understanding of aerosol microphysics is essential as they are the fundamental pieces of information required to convert aerosol emissions information to radiative and cloud-nucleating properties that drive radiative effects and forcing of climate. Model representation of aerosol microphysical properties has lagged in part due to their inherently greater spatial variability, but also due to a lack of available observations. This project – the Printed Optical Particle Spectrometer Network (POPSnet)-Southern Great Plains (SGP) Pilot – launched the first spatially dense network of aerosol size distribution measurements over an area the size of a global model grid cell and demonstrated its use in providing valuable information regarding the spatial variability of aerosol microphysical properties and its drivers for improving model constraints.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2020

Composite Piezoelectric Resonator 1D Modeling with Loss

Abstract – Maximizing the benefit of applying piezoelectric materials in composite structures, which are a mix of piezoelectric and elastic elements, requires analytical modeling. In this paper we describe two main classes of resonators and how to model using network equivalent including material losses. These include extensional devices where the layer areas are parallel to the direction of wave travel as found in a variety of thickness or length mode transducers (BC. Stress T1 = T2 at boundary) and transverse mode resonators (BC. Strain S1 = S2 at boundary) where the major layer area is perpendicular to the wave direction as is found in radial mode or length thickness mode composite resonators. In order to illustrate these models we will use Mason’s equivalent circuits with elastic, dielectric and piezoelectric loss. It is noted that in order to maintain consistency with the linear equations of piezoelectricity and the wave equation care is required when applying complex loss coefficients to the models. Although these techniques are applicable to other models we use Mason’s network equivalent circuit because it is intuitive for composite modeling due to the frequency independent turns ratio and the independence of the equations describing the elastic layers from the adjoining layer properties which aids in understanding the boundary conditions to impose on the layer boundaries. We will present network models for a variety of applications with loss and show how to calculate their impedance spectra, interface stress, strain velocity and displacement spectra.

Sherrit, Stewart↗
DOE OSTI2021

Efficient Contingency Analysis in Power Systems via Network Trigger Nodes

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

Tabassum, Anika↗
NASA NTRS1985

Integrated risk/cost planning models for the US Air Traffic system

A prototype network planning model for the U.S. Air Traffic control system is described. The model encompasses the dual objectives of managing collision risks and transportation costs where traffic flows can be related to these objectives. The underlying structure is a network graph with nonseparable convex costs; the model is solved efficiently by capitalizing on its intrinsic characteristics. Two specialized algorithms for solving the resulting problems are described: (1) truncated Newton, and (2) simplicial decomposition. The feasibility of the approach is demonstrated using data collected from a control center in the Midwest. Computational results with different computer systems are presented, including a vector supercomputer (CRAY-XMP). The risk/cost model has two primary uses: (1) as a strategic planning tool using aggregate flight information, and (2) as an integrated operational system for forecasting congestion and monitoring (controlling) flow throughout the U.S. In the latter case, access to a supercomputer is required due to the model's enormous size.

Mulvey, J. M.↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2025

Anomaly Detection On Network Traffic Using A Sequence Model Approach

The code ingests Zeek logs derived from network packet captures, applies a DBSCAN model, Gower distance to build a feature set as input into a transformer model that will output anomaly scores and provides the option to set a threshold as to what’s considered an anomaly.

Quach, Anna [Idaho National Laboratory (INL), Idah↗
DOE OSTI2021

On the Use of Smart Meter Data to Estimate the Voltage Magnitude on the Primary Side of Distribution Service Transformers

This paper develops a novel method to estimate the voltage magnitude on the primary side of distribution service transformers. The proposed method relies exclusively on smart meters, and therefore it is fully data-driven. This is an important feature because electric utilities have detailed models of only the primary network - that is, the network between the distribution substation and the primary side of service transformers that are installed closer to end-customer sites. The network that connects the secondary side of service transformers to end-customer sites, referred to as the secondary network, is simply represented by a lumped load. For each secondary network, the proposed method uses data acquired from only 2 smart meters: the closest and the farthest-in the sense of electrical distance - from the service transformer. As a reference to this feature, the proposed method is named SM2Vp. To our knowledge, this is the first time a method is shown to provide actionable information for realtime operation and control of power distribution grids using only two smart meters per secondary network. This is important because utilities have experienced barriers in managing and using large data sets for real-time operation and control. SM2Vp is primarily intended to provide pseudo-measurements for distribution system state estimation, but it can also be used directly for voltage control schemes. The performance of SM2Vp is demonstrated by numerical simulations carried out on three secondary network synthetic models and by using field data provided by a utility partner serving customers in southwestern California. A maximum relative error of approximately 3.9% or less is observed for the primary voltage magnitude estimates in all numerical experiments.

distribution service transformer↗