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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications

There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Single-layer tensor network study of the Heisenberg model with chiral interactions on a kagome lattice

Here, we study the antiferromagnetic kagome Heisenberg model with an additional scalar-chiral interaction by using the infinite projected entangled-pair state (iPEPS) ansatz. We discuss in detail the implementation of the optimization algorithm in the framework of the single-layer tensor network based on the corner transfer matrix technique. Our benchmark based on the full-update algorithm shows that the single-layer algorithm is stable, which leads to the same level of accuracy as the double-layer ansatz but with much less computation time. We further apply this algorithm to study the nature of the kagome Heisenberg model with a scalar-chiral interaction by computing the bond dimension scaling of magnetization, bond energy difference, chiral order parameter, and correlation length. In particular, we find that for strong chiral coupling the correlation length, which is extracted from the transfer matrix, saturates to a finite value for large bond dimension, representing a gapped spin-liquid state. Further comparison with density matrix renormalization group results supports that our iPEPS faithfully represents the time-reversal symmetry breaking chiral state. Our iPEPS simulation results shed new light on constructing PEPS for describing gapped chiral topological states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Fantômas unconfined: global QCD fits with Bézier parameterizations

Fantômas is a C++ toolkit for exploring the parametrization dependence of parton distribution functions (PDFs) and other correlator functions in quantum chromodynamics (QCD). Fantômas facilitates the generation of adaptable polynomial parametrizations for PDFs, called metamorphs, to find best-fit PDF solutions and quantify the epistemic uncertainty associated with the parametrizations during their fitting. The method employs Bézier curves as universal approximators for a variety of PDF shapes. Integrated into the xFitter framework for the global QCD analysis, Fantômas provides a foundation for general models of PDFs, while reducing the computational time compared to the approaches utilizing traditional polynomial parametrizations as well as providing an interpretable alternative to neural-network-based models. This paper outlines the structure and practical usage of the Fantômas toolkit, including its inputs, outputs, and implementation within xFitter. It also provides a practical example of using Fantômas for uncertainty quantification as well as the combination of PDF fits into a single ensemble.

Bézier curves↗
DOE OSTI2019

Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram-Based Machine Learning Approach

Abstract The capability to discriminate low-magnitude earthquakes from low-yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. Here, we used a dataset of seismic events in Utah recorded during a 14-day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes M C ranging from –2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining-induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg-to-Sg phase ARs and Rg-to-Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML technique used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. Furthermore, we compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal-to-noise ratio data, allowing them to classify significantly smaller events.

58 GEOSCIENCES↗
NASA NTRS1992

Space-Time Neural Networks

Concept of space-time neural network affords distributed temporal memory enabling such network to model complicated dynamical systems mathematically and to recognize temporally varying spatial patterns. Digital filters replace synaptic-connection weights of conventional back-error-propagation neural network.

Villarreal, James A.↗
DOE OSTI2024

Wormholes without averaging

After averaging over fermion couplings, SYK has a collective field description that sometimes has “wormhole” solutions. We study the fate of these wormholes when the couplings are fixed. Working mainly in a simple model, we find that the wormhole saddles persist, but that new saddles also appear elsewhere in the integration space — “half-wormholes.” The wormhole contributions depend only weakly on the specific choice of couplings, while the half-wormhole contributions are strongly sensitive. The half-wormholes are crucial for factorization of decoupled systems with fixed couplings, but they vanish after averaging, leaving the non-factorizing wormhole behind.

1/N Expansion↗
NASA NTRS1988

Neural networks with hierarchically correlated patterns

The Hopfield model of neural networks is extended to allow for the storage and retrieval of hierarchically correlated patterns. The overlaps between these patterns form an ultrametric tree. Intermediate states, which serve as ancestors for the following levels, are generated at each level of the tree. The states belonging to each level are stored, by a modified learning rule, in a series of identical networks, one for each level. The retrieval of a particular pattern is preceded and assisted by the successive retrieval of its ancestors. The performance of this scheme is studied analytically and numerically.

Gutfreund, H.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32188-DND

Engineers rely on a wide variety of modern thermal tools to model thermal problems; many of these tools offer graphical “front ends” whereby users formulate their analytical models using a CAD interface. Behind the scenes, the analysis is performed on a thermal network – this is true whether the analyst uses finite differencing or finite element methodologies. While graphical front ends are very powerful, understanding the resulting thermal network representation used for a model gives the user the ability to check or even modify the models at a basic level. Additionally, there are instances where an engineer may prefer to develop network models from scratch or use heritage code that does not have a graphical front end. Some front-end programs output thermal networks in the widely used SINDA format; our lesson will focus on this input format.

Steven L Rickman↗
DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow: Preprint

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. In this report we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO–LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO–LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20–30 features. Analysis of the essential descriptors for HOMO level and HOMO–LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveal the paramount importance of nonlocal, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO–LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as Internet of Things (IoT), edge computing, and usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN’s (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optics. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded to fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. In conclusion, the development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Application of a Geochemically Informed Leak Detection (GILD) Model to CO 2 Injection Sites on the United States Gulf Coast

The Gulf Coast region possesses great potential for CO 2 enhanced oil recovery (EOR) and CO 2 storage. A geochemically informed leak detection (GILD) model has been applied to CO 2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO 2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO 2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca 2+ or HCO 3 - is used as the monitoring parameter, but only for aquifers with calcite.

58 GEOSCIENCES↗
DOE OSTI2017

Cascading Failures in Interdependent Networks with Multiple Supply-Demand Links and Functionality Thresholds

Various social, financial, biological and technological systems can be modeled by interdependent networks. It has been assumed that in order to remain functional, nodes in one network must receive the support from nodes belonging to different networks. So far these models have been limited to the case in which the failure propagates across networks only if the nodes lose all their supply nodes. In this paper we develop a more realistic model for two interdependent networks in which each node has its own supply threshold, i.e., they need the support of a minimum number of supply nodes to remain functional. In addition, we analyze different conditions of internal node failure due to disconnection from nodes within its own network. We show that several local internal failure conditions lead to similar nontrivial results. When there are no internal failures the model is equivalent to a bipartite system, which can be useful to model a financial market. We explore the rich behaviors of these models that include discontinuous and continuous phase transitions. Using the generating functions formalism, we analytically solve all the models in the limit of infinitely large networks and find an excellent agreement with the stochastic simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Can Convolution and Deconvolution Be Used as Tools for Modeling Multicomponent, Mixing-Limited Reaction Networks?

Transfer functions (i.e., convolutions) have been powerful tools for both forward modeling and reconstructing past states of transport systems in a wide range of subsurface flow problems. The advantage of the transfer function is that it is a simple alternative to complicated, distributed parameter models of flow and transport, but the majority of applications of transfer functions in hydrology have been limited to relatively simple cases, like passive tracers or first-order decay. The central question evaluated in this note is whether or not multicomponent mixing-limited reactive transport can be represented within a transfer function framework. Our examples consider forward-in-time predictions and backward-in-time reconstructions of a carbonate system that represents the intrusion of seawater into a freshwater aquifer. The main result is that accurate forward-in-time and backward-in-time models are developed by posing the problem in terms of conservative components. As with all convolution-based methods, the results are sensitive to errors and/or noise in the input functions, but we show that smoothed approximations of the requisite functions provide good representations of transport. Given the vast unknowns in any subsurface transport problem, such generalized, reactive transfer function models may have yet unexplored advantages when the trade-offs between overall computational cost, accuracy, and uncertainty are explored in more detail.

42 ENGINEERING↗
DOE OSTI2019

A Multidimensional Holistic Framework for the Security of Distributed Energy and Control Systems

The digitization of smart grid distributed generation and industrial control systems has prompted utilities to deploy tools with ubiquitous communications that potentially widen the attack surface. The utilities still continue to rely on the traditional cybersecurity technologies, such as firewalls, anti-malware tools, and passwords that do not ensure security across all dimensions of the information assurance model required for a strong cybersecurity business process. This paper proposes a multidimensional holistic framework that addresses this gap through advanced technologies, intelligent algorithms, and continued assessments. To show proof, the layered defense model, a solution dimension of the framework, is integrated into the National Renewable Energy Laboratory's Security and Resilience Testbed to replicate a utility's enterprise and substation networks. The model is used to evaluate the security and resilience of microgrid control systems, and, based on the insights gathered, recommend best practices for utility cybersecurity analysts for a strong business process. Here, with this baseline, the paper conceptually introduces intelligence-driven solutions comprising contextual data analysis and machine learning to respond to advanced persistent threats sponsored by campaign efforts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob↗