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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Supply Chain-Based Solution to Prevent Fuel Tax Evasion: Final Report

The primary source of funding for the United States transportation system is derived from motor fuel and other highway use taxes. Loss of revenue attributed to fuel-tax evasion has been assessed to be somewhere between 1 billion and 3 billion per year. Any solution that addresses this problem needs to include not only the tax-collection agencies and auditors, but also the carriers transporting oil products and the carriers customers. This report presents a system developed by the Oak Ridge National Laboratory (ORNL) for the Federal Highway Administration which has the potential to reduce or eliminate many fuel-tax evasion schemes. The solution balances the needs of tax-auditors and those of the fuel-hauling companies and their customers. The system has three main components. The on-board subsystem combined sensors, tracking and communication devices, and software (the on-board Evidential Reasoning System, or obERS) to detect, monitor, and geo-locate the transfer of fuel among different locations. The back office sub-system (boERS) used self-learning algorithms to determine the legitimacy of the fuel loading and offloading (important for tax auditors) and detect potential illicit operations such as fuel theft (important for carriers and their customers, and may justify the deployment costs). The third sub-system, the Fuel Distribution and Auditing System or FDAS, is a centralized database, which together with a user interface allows tax auditors to query the data submitted by the fuel-hauling companies and correlate different parameters to quickly identify any anomalies. Industry partners included Barger Transport of Weber City, Virginia (fleet); Air-Weigh, of Eugene, Oregon (and their wires and harnesses); Liquid Bulk Tank (LBT) of Omaha, Nebraska (three five-compartment trailers); and Innovative Software Engineering (ISE) of Coralville, Iowa(on-board telematics device and back-office system). ORNL conducted a pilot test with the three instrumented vehicles collecting real-world data during an eight-month period (October 2014 to June 2015). The vehicles logged a total of 375,000 miles and transported more than 7.5 million gallons of fuel. The drivers entered information on the telematics device about 77% of the time, with about 15% (10%) of the data showing at least one compartment with loading (offloading) valve activity, but not driver information entry for that compartment. Seven events (or about 0.7% of the trips) were identified by the boERS as having a missing fuel diversion number. The solution developed and tested in the pilot test had federal- and state-level tax auditors as its main audience. Nonetheless, the technology has to be adopted by the fuel carriers and therefore the solution had to address the needs of fuel-hauling companies and their customers (i.e., fuel theft and cocktailing). Sensors in the hatches allowed the obERS recognize when one of those were opened (always a suspicious activity, unless it happens at locations where maintenance is performed on the vehicle). The fuel-theft issue was addressed by a self-learning algorithm deployed on the boERS that continuously processed the data from the field to construct probability distributions of measures such as elapsed time of fuel loading and offloading by driver, vehicle, and compartment, valve actuation sequence, elapsed time between the first two valve actuations (by driver, compartment, and location), and other parameters. Probability thresholds, which can be set up by the carrier, determined how to classify the observed events. The boERS also kept track of valve sequencing at a given location and analyzed these actuations to help identify any suspicious activities. Technical and economic recommendations include: a) simplification of the driver data-entry task; b) reduction of system deployment cost; c) addition of capabilities that make the system more appealing to industry (i.e., identify and avoid fuel misdelivery); and d) incorporation of additional capabilities to the FDAS. Path to commercialization recommendations include: a) identify a company willing to further develop, test, and certify a hardened system with a price point the market will bear; b).transfer ERS to licensee; c) establish a fleet or fleets who want this technology for carrier benefits or trailer manufacturer who wants to offer it as optional technology; and d) identify a location for the deployment of FDAS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗
DOE OSTI2020

Nonlinear sparse Bayesian learning for physics-based models

This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. Here, a semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

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

Extraction of Drell-Yan Angular Parameters in $pp$ Collisions with a 120 GeV Beam Energy Using a Deep-Learning Unfolding Algorithm

Dilepton production in pp collisions through the Drell-Yan process provides a crucial tool for studying the internal quark-gluon structure of the nucleon. By precisely measuring the $\cos2\phi$ asymmetry, where $\phi$ represents the azimuthal angle of the $l^{+}l^{-}$ pair in the Collins-Soper frame, we can gain valuable insights into the proton’s structure and the transverse momentum ($q_{T}$) dependence of the $\cos2\phi$ asymmetry. SeaQuest, a fixed-target Drell-Yan experiment at Fermilab, involved an unpolarized proton beam colliding with unpolarized LH$_{2}$ and LD$_{2}$ targets. Measurements obtained from experiments typically require corrections for detector inefficiencies, smearing, and acceptance. Traditionally, these corrections involve “unfolding” the detector-level measurements through matrix operations. However, in higher-dimensional phase space, these conventional methods fail to scale effectively. To overcome these limitations, we employ an unbinned unfolding method that utilizes deep neural networks for unfolding higher-dimensional phase space. In this presentation, we will explain the design of the neural network architecture, our training strategies, and outline our plans to achieve conclusive results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2010

Algorithms for Learning Preferences for Sets of Objects

A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features.

Wagstaff, Kiri L.↗
NASA NTRS1991

Learning classification trees

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

Buntine, Wray↗
DOE OSTI2022

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-driven activation and bio-inspired learning algorithms. SNN-based machine learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called a crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of the Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine learning programs. Our results demonstrate that DFSynthesizer provides a much tighter performance guarantee compared to current mapping approaches.

Computer Science↗
DOE OSTI2025

Decision and Control of Complex Systems – A Data-Drive Framework

During the project period, we have collaborated with other team members and developed novel algorithms for novelty detection, continual learning, and graph learning algorithms for dynamic systems. The results are documented in publications and meeting notes. Moreover, we leverage virtual collaboration tools (such as Basecamp, Microsoft Teams and Zoom) for technical exchanges. Our research on novelty detection was published at AAAI 2022 and Lecture Notes in Artificial Intelligence, Springer Nature. The newly developed algorithms were successfully applied to realistic cases, including thermal data from buildings at Pacific Northwest National Lab and microelectronic data provided by GlobalFoundries. Multiple publications have been produced from this project, in collaboration with other team members. Three PhD students were supported in this project to conduct their research.

42 ENGINEERING↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗