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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Advances in cyber–physical technologies have enabled real-time sensing and adaptive control of stormwater infrastructure. These smart stormwater systems allow for inexpensive, minimally-invasive stormwater control interventions in lieu of new construction. Importantly, however promising the area of smart stormwater control, there still remain barriers – for experts and novices alike – to access shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical and accessible, we present pystorms, an open-source Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. pystorms consists of a collection of real world-inspired smart stormwater control scenarios on which any number of control strategies can be applied and tested via an accompanying Python programming interface and coupled stormwater simulator. pystorms provides a framework for the rigorous and efficient evaluation of smart stormwater control methodologies across diverse watersheds with only a few lines of code.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Scalable Predictive And Risk Technologies

The research involves developing scalable technologies for risk-informed predictive analytics to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop technologies to scale risk-informed predictive analytic algorithms across different plant assets at the plant site and across the nuclear fleet. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation using plant operation data 2. Federated and Transfer learning model 3. Feature group based Multi-kernel SVM 4. Three state markov model

Manjunatha, KoushikAraseethota↗
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Open Source Fault-tolerant Grid Frequency Measurement for Solar Inverters

The Discrete Fourier transform (DFT) based measurement algorithms are one of the most common measurement algorithms for grid parameter estimation such as rms, phase angle, frequency. Over the past few years, many DFT based algorithms have been developed to enhance its measurement accuracy under steady-state and/or dynamic grid conditions. For example, an adaptive band-pass filter utilizing exponential modulation filter has been proposed to reduce measurement errors at the presence of large frequency deviations. Measurement accuracy of different algorithms including FIR filter, extended Kalman filtering (EKF), and enhanced DFT method have been compared in detail under different grid conditions. Two artificial signals that have 90-degree phase difference were constructed by the Clarke transformation to address the frequency spectrum leakage of DFT. A multi-module approach was developed to enhance both steady-state and dynamic measurement accuracies, in which each module was developed to eliminate some specific errors. Besides DFT-based measurement algorithms, some signal model-based algorithms have been developed to further improve the accuracy under dynamic conditions. However, a key drawback of the state-of-the-art algorithms is that they cannot perform measurements accurately during system transient faults. In the Blue Cut Fire event, there was a phase angle jump of about 26 degrees in the voltage waveform during the transient fault. The phase angle jump fault will cause waveform discontinuity, and these algorithms will fail to provide reliable measurements during this period because they typically assume the waveform to be measured is continuous, no matter what method (DFT, PLL, EKF, FIR, or Taylor WLS) is used for estimation. In fact, the measurement errors during the system transient faults like phase-jump is not required in the IEEE Standard. As a result, although a measurement instrument can pass the strict IEEE Standard, it could still be the source of the problem in the future if we have similar system transient faults, which could happen again. Therefore, developing the fault-tolerant measurement technology is the key to solve the problem.

14 SOLAR ENERGY↗
DOE OSTI2020

Pattern-matching indexing of Laue and monochromatic serial crystallography data for applications in materials science

Serial crystallography data can be challenging to index, as each frame is processed individually, rather than being processed as a whole like in conventional X-ray single-crystal crystallography. An algorithm has been developed to index still diffraction patterns arising from small-unit-cell samples. The algorithm is based on the matching of reciprocal-lattice vector pairs, as developed for Laue microdiffraction data indexing, combined with three-dimensional pattern matching using a nearest-neighbors approach. As a result, large-bandpass data (e.g. 5–24 keV energy range) and monochromatic data can be processed, the main requirement being prior knowledge of the unit cell. Furthermore, angles calculated in the vicinity of a few theoretical and experimental reciprocal-lattice vectors are compared, and only vectors with the highest number of common angles are selected as candidates to obtain the orientation matrix. Global matching on the entire pattern is then checked. Four indexing options are available, two for the ranking of the theoretical reciprocal-lattice vectors and two for reducing the number of possible candidates. Furthermore, the algorithm has been used to index several data sets collected under different experimental conditions on a series of model samples. Additionally, knowing the crystallographic structure of the sample and using this information to rank the theoretical reflections based on the structure factors helps the indexing of large-bandpass data for the largest-unit-cell samples. For small-bandpass data, shortening the candidate list to determine the orientation matrix should be based on matching pairs of reciprocal-lattice vectors instead of triplet matching.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data-driven profile prediction for DIII-D

A new, fully data-driven algorithm has been developed that uses neural networks to predict plasma pro les on a scale of τ E into the future given actuators and the present plasma state. The model was trained and tested on DIII-D data from the 2013-2018 experimental campaigns. The model is accurate on average, with q predictions the worst and pressure predictions the best. The model can run in milliseconds and is very simple to use. This makes it a potentially useful tool for operators and physicists when planning plasma scenarios. It also is a candidate for doing phase-space exploration without going through the DIIID database or complicated and computationally expensive simulation codes. Here, a reduced model using only realtime diagnostics has also been developed and formed the basis for a model-predictive control algorithm implemented and successfully tested on DIII-D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

AdenPredictor: accurate prediction of the adenylation domain specificity of nonribosomal peptide biosynthetic gene clusters in microbial genomes

Microbial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

On Distribution Grid Optimal Power Flow Development and Integration

Due to changes in electric distribution grid operation, new operation regimes have been recommended. Distribution grid optimal power flow (DOPF) has received tremendous attention in the research community, yet it has not been fully adopted across the utility industry. Our paper recognizes this problem and suggests a development and integration procedure for DOPF. We propose development of DOPF as a three step procedure of 1) processing the grid, 2) obtaining a tractable solution, and 3) implementing multiple solution algorithms and benchmarking them to improve application reliability. For the integration of DOPF, we demonstrate how a DOPF federate may be developed that can be integrated in a co-simulation environment to mimic the real-world conditions and hence improve its practicality to be deployed in the field. To demonstrate the efficacy of the proposed methods, tests on IEEE 123 bus system are performed where the usage of tractable formulation in DOPF algorithm development and its comparison to the benchmark solution are demonstrated.

Hanif, Sarmad↗
DOE OSTI2020

A Geometric Measure Theory Approach to Identify Complex Structural Features on Soft Matter Surfaces

The structural features that protrude above or below a soft matter interface are well-known to be related to interfacially mediated chemical reactivity and transport processes. It is a challenge to develop a robust algorithm for identifying these organized surface structures, as the morphology can be highly varied and they may exist on top of an interface containing significant interfacial roughness. A new algorithm that employs concepts from geometric measure theory, algebraic topology, and optimization is developed to identify candidate structures at a soft matter surface, and then, using a probabilistic approach, to rank their likelihood of being a complex structural feature. The algorithm is tested for a surfactant laden water/oil interface, where it is robust to identifying protrusions responsible for water transport against a set identified by visual inspection. To our knowledge, this is the first example of applying geometric measure theory to analyze the properties of a chemical/materials science system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Simulating the Autonomous Future: A Look at Virtual Vehicle Environments and How to Validate Simulation Using Public Data Sets

The rapid evolution of autonomous vehicles (AVs) has exposed the need for fast-paced development and testing processes of a variety of perception, planning, and control algorithms. To expedite development, the AV industry and researchers leverage virtual vehicle environments to simulate a range of test scenarios that may otherwise be costly or difficult to conduct on a real test track. However, the various virtual environments may have different results depending on the fidelity of various simulation features, such as vehicle dynamics, sensor simulation, and environment recreation. Herein, this tutorial article examines a proposed framework for constructing, parameterizing, and validating a virtual vehicle environment using an existing AV data set. First, an overview of several open source and commercially available simulation tools, including their associated workflows, for scene and scenario creation is presented. Next, various open AV data sets are examined to inform the data set selection for the validation framework. Then, an example workflow of recreating a real-world scene from the selected data set in a simulation tool with various emulated sensors parameterized to match the data set is demonstrated. Finally, an example AV-perception algorithm is subjected to data streams from virtual and real-world environments and suggested metrics for analyzing the results are discussed.

42 ENGINEERING↗
DOE OSTI2021

Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

Atmospheric rivers (ARs) are long and narrow filaments of vapor transport that are responsible for most poleward moisture transport outside of the tropics. Many AR detection algorithms have been developed to automatically identify ARs in climate data. The diversity of these algorithms has introduced appreciable uncertainties in quantitative measures of AR properties and thereby impedes the construction of a unified and internally consistent climatology of ARs. This paper compares nine global AR detection algorithms from the perspective of AR lifecycles following the propagation of ARs from origin to termination in the MERRA2 reanalysis over the period 1980–2017. Uncertainties in AR lifecycle characteristics, including event number, lifetime, intensity, and frequency distribution are discussed. Notably, the number of AR events per year in the Northern Hemisphere can vary by a factor of 5 with different algorithms. Although all algorithms show that the maximum AR origin (termination) frequency is located over the western (eastern) portion of ocean basins, significant disagreements appear in regional distribution. Spreads are large in AR lifetime and intensity. Here, the number of landfalling AR events produced by the algorithms can vary from 16 to 80 events per year, although the agreement improves for stronger ARs. By examining the ARs' connections with the Madden-Julian Oscillation and El Niño Southern Oscillation, we find that the overall responses of ARs (such as changes in AR frequency, origin, and landfall activity) to climate variability are consistent among algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Sea Breezes and Associated Convective Cloud Evolution in the Model Gray Zone

We characterize convective clouds associated with sea‐breeze circulations (SBC) using multi‐agency observations and multi‐case ensemble model simulations. The focus is on assessing convective cloud lifecycle properties and their merging behavior, as well as the environmental conditions they are embedded in, particularly SBC features. In total, 46 SBC days over the Houston‐Galveston region are selected and simulated using the Weather Research and Forecasting (WRF) model at a gray zone scale with a forecast‐like parameterization setup. Advanced techniques, including change‐point detection, a Lagrangian cloud tracking method, and a newly developed cell merging and splitting detection algorithm, are applied and/or developed for this study. Our findings indicate that the WRF model at 1 km grid spacing well represents the thermodynamic conditions over the region, as well as SBC timing and intensity. However, for the associated convective cells, WRF overestimates the 30‐dBZ echo top height, cell area, and maximum radar reflectivity compared to radar observations. This overestimation is potentially due to under‐resolved entrainment processes, an overestimated merging frequency, and the overestimation of updraft intensity. Furthermore, the model exhibits a deficiency in simulating congestus clouds, showing a more rapid transition from shallow to deep convection compared to observed behavior. Moreover, observations indicate stronger, deeper, and wider clouds when merging happens. Conversely, in simulations, the merging process does not necessarily lead to higher or longer‐lived cells, as many cases experience rapid and frequent merging and splitting which may result in more variance in convective updraft velocity during the convection lifetime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Slicing with deep learning models at ProtoDUNE-SP

DUNE is a cutting-edge experiment aiming to study neutrinos in detail, with a special focus on the flavor oscillation mechanism. The prototype of the DUNE Far Detector Single Phase TPC (ProtoDUNE-SP) was built and operated at CERN with a full set of reconstruction tools. To implement these reconstruction tools, Pandora, a multi-algorithm framework, has been developed. A large number of these algorithms, some of them being exploiting traditional clustering, detector physics and deep learning approaches, have been applied to images to gradually build up a picture out of singular events. One of such algorithms is the Pandora slicing algorithm which aims to partition the detector hits of an event in sets called slices. Each slice represents a single interaction in the detector and should identify all the hits related to the interacting particle and its subsequent decay products. We expect the order of tens of slices per event in ProtoDUNE-SP. In this paper we present a deep learning approach to the problem, designing a model able to outperform the state-of-the-art slicing algorithm which is currently implemented within Pandora. We assess the performance of our tool in terms of efficiency and accuracy, while exploiting hardware accelerating setups. The ultimate goal is to incorporate this deep learning approach in the Pandora reconstruction tool.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems

The objective of this project is to design, develop, and evaluate scalable software to enhance resilience, data checkpointing, program restart, and analysis. The proposed tasks are to 1) develop scalable machine learning techniques to learn temporal change patterns in a scalable and in-situ manner, and to minimize data movement and maximize learning locally closest to data; 2) design a concise data representation and indexing mechanism to capture the distribution of changes in data that can guarantee point-wise user-defined tolerable errors while reducing the data storage requirements by an order of magnitude or more; 3) develop data reduction techniques as library modules; 4) exploit local SSD for minimizing data movement in storage hierarchy; 5) develop anomaly detection algorithms that can predict corruptions based on learning of emerging patterns; 6) develop software libraries to be incorporated within widely used data formats and APIs; and 7) evaluate the proposed software using DOE scientific applications. The outcomes of the proposed work are to satisfy many synergistic data reduction and resilience requirements for large-scale data intensive applications executed on extreme-scale computing systems. The developed mechanism for error-bound data approximation is directly applicable to existing scientific applications. Through machine learning from historical events and change distribution, this work will enable anomaly detection for DOE computer facility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Reliable edge machine learning hardware for scientific applications

Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery.

Baldi, Tommaso↗
DOE OSTI2021

A review of ptychographic techniques for ultrashort pulse measurement

The measurement of optical ultrafast laser pulses is done indirectly because the required bandwidth to measure these pulses exceeds the bandwidth of current electronics. As a result, this measurement problem is often posed as a 1-D phase retrieval problem, which is fraught with ambiguities. The phase retrieval method known as ptychography solves this problem by making it possible to measure ultrafast pulses in either the time domain or the frequency domain. One well known algorithm is the principal components generalized projections algorithm (PCGPA) for extracting pulses from Frequency-Resolved Optical Gating (FROG) measurements. In this work, we discuss the development of the PCPGA and introduce new developments including an operator formalism that allows for the convenient addition of external constraints and the development of more robust algorithms. A close cousin, the ptychographic iterative engine will also be covered and compared to the PCGPA. Additional developments using other algorithmic strategies will also be discussed along with new developments combining optics and high-speed electronics to achieve megahertz measurement rates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

0BGRaman: Graph Network based Simulator for Forecasting Molecular Polarizability

This report presents the work performed under the GRaman project, sponsored by the PCSD LDRD Seed program. The project aimed at accelerating ab initio molecular dynamics simulation using Graph Networks. The Graph Network framework is a ML framework that has been successfully employed to simulate the dynamics of several physical systems: including water splashing in a container and flags moving with the wind. In this effort, we performed a data collection campaign for 3 different molecules of interest. We have built tools for preprocessing the trajectories obtained by simulating Raman Spectroscopy with NWChem and translating them into a suitable format for training. We have developed a training algorithm to train the Graph Network based simulators based on our data and developed a simulator that produces trajectories in the same NWChem format. While the tool has improved with each iteration of development and subsequent experiments, the current state of the tool does not allow to directly incorporate the technology within the NWChem framework because the trajectories produced by the tool are not yet accurate enough. However, the technology has proved to have good potential and it is certainly worth further research and development.

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↗