Results for “fast algorithm” — Page 74

NASA NTRS2022

Sharing Operational Intent with Containment Confidence Level for Negotiating Deconfliction in Upper Class E Airspace

Community-based Cooperative Separation Management (CSM) is expected to provide separation services in Upper Class E airspace (near and above FL600). Under CSM, operators are responsible for maintaining separation. The CSM concept is enabled by sharing Operational Intent (OI) among the operators to ensure common situation awareness. The OI is represented as four-dimensional (time and space) information that indicates where an aircraft would be contained within the space and time, with a known level of confidence. However, each vehicle’s ability to stay within its region of OI may differ based on each vehicle’s performance characteristics, resulting in varying OI sizes among the vehicles. Such varying OI size could adversely affect efficient and fair access to the airspace. In this paper, an OI-generation algorithm under varying OI size restriction with Containment Confidence Level (CCL) is presented. High-Altitude Long Endurance (HALE) balloon operations are used as an example application. A framework is presented by which CCL information is used in the deconfliction process. A fast-time simulation experiment is conducted to evaluate the feasibility of the proposed framework. The simulation results show a reduced number of unnecessary deconfliction actions.

Upper Class E Traffic Management↗

NASA NTRS2022

Sharing Operational Intent with Containment Confidence Level for Negotiating Deconfliction in Upper Class E Airspace

Community-based Cooperative Separation Management (CSM) is expected to provide separation services in Upper Class E airspace (near and above FL600). Under CSM, operators are responsible for maintaining separation. The CSM concept is enabled by sharing Operational Intent (OI) among the operators to ensure common situation awareness. The OI is represented as four-dimensional (time and space) information that indicates where an aircraft would be contained within the space and time, with a known level of confidence. However, each vehicle’s ability to stay within its region of OI may differ based on each vehicle’s performance characteristics, resulting in varying OI sizes among the vehicles. Such varying OI size could adversely affect efficient and fair access to the airspace. In this paper, an OI-generation algorithm under varying OI size restriction with Containment Confidence Level (CCL) is presented. High-Altitude Long Endurance (HALE) balloon operations are used as an example application. A framework is presented by which CCL information is used in the deconfliction process. A fast-time simulation experiment is conducted to evaluate the feasibility of the proposed framework. The simulation results show a reduced number of unnecessary deconfliction actions.

Upper Class E Traffic Management, ETM, Cooperative↗

DOE OSTI2023

Superconductivity in the Li-B-C system at 100 GPa

Layer Li-B-C compounds have been shown to have feasible superconductivity. Using the adaptive genetic algorithm, we predict the structures of the Li-B-C system at 100 GPa. Here we identify several low-enthalpy metallic phases with stoichiometries of Li B 2 C, Li B 3 C, Li 2 BC 2 , Li 3 B 2 C 3 , Li 3 BC, and Li 5 BC. Using a fast screening method of electron-phonon interaction, we find that Li B 3 C is a promising candidate for superconductivity. The consecutive calculations using the full Brillouin zone confirm the existence of the strong electron-phonon coupling (EPC) in this system. The anharmonic B-C phonon modes near the zone center provide the major contribution to the EPC. The EPC constant is 1.40, and the estimated critical temperature is 22 K. In this paper, we indicate that superconductivity can also happen without a layered structural motif in the Li-B-C system. We also demonstrate an effective strategy for crystal structure prediction of superconducting materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Advanced battery modeling for interfacial phenomena and optimal charging

Lithium ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack.

25 ENERGY STORAGE↗

DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Modeling the co-assembly of binary nanoparticles

Abstract In this work, we present a binary assembly model that can predict the co-assembly structure and spatial frequency spectra of monodispersed nanoparticles with two different particle sizes. The approach relies on an iterative algorithm based on geometric constraints, which can simulate the assembly patterns of particles with two distinct diameters, size distributions, and at various mixture ratios on a planar surface. The two-dimensional spatial-frequency spectra of the modeled assembles can be analyzed using fast Fourier transform analysis to examine their frequency content. The simulated co-assembly structures and spectra are compared with assembled nanoparticles fabricated using transfer coating method are in qualitative agreement with the experimental results. The co-assembly model can also be used to predict the peak spatial frequency and the full-width at half-maximum bandwidth, which can lead to the design of the structure spectra by selection of different monodispersed particles. This work can find applications in fabrication of non-periodic nanostructures for functional surfaces, light extraction structures, and broadband nanophotonics.

Mohanty, Saurav (ORCID:0009000302592855)↗

NASA NTRS2002

Rapid evolution of analog circuits configured on a field programmable transistor array

The purpose of this paper is to illustrate evolution of analog circuits on a stand-alone board-level evolvable system (SABLES). SABLES is part of an effort to achieve integrated evolvable systems. SABLES provides autonomous, fast (tens to hundreds of seconds), on-chip circuit evolution involving about 100,000 circuit evaluations. Its main components are a JPL Field Programmable Transistor Array (FPTA) chip used as transistor-level reconfigurable hardware, and a TI DSP that implements the evolutionary algorithm controlling the FPTA reconfiguration. The paper details an example of evolution on SABLES and points out to certain transient and memory effects that affect the stability of solutions obtained reusing the same piece of hardware for rapid testing of individuals during evolution.

SABLES analog circuits field programmable transist↗

DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A Theory of Ultrafast Charge Transfer Relaxation with Non-Innocent Solvent Molecules

In this work we revisit the photodynamics of tetracyanoethylene-hexamethylbenzene (TCNE- HMB), the molecular complex studied by Hochstrasser et al. [J. Chem. Phys. 100, 4797–4810, 1994] that has long challenged the applicability of Marcus’s theory of elec- tron transfer for predicting photochemical reactions. Using a novel black-box electronic structure algorithm (time-dependent density functional theory with one double, TD- DFT-1D) to efficiently run molecular dynamics that can treat charge recombination, we run ab initio surface hopping molecular dynamics and confirm that, for a polar solvent, charge recombination rates can be incredibly fast (indeed faster than the sol- vent relaxation time); for non-polar solvents, the rate is much slower. We demonstrate that, although Marcus theory cannot be directly applied, these nonequilibrium (and sometimes incredibly fast) photoexcited dynamics can be effectively explained within a two-state model without any evidence of a transition through a conical intersection. Most importantly, for this paradigmatic model system, we are able to identify two nuclear coordinates of interest (rather than the single coordinate predicted by Marcus or a full set of internal quantum modes studied by Bixon and Jortner): the solvent relaxation in the first shell (that strongly modulates the energies of the charge trans- fer state and differentiates time scales for relaxation) and a nuclear displacement in the TCNE-HMB complex arising from a handful of vibrations that induces non-Born Oppenheimer motion and eventually facilitates an abrupt electronic transition to the ground state. Altogether, these findings suggest a tractable generalization of Marcus theory for future simulations of photochemistry with non-innocent solvent environ- ments in the spirit of a Hamiltonian suggested by Stuchebrukhov (J. Chem. Phys. 107, 3821, 1997).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Data-driven selection and spectral classification of white dwarf stars

The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection, and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios ≥9. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with ≥ 90 % accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Graph Contractions for Calculating Correlation Functions in Lattice QCD

Computing correlation functions for many-particle systems in Lattice QCD is vital to extract nuclear physics observables like the energy spectrum of hadrons such as protons. However, this type of calculation has long been considered to be very challenging and computing-resource intensive because of the complex nature of a hadron composed of quarks with many degrees of freedom. In particular, a correlation function can be calculated through a sum of all possible pairs of quark contractions, each of which is a batched tensor contraction, dictated by Wick's theorem. Because the number of terms of this sum can be very large for any hadronic system of interest, fast evaluation of the sum faces several challenges: an extremely large number of contractions, a huge memory footprint at runtime, and the speed of tensor contractions. In this paper, we present a Lattice QCD analysis software suite, Redstar, which addresses these challenges by utilizing novel algorithmic and software engineering methods targeting modern computing platforms such as many-core CPUs and GPUs. In particular, Redstar represents every term in the sum of a correlation function by a graph, applies efficient graph algorithms to reduce the number of contractions to lower the cost of computations, and minimizes the total memory footprint. Moreover, Redstar carries out the contractions on either CPUs or GPUs utilizing an internal and highly efficient Hadron contraction library. Specifically, we illustrate some important algorithmic optimizations of Redstar, show various key design features of Hadron library, and present the speedup values due to the optimizations along with performance figures for calculating six correlations functions on four computing platforms.

Chen, Jie↗

DOE OSTI2023

Crystallographic variant mapping using precession electron diffraction data

In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO2 thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities.

Crystallographic variant mapping↗

DOE OSTI2021

Peer-to-peer Communication Control Architecture for Engaging Ubiquitous DERs to Support Distribution System Resiliency

In this project, we developed a leader-follower consensus (LFC) control architecture to coordinate between GFM and GFL inverters with the former being leaders and the latter being followers. The objective is to simultaneously achieve a fast frequency and voltage regulation along with accurate real power and var sharing among all inverters under various disturbances. The distinction of our work is that we consider both GFM and GFL inveters for frequency and voltage regulations. In addition, rather than a standard consensus algorithm, our work utilized a leader-follower consensus algorithm which exploits the physical characteristics of GFM-GFL inverters: (i) the GFM inverters directly control frequency and voltage, and hence, serve as leaders, (ii) the GFL inverters measure frequency/voltage to modulate their outputs, and hence, they should serve as followers. The effectiveness of the proposed LFC coordination was tested on a networked microgrid test system under different disturbances and communication degradation events. We demonstrated that the proposed fully coordinated control is robust to disturbances, while outperforming uncoordinated and GFM-only coordinated approaches. Overall, this work emphasized the necessity and benefits of the GFMGFL coordination in the secondary control of microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Graph Contractions for Calculating Correlation Functions in Lattice QCD

Computing correlation functions for many-particle systems in Lattice QCD is vital to extract nuclear physics observables like the energy spectrum of hadrons such as protons. However, this type of calculation has long been considered to be very challenging and computing-resource intensive because of the complex nature of a hadron composed of quarks with many degrees of freedom. In particular, a correlation function can be calculated through a sum of all possible pairs of quark contractions, each of which is a batched tensor contraction, dictated by Wick's theorem. Because the number of terms of this sum can be very large for any hadronic system of interest, fast evaluation of the sum faces several challenges: an extremely large number of contractions, a huge memory footprint at runtime, and the speed of tensor contractions. In this paper, we present a Lattice QCD analysis software suite, Redstar, which addresses these challenges by utilizing novel algorithmic and software engineering methods targeting modern computing platforms such as many-core CPUs and GPUs. In particular, Redstar represents every term in the sum of a correlation function by a graph, applies efficient graph algorithms to reduce the number of contractions to lower the cost of computations, and minimizes the total memory footprint. Moreover, Redstar carries out the contractions on either CPUs or GPUs utilizing an internal and highly efficient Hadron contraction library. Specifically, we illustrate some important algorithmic optimizations of Redstar, show various key design features of Hadron library, and present the speedup values due to the optimizations along with performance figures for calculating six correlations functions on four computing platforms.

Chen, Jie↗

NASA NTRS1979

Simplified algorithm for correcting both errors and erasures of Reed-Solomon codes

Using a finite-field transform, a simplified algorithm for decoding Reed-Solomon codes is developed to correct erasures as well as errors over the finite-field GF(q to the m power), where q is a prime and m is an integer. If the finite-field transform is a fast transform, this decoder can be faster and simpler than a decoder that uses more conventional methods.

Reed, I. S.↗

DOE OSTI2023

Fast Coordination of Distributed Energy Resources Over Time-Varying Communication Networks

Not Available

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗