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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Training Spiking Neural Networks Using Combined Learning Approaches

Spiking neural networks (SNNs), the class of neural networks used in neuromorphic computing, are difficult to train using traditional back-propagation techniques. Spike timingdependent plasticity (STDP) is a biologically inspired learning mechanism that can be used to train SNNs. Evolutionary algorithms have also been demonstrated as a method for training SNNs. In this work, we explore the relationship between these two training methodologies. We evaluate STDP and evolutionary optimization as standalone methods for training networks, and also evaluate a combined approach where STDP weight updates are applied within an evolutionary algorithm. We also apply Bayesian hyperparameter optimization as a meta learner for each of the algorithms. We find that STDP by itself is not an ideal learning rule for randomly connected networks, while the inclusion of STDP within an evolutionary algorithm leads to similar performance, with a few interesting differences. This study suggests future work in understanding the relationship between network topology and learning rules.

Elbrecht, Daniel↗
DOE OSTI2021

Enabling Pulse-level Programming, Compilation, and Execution in XACC

Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Virtual-Voltage Partition-Based Approach to Optimal Transmission Switching

This article deals with optimal transmission switching (OTS) problems involving binary decisions about network topology and nonconvex power flow constraints. We adopt a semidefinite programming formulation for the optimal power flow (OPF) problem that, however, remains nonconvex due to the presence of discrete variables and bilinear products between the decision variables. To tackle the latter, we introduce a physically inspired, virtual-voltage approximation that leads to provable lower and upper bounds on the solution of the original problem. To deal with the exponential complexity caused by the discrete variables, we introduce a graph partition-based algorithm that breaks the problem into several parallel mixed-integer subproblems of smaller size. Simulations on the IEEE bus test cases demonstrate the high degree of accuracy and affordable computational requirements of our approach.

42 ENGINEERING↗
DOE OSTI2022

Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems which display a variety of complex rearrangements (e.g. mobile genetic elements), mutation for genetic algorithms commonly utilizes only random point-wise changes. Furthermore, generalizing beyond point-wise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined point-wise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e. mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Finally, our results show that moving beyond random point-wise mutations accelerates genetic algorithm optimization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator

Here, in the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial differential equations (PDEs), including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice. Instead, inspired by the idea of operator learning, this work leverages the Fourier neural operator (FNO) to effectively learn the frequency domain seismic wavefields under the context of variable velocity models. We also propose a new framework paralleled FNO (PFNO) for efficiently training the FNO-based solver given multiple source locations and frequencies. Numerical experiments demonstrate the high accuracy of both FNO and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore, the cross-dataset generalization test verifies that PFNO adapts to out-of-distribution velocity models. Finally, PFNO admits higher computational efficiency on large-scale testing datasets than the traditional finite-difference method. The aforementioned advantages endow the FNO-based solver with the potential to build powerful models for research on seismic waves.

58 GEOSCIENCES↗
DOE OSTI2024

Simulated Feasibility of 3-D Lightning Mapping From Space

In addition to the awe it inspires, lightning can illuminate the microphysical processes hidden away within deep convection. The current generation of space-based lightning mapping uses mostly 2-D optical imaging to connect overall flash characteristics to their parent storm dynamics, but are missing a dimension’s worth of information. With lightning now classified as an essential climate variable, future spaceborne mappers will need improved capabilities to take advantage of the 3-D structure of lightning flashes to support meteorological and climate modeling. We report here on a study of the feasibility of high-resolution 3-D lightning mapping using a radio frequency (RF)-based network of satellites from low-Earth orbit (LEO). Lightning sources are simulated using existing lightning mapping array (LMA) tools, modified for orbital detection, and spatially reconstructed using a Levenberg–Marquardt geolocation algorithm to assess sources of uncertainty in these solutions. We analyze the benefits and limitations of this approach compared to existing orbital and ground-based methods. Results of this study show that lightning can be mapped in 3-D with a vertical location accuracy better than 2 km using as few as five satellites in LEO capable of measuring the time-of-arrival of impulsive RF signals in the very high-frequency (VHF) band. The consequence of this study is that high-resolution, spaceborne 3-D mapping of lightning is achievable across most of the globe, having crucial implications for our understanding of not only lightning, but also severe weather development, climate science, and more.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control

This article proposes a deep learning (DL)-based control algorithm—DL velocity-based model predictive control (VMPC)—for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Herein, simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations

We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE)-originally designed for nonlinear dimensionality reduction-for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probability density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes.

deformation↗
DOE OSTI2021

DC-Saturated Continuously Variable Series Reactors (CVSRs) for Power Flow Control in Power Transmission Systems

This paper proposes a novel continuously variable series reactor (CVSR) based on a dc current controller (DCC) to manage power flow in transmission systems. There are three major contributions. First, the three-dimensional electromagnetic interaction has been comprehensively analyzed to extend the understanding beyond the conventional 2D relationship. Second, a high-fidelity reluctance model of the CVSR with an improved DCC model is proposed and implemented. To overcome the fundamental concern for the system modeling, the DCC has been modeled as an ideal current source in parallel with an output impedance. Additionally, the induced back-EMF can be precisely projected which provides critical design guidelines for the DCC. Third, inspired by the theoretical analysis and modeling, a reliable high power DCC converter is designed accordingly to interface with kV-level back-EMF and supply kA-level dc current for a 115 kV/1500 A CVSR. Experiments are conducted in a practical transmission demonstration system. When the ac current in the transmission system varies from zero to 1500 A, experimental results show that the proposed CVSR can continuously regulate the reactance from 1.6 Ω to 5 Ω, validating the effectiveness of the proposed system design and modeling methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Employing Interacting Qubits for Distributed Microgrid Control

To empower flexible and scalable operations, distributed control of multi-inverter microgrids, based on classical communication networks among distributed energy resources, has attracted considerable attention as it can guarantee synchronization and provide suitable remedies to the problem of improper power sharing. Notwithstanding this, resilience of the current schemes on classical communication makes microgrids vulnerable to cyber attacks. Inspired by recent revolutionary breakthroughs in quantum communication, in this paper, we devise a novel synchronization mechanism. We extend the synchronization framework utilized in distributed control algorithms to networks of quantum systems by generating pinning terms and coupling mechanism for the new synchronization rule via exploiting proper quantum jump operators and observables, and show that the quantum system will converge to a time-variant target state. Our devised quantum distributed controller (QDC) gives rise to a novel quantum communication scheme for distributed control of microgrids and enables microgrids to exploit the state-of-the-art quantum communication frameworks as communication infrastructure. Finally, test results on two representative AC and DC networked microgrids validate the efficacy and universality of the quantum distributed control.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Controller-Based Energy-Aware Wireless Sensor Network Routing Using Quantum Algorithms

Energy-efficient routing in wireless sensor networks has attracted attention from researchers in both academia and industry, most recently motivated by the opportunity to use software-defined network-inspired approaches. These problems are NP-hard, with algorithms needing computation time that scales faster than polynomials in the problem size. Consequently, heuristic algorithms are used in practice, which are unable to guarantee optimally. In this article, we show proof-of-principle for the use of a quantum annealing processor instead of a classical processor, to find optimal or nearly optimal solutions very quickly. Our preliminary results for small networks show that this approach using quantum computing has great promise and may open the door for other significant improvements in the efficacy of network algorithms.

42 ENGINEERING↗
DOE OSTI2024

Localized Evaluation for Constructing Discrete Vector Fields

Topological abstractions offer a method to summarize the behavior of vector fields, but computing them robustly can be challenging due to numerical precision issues. One alternative is to represent the vector field using a discrete approach, which constructs a collection of pairs of simplices in the input mesh that satisfies criteria introduced by Forman's discrete Morse theory. While numerous approaches exist to compute pairs in the restricted case of the gradient of a scalar field, state-of-the-art algorithms for the general case of vector fields require expensive optimization procedures. This paper introduces a fast, novel approach for pairing simplices of two-dimensional, triangulated vector fields that do not vary in time. The key insight of our approach is that we can employ a local evaluation, inspired by the approach used to construct a discrete gradient field, where every simplex in a mesh is considered by no more than one of its vertices. Specifically, we observe that for any edge in the input mesh, we can uniquely assign an outward direction of flow. We can further expand this consistent notion of outward flow at each vertex, which corresponds to the concept of a downhill flow in the case of scalar fields. Working with outward flow enables a linear-time algorithm that processes the (outward) neighborhoods of each vertex one-by-one, similar to the approach used for scalar fields. Here, we couple our approach to constructing discrete vector fields with a method to extract, simplify, and visualize topological features. Empirical results on analytic and simulation data demonstrate drastic improvements in running time, produce features similar to the current state-of-the-art, and show the application of simplification to large, complex flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SPEL: Software tool for Porting E3SM Land Model with OpenACC in a Function Unit Test Framework

Most high-end computers adopt hybrid architecture, porting a large-scale scientific code onto accelerators is necessary. The paper presents a generic method for porting large-scale scientific code onto accelerators using compiler directives within a modularized function unit test platform. We have implemented the method and designed a software tool (SPEL) to port the E3SM Land Model (ELM) onto the GPUs in the Summit computer. SPEL automatically generates GPU-ready test modules for all ELM functions, such as CanopyFlux, SoilTemperature, and EcosystemDynamics. SPEL breaks the ELM into a collection of standalone unit test programs for easy code verification and further performance improvement. We further optimize several ELM test modules with advanced techniques, including memory reduction, reconstructed parallel loops, and asynchronous GPU kernel launch. We hope our study will inspire new toolkit developments that expedite large-scale scientific code porting with compiler directives.

Schwartz, Peter↗
DOE OSTI2022

Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

We report N\non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Role of carboxysomes in cyanobacterial CO 2 assimilation: CO 2 concentrating mechanisms and metabolon implications

Many carbon-fixing organisms have evolved CO 2 concentrating mechanisms (CCMs) to enhance the delivery of CO 2 to RuBisCO, while minimizing reactions with the competitive inhibitor, molecular O 2 . These distinct types of CCMs have been extensively studied using genetics, biochemistry, cell imaging, mass spectrometry, and metabolic flux analysis. Highlighted in this paper, the cyanobacterial CCM features a bacterial microcompartment (BMC) called ‘carboxysome’ in which RuBisCO is co-encapsulated with the enzyme carbonic anhydrase (CA) within a semi-permeable protein shell. Further, the cyanobacterial CCM is capable of increasing CO 2 around RuBisCO, leading to one of the most efficient processes known for fixing ambient CO 2 . The carboxysome life cycle is dynamic and creates a unique subcellular environment that promotes activity of the Calvin–Benson (CB) cycle. The carboxysome may function within a larger cellular metabolon, physical association of functionally coupled proteins, to enhance metabolite channelling and carbon flux. In light of CCMs, synthetic biology approaches have been used to improve enzyme complex for CO 2 fixations. Research on CCM-associated metabolons has also inspired biologists to engineer multi-step pathways by providing anchoring points for enzyme cascades to channel intermediate metabolites towards valuable products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Production of Vanillin From Ferulic Acid by Pseudomonas putida KT2440 Using Metabolic Engineering and In Situ Product Recovery

Vanillin is the most in-demand flavouring compound in the world and because vanillin extracted from vanilla pods cannot meet the global demand, most vanillin on the market today is chemically synthesised. Increasing demands by consumers for natural ingredients have inspired efforts to develop vanillin derived from microbial sources. These efforts have been challenged by low titers, likely caused by the toxicity of vanillin to most microbial biocatalysts. In this study, we engineered a Pseudomonas putida KT2440-derived strain that accumulated vanillin from ferulic acid to 0.64 g/L. To increase the overall titre, we applied a hydrophobic polystyrene-based resin to vanillin-accumulating cultures, which enabled an increase in total vanillin recovery to an apparent titre of 3.35 g/L. This study demonstrates that P. putida can accumulate vanillin from ferulic acid to higher titers when vanillin is removed from the cultivation medium, mitigating its toxicity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

K in an Urban World: New Contexts for Hydraulic Conductivity

Abstract Hydraulic conductivity ( K ) is a key hydrologic parameter widely recognized to be difficult to estimate and constrain, with little consistent assessment in disturbed, urbanized soils. To estimate K , it is either measured, or simulated by pedotransfer functions, which relate K to easily measured soil properties. We measured K in urbanized soils by double‐ring infiltrometer ( K dring ), near‐saturated tension infiltrometry ( K minidisk ), and constant head borehole permeametry ( K borehole ), along with other soil properties across the major soil orders in 12 United States cities. We compared measured K with that predicted from the pedotransfer function, ROSETTA. We found that regardless of soil texture, K dring was consistently larger than K minidisk ; with the latter having slightly less sample variance. K borehole was dependent upon specific subsurface conditions, and contrary to common expectations, did not always decrease with depth. Based on either soil textural class, or percent textural separates (sand, silt clay), ROSETTA did not accurately predict measured K for surface nor subsurface soils. We go on to discuss how K varies in urban landscapes, the role of measurement methods and artifacts in the perception of this metric, and implications for hydrologic modeling. Overall, we aim to inspire consistency and coherence when addressing K ‐related challenges in sustainable urban water management.

Shuster, W. D.↗