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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Quantum algorithms for geologic fracture networks

Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.

58 GEOSCIENCES↗

DOE OSTI2020

Spatiotemporal Variability in Transport and Reactive Processes Across a First- to Fifth-Order Fluvial Network

Fluvial networks integrate and transport constituents from terrestrial and aquatic ecosystems. To date, most research on water quality dynamics has focused on process understanding at individual streams and, as a result, there is a lack of studies analyzing how small-scale, physical and biogeochemical drivers scale across fluvial networks. We performed tracer tests in five stream orders of the Jemez River continuum in New Mexico, USA, to quantify reach-scale hyporheic exchange during two different seasonal periods to address: how do hyporheic zone contributions to riverine processing change with space and time? And does the spatiotemporal variability of hyporheic exchange scale across fluvial networks? Combining conservative (i.e., bromide) and reactive (i.e., resazurin) tracer analyses with solute transport modeling, we found a dominance of reaction-limited transport conditions in space (for all stream orders) and time (two contrasting flow regimes). Moreover, we found a trend toward more reaction-limited conditions with increasing discharge across all stream orders. While our studied fluvial network did not consistently follow the expectations of decreasing hyporheic exchange contributions with increasing stream order, as it has been previously suggested from modeling studies, we found that processing rate coefficients and Damköhler numbers consistently decreased along the continuum with increasing discharge. Our findings suggest that knowledge transferability of hyporheic zone processing along fluvial networks may be possible if the scaling patterns that we found hold true in other catchments. Thus, more research on scaling patterns along fluvial networks is highly needed.

Gootman, Kaylyn S.↗

DOE OSTI2021

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. Here, we make the method available to the scientific community to speed biological research.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Ab initio ground states of strongly-correlated materials on quantum computers

The accurate first-principles description of strongly-correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving accurate many-body Hamiltonians including strong correlations, representing a subspace of interest of a material, using density functional theory calculations as a starting point. However, the solution of these material-specific models can scale exponentially on classical computers, constituting a challenge. Here we propose that utilizing quantum computers for obtaining the properties of downfolded Hamiltonians yields an accurate description of the ground state properties of strongly-correlated systems, while circumventing the exponential scaling problem. We benchmark the solution of Hubbard-like models obtained through downfolding by utilizing a classical tensor network implementation of variational quantum eigensolvers (VQE), and we reveal a strategy for driving the optimization through a hybrid minimization of the energy and maximization of the overlap with an approximate solution obtained through low-cost computational methods. This results in a reduction of the energy error by orders of magnitude compared to conventional VQE approaches, and allows us to reproduce long-range correlations for the first time. We demonstrate our first-principles approach for diverse strongly-correlated materials, correctly predicting the antiferromagnetic state of one-dimensional cuprate Ca 2 CuO 3 , the excitonic ground state of monolayer WTe2, and the charge-ordered state of correlated metal SrVO 3 . Our efficient computational implementation allows us to simulate large systems with up to 54 qubits and encompassing up to four correlated bands, which is indicative of the complexity that our framework can address.

Antonios M Alvertis↗

DOE OSTI2023

Reactive membrane networks for CWA protection

A membrane includes a first layer, and a second layer coupled to the first layer. The second layer includes a network of catalytic sites, each catalytic site having a catalytic center characterized by promoting a chemical reaction of a target material. A method of forming a chemically reactive membrane includes applying a first solution to a structure, the first solution includes a macrocyclic ligand having electron-donating ligands and a side functional group for crosslinking, crosslinking a plurality of the macrocyclic ligand to form a first network of crosslinked macrocyclic ligands, and applying a second solution to the structure, the second solution comprising a catalytic center. Each catalytic center complexes with the electron-donating ligands of each macrocyclic ligand to form catalytic sites in the first network of crosslinked macrocyclic ligands.

Fornasiero, Francesco↗

DOE OSTI2018

A Novel Transactive Energy Control Mechanism for Collaborative Networked Microgrids

Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes here a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, de-centralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Enhancing Active Distribution Systems Resilience by Fully Distributed Self-Healing Strategy

Distributed restoration can exploit smart grid technologies to enhance the resilience of active distribution networks toward a self-healing smart grid. However, the large number of decision variables, especially the binary ones for reconfiguration, bring challenges to developing scalable distributed distribution service restoration (DDSR) strategies. This paper proposes a fully distributed solution procedure based on the alternating direction method of multipliers (ADMM) for mixed-integer programming problems and applies to develop the DDSR framework. The method consists of relax-drive-polish phases, 1) relaxing binary variables, and applying the convex ADMM as a warm start; 2) driving the solutions toward Boolean values through a proximal operator; 3) fixing the obtained binding binary variables and solving the rest of the problem to polish results and achieve a high-quality suboptimal solution. Then, an autonomous clustering strategy and consensus ADMM are integrated with the proposed method to realize the fully distributed cluster-based framework of DDSR. This framework can first determine DER scheduling and switch status for reconfiguration to energize the out-of-service areas from local faults, and then provide the load restoration solution in a distributed manner for total blackouts in large-scale distribution networks. Furthermore, the effectiveness and scalability of the proposed DDSR framework are demonstrated through testing on the IEEE 123-node, IEEE 8500-node, and synthetic 100k-node test feeders.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Fluid System Stability Analysis Techniques

Fluid systems, or networks, consist of multiple components that work together to achieve some desired thermofluid state. For any generic application, this fluid state can be a combination of the fluid pressure, flow rate, enthalpy, or species concentration. Fluid system components, such as pumps and valves, are often governed by nonlinear differential equations, resulting in complex component-to-component interactions. System-level fluid network stability occurs when the flow through the system can maintain a steady-state solution in the presence of small perturbations, which depends on these component interactions. System instability, however, can go undetected until issues arise during integrated system testing. This presentation explores a method for system designers to think of the fluid network as an assembly of components, each with their own thermofluid surfaces of partial stability, called nullclines. The intersections of all nullclines yields system-level solutions, called equilibrium points. When designers define operating points, they are tuning system parameters so that these equilibrium points move to the desired location in the thermofluid state plane. However, linearization theory shows us that the dynamic behavior around these points can be unstable. The local stability of these equilibrium points can be assessed analytically with eigen-analysis, or numerically by propagating state-plane samples to construct a phase portrait. Investigating a phase portrait can help designers gain a qualitative understanding of a system’s dynamic performance. This understanding can then help inform requirement definitions, component selection, and operational procedures. This presentation includes an example of the phase portrait technique on a system featuring a centrifugal pump and a back-pressure regulator (BPR). Numerical modeling of this system suggests that equilibrium points on the left-hand side of the pump curve are dynamically unstable.

Fluid System↗

DOE OSTI2025

Extended Galerkin Neural Network Approximation of Singular Variational Problems with Error Control

We present extended Galerkin neural networks, a variational framework for approximating general boundary value problems (BVPs) with error control. The main contributions of this work are (1) a rigorous theory guiding the construction of new weighted least squares variational formulations suitable for use in neural network approximation of general BVPs, and (2) an “extended” feedforward network architecture which incorporates and is even capable of learning singular solution structures, thus greatly improving approximability of singular solutions. Furthermore, numerical results are presented for several problems, including steady Stokes flow around reentrant corners and in convex corners with Moffatt eddies in order to demonstrate efficacy of the method.

a posteriori error estimate↗

NASA NTRS2012

Implementation of Finite Volume based Navier Stokes Algorithm Within General Purpose Flow Network Code

This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.

Schallhorn, Paul↗

DOE OSTI2019

Evolution of Ionomer Morphology from Dispersion to Film: An in Situ X-ray Study

Ion-conducting polymers (ionomers) have been extensively studied in solution, as membranes and substrate-supported thin films for various electrochemical energy-conversion devices, including fuel cells and electrolyzers. Formation of an ionomer film from a solution, however, is not well understood, despite its importance for fabrication of electrodes in energy devices. Here, the evolution of the perfluorinated sulfonic acid morphology upon casting from a solution is observed using in situ grazing-incidence small- and wide-angle X-ray scattering. Aggregate interactions in dispersion directly impact the hydrophilic-domain network of the cast film and the onset of crystallization occurs simultaneously with the solution-to-film transition but continues to evolve on different time scales. In addition, confinement is shown to induce anisotropic morphology at multiple length scales. These results show promise for elucidating the role of casting parameters, drying protocols, and ionomer-solvent interactions in governing film morphology and open new avenues for establishing structure/processing/property relationships for ionomer films and modifying their transport functionality at catalytic interfaces.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Toward lower-diameter large-scale HPC and data center networks with co-packaged optics

We investigate the advantages of using co-packaged optics for building low-diameter, large-scale high-performance computing (HPC) and data center networks. The increased escape bandwidth offered by co-packaged optics can enable high-radix switch implementations of more than 150 switch ports, which can be combined with data rates of up to 400 Gb/s per port. From the network architecture perspective, the key benefits of using co-packaged optics in future fat-tree networks include (a) the ability to implement large-scale topologies of > <#comment/> 11 , 000 end points by eliminating the need for a third switching layer and (b) the ability to provide up to 4 × <#comment/> higher bisection bandwidth compared to existing solutions, reducing at the same time the number of required switch application-specific integrated circuits by > <#comment/> 80 % <#comment/> . From the network operation perspective, both reduced energy consumption and lower packet delays can be achieved since fewer hops are required; i.e., packets need to traverse fewer serializer/deserializer lanes and fewer switch buffers, which reduces the probability of contending with other packets and improves the tolerance of network congestion. The performance of the proposed architecture is evaluated via discrete-event simulations for a wide range of representative HPC synthetic-traffic cases that include both hotspot and non-hotspot scenarios. The simulation results suggest that co-packaged optics form a promising solution to keep up with bandwidth scaling in future networks, while the reduced number of switching layers can lead to significant mean packet delay improvements that start from 30% and reach up to 74% for high-load conditions.

Maniotis, Pavlos (ORCID:0000000244905253)↗

NASA NTRS2005

Developing a Framework for Effective Network Capacity Planning

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Yaprak, Ece↗

DOE OSTI2024

Solar Energy Innovation Network 2017-2024: Abbreviated Final Technical Report

This material is based upon work supported by the U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office under the Agreement/Award Number 32954 for Solar Energy Innovation Network (SEIN) Project, 2017-2024. SEIN is a dynamic program that assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. In conjunction with its partner organizations, NREL implemented the program by providing research, analysis, and technical expertise directly to project teams and groups of teams (cohorts), by facilitating networked learning through cohorts and peer exchange, and by facilitating dissemination and replication of solutions and lessons learned among stakeholders across the U.S. with similar challenges.

14 SOLAR ENERGY↗

DOE OSTI2022

Data Fusion via Neural Network Entropy Minimization for Target Detection and Multi-Sensor Event Classification

Broadly applicable solutions to multimodal and multisensory fusion problems across domains remain a challenge because effective solutions often require substantive domain knowledge and engineering. The chief questions that arise for data fusion are in when to share information from different data sources, and how to accomplish the integration of information. The solutions explored in this work remain agnostic to input representation and terminal decision fusion approaches by sharing information through the learning objective as a compound objective function. The objective function this work uses assumes a one-to-one learning paradigm within a one-to-many domain which allows the assumption that consistency can be enforced across the one-to-many dimension. The domains and tasks we explore in this work include multi-sensor fusion for seismic event location and multimodal hyperspectral target discrimination. We find that our domain- informed consistency objectives are challenging to implement in stable and successful learning because of intersections between inherent data complexity and practical parameter optimization. While multimodal hyperspectral target discrimination was not enhanced across a range of different experiments by the fusion strategies put forward in this work, seismic event location benefited substantially, but only for label-limited scenarios.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Multilevel-in-width training for deep neural network regression

A common challenge in regression is that for many problems, the degrees of freedom required for a high-quality solution also allows for overfitting. Regularization is a class of strategies that seek to restrict the range of possible solutions so as to discourage overfitting while still enabling good solutions, and different regularization strategies impose different types of restrictions. In this paper, we present a multilevel regularization strategy that constructs and trains a hierarchy of neural networks, each of which has layers that are wider versions of the previous network's layers. Here, we draw intuition and techniques from the field of Algebraic Multigrid (AMG), traditionally used for solving linear and nonlinear systems of equations, and specifically adapt the Full Approximation Scheme (FAS) for nonlinear systems of equations to the problem of deep learning. Training through V-cycles then encourage the neural networks to build a hierarchical understanding of the problem. We refer to this approach as multilevel-in-width to distinguish from prior multilevel works which hierarchically alter the depth of neural networks. The resulting approach is a highly flexible framework that can be applied to a variety of layer types, which we demonstrate with both fully connected and convolutional layers. We experimentally show with PDE regression problems that our multilevel training approach is an effective regularizer, improving the generalize performance of the neural networks studied.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Fluid System Stability Analysis Techniques

Fluid System↗

DOE OSTI2021

Proactive Operations and Investment Planning via Stochastic Optimization to Enhance Power Systems’ Extreme Weather Resilience

We present scalable stochastic optimization approaches for improving power systems’ resilience to extreme weather events. We consider both proactive redispatch and transmission line hardening as alternatives for mitigating expected load shed due to extreme weather, resulting in large-scale stochastic linear programs (LPs) and mixed-integer linear programs (MILPs). We solve these stochastic optimization problems with progressive hedging (PH), a parallel, scenario-based decomposition algorithm. Our computational experiments indicate that our proposed method for enhancing power system resilience can provide high-quality solutions efficiently. With up to 128 scenarios on a 2,000-bus network, the operations (redispatch) and investment (hardening) resilience problems can be solved in approximately 6 min and 2 h of wall-clock time, respectively. Additionally, we solve the investment problems with up to 512 scenarios, demonstrating that the approach scales very well with the number of scenarios. Moreover, the method produces high quality solutions that result in statistically significant reductions in expected load shed. Our proposed approach can be augmented to incorporate a variety of other operational and investment resilience strategies, or a combination of such strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗