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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗
DOE OSTI2019

Fast and Scalable Implementations of Influence Maximization Algorithms

The Influence Maximization problem has been extensively studied in the past decade because of its practical applications in finding the key influencers in social networks. Due to the hardness of the underlying problem, existing algorithms have tried to trade off practical efficiency with approximation guarantees. However, even such approximate solutions take several hours of compute time on modest sized real world inputs. On the other hand, there is a lack of effective parallel and distributed algorithms to solve this problem. In this paper, we present efficient parallel algorithms for multithreaded and distributed systems to solve the influence maximization with approximation guarantee. Our algorithms extend state-of-the-art sequential approach based on computing reverse reachability sets. We present a detailed experimental evaluation, and analyze their performance and their sensitivity to input parameters, using real world inputs. Our experimental results demonstrate significant speedup on parallel architectures. We further show a speedup of up to 586X relative to the state-of-the-art sequential baseline using 1024 nodes of a supercomputer at far greater accuracy and twice the seed set size. To the best of our knowledge, this is the first effort in parallelizing the influence maximization operation at scale.

Minutoli, Marco↗
DOE OSTI2023

BARC Element Classifier (barc)

SAND2024-02120O This program enumerates and classifies all possible functional elements in several different categories within the Ballistic Asynchronous Reversible Computing (barc) model of computation. barc software is a research tool that is being used to help document the possible digital behaviors of primitive functional elements in the ABRC a.k.a. BARC model of computation. The method of operation of this software leverages elementary concepts and methods from discrete mathematics: combinatorics (permutations), partitions and equivalence classes, symmetry transformations, and the theory of finite groups. Implementation is in Python programming language. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Frank, Michael↗
DOE OSTI2023

Pressure-driven tearing and thermal transport in finite-beta reversed field pinch computations

In this work, nonlinear resistive-magnetohydrodynamics (MHD) computation with heating and anisotropic transport is applied to examine the interaction between thermal energy and magnetic fluctuations in inductively driven reversed-field pinches (RFPs). The magnetic fluctuations underlie magnetic field reversal through dynamo-like correlations, and they enhance thermal energy transport through fluctuations of parallel heat flux density. With the unfavorable magnetic curvature that exists across the RFP profile, thermal energy also affects the magnetic fluctuations. Computations with the NIMROD code [Sovinec et al., J. Comput. Phys. 195, 355–386 (2004)] integrate nonlinear MHD dynamics with energy transport and reproduce an RFP state with experimentally relevant values of plasma-β. Equilibria constructed from results of the 3D computations are analyzed to assess the sources of free energy in the saturated nonlinear state. Linear computations for these profiles show unstable modes of tearing parity. Their eigenfunctions are used to evaluate and compare stabilizing and destabilizing contributions to the kinetic energy integral. An assessment of the drives in the integral reveals that the pressure gradient drive is of comparable magnitude to the parallel current drive, and only the sum of the two surpasses the stabilizing contributions. Correlation of magnetic and parallel heat flux density fluctuations in the nonlinear computations shows that fluctuation-induced thermal conduction is the dominant mode of energy loss, as expected from experimental evidence. Decomposition of the fluctuating heat flux density shows that second-order correlations, alone, do not explain the total energy transport. Higher-order correlations are also important.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Designing Energy-Efficient Quantum Computers Through Prediction and Reduction of Cooling Requirements for Cryogenic Electronics

Quantum computing has been identified as a “wild card” by the International Energy Agency in predicting future global data center energy usage. This is primarily because both uncertainty in the extent to which quantum computing will be adopted, and uncertainty in the power consumption of individual quantum data centers. Unlike the classical counterparts, quantum computers need to be maintained at near absolute zero, requiring energy-intensive cryogenic cooling systems. Therefore, as quantum computers scale up from existing 50 qubit technology demonstrations to the 10,000 to 100,000 qubit systems that will be able to solve complex problems, the energy consumption of both the electronics and the required cooling systems will also increase. To predict this scaling, this work analyzes the energy requirements for both computation and cooling of quantum hardware. We show that the energy requirements for cooling of quantum computers is determined by several computing system parameters, including the number and type of physical qubits, the operating temperature, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. The energy requirements can then be found based on thermal system parameters such as cooling efficiency and cryostat heat transfer. Analysis of these parameters shows that the energy required for cooling is significantly larger than that required for computation, a reversal from energy usage patterns seen in conventional computing. The results and discussions provide a road-map for creating energy efficient quantum computers through the selection of computer architectures and cryogenic system configurations that minimize cooling requirements.

energy efficiency↗
DOE OSTI2018

Efficient reversible uniform and non-uniform random number generation in UNU.RAN

Reversible random number generations are useful in large-scale fault-tolerant parallel computations and parallel discrete event simulations that are based on reversible computation. The Universal Non-Uniform Random Number Generator (UNU.RAN) is one of the popular random number generators used in the simulation community, but the generators are forward-only in nature. In this paper, we develop new reverse algorithm for the default uniform random number generator algorithm of UNU.RAN and also a few nonuniform random generators that use the Transform Density Reduction (TDR) method. We verify the correctness of reversals of our algorithms and also provide performance results to demonstrate reverse computing runtime adds little overheads relative to its forward counterpart.

Yoginath, Srikanth↗
DOE OSTI2019

Arrow of time and its reversal on the IBM quantum computer

Uncovering the origin of the “arrow of time” remains a fundamental scientific challenge. Within the framework of statistical physics, this problem was inextricably associated with the second Law of thermodynamics, which declares that entropy growth proceeds from the system’s entanglement with the environment. this poses a question of whether it is possible to develop protocols for circumventing the irreversibility of time and if so to practically implement these protocols. Here we show that, while in nature the complex conjugation needed for time reversal may appear exponentially improbable, one can design a quantum algorithm that includes complex conjugation and thus reverses a given quantum state. Using this algorithm on an IBM quantum computer enables us to experimentally demonstrate a backward time dynamics for an electron scattered on a two-level impurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Investigation of Solar Proton Access Into the Inner Magnetosphere on 11 September 2017

Here, access of solar energetic protons to the inner magnetosphere on 11 September 2017 is investigated by computing the reverse particle trajectories with the Dartmouth geomagnetic cutoff code (Kress et al., 2010). The maximum and minimum cutoff rigidity at each point along the orbit of Van Allen Probe A is numerically computed by extending the code to calculate cutoff rigidity for particles coming from arbitrary direction. Pulse height analyzed (PHA) data have the advantage of providing individual particle energies and effectively excluding background high-energy proton contamination. This technique is adopted to study the cutoff locations for solar protons with different energy. The results demonstrate that cutoff latitude is lower for solar protons with higher energy, consistent with low-altitude vertical cutoffs. Both the observations and numerical results show that proton access into the inner magnetosphere depends strongly on angle between particle arrival direction and magnetic west. Finally, the numerical result is approximately consistent with the observation that the energy of almost all solar protons stays above the minimum cutoff rigidity.

58 GEOSCIENCES↗
DOE OSTI2017

Structural and Preclinical Studies of Computationally Designed Non-Nucleoside Reverse Transcriptase Inhibitors for Treating HIV infection

The clinical benefits of HIV-1 non-nucleoside reverse transcriptase (RT) inhibitors (NNRTIs) are hindered by their unsatisfactory pharmacokinetic (PK) properties along with the rapid development of drug-resistant variants. However, the clinical efficacy of these inhibitors can be improved by developing compounds with enhanced pharmacological profiles and heightened antiviral activity. We used computational and structure-guided design to develop two next-generation NNRTI drug candidates, compounds I and II, which are members of a class of catechol diethers. We evaluated the preclinical potential of these compounds in BALB/c mice because of their high solubility (510 µg/ml for compound I and 82.9 µg/ml for compound II), low cytotoxicity, and enhanced antiviral activity against wild-type (WT) HIV-1 RT and resistant variants. Additionally, crystal structures of compounds I and II with WT RT suggested an optimal binding to the NNRTI binding pocket favoring the high anti-viral potency. A single intraperitoneal dose of compounds I and II exhibited a prolonged serum residence time of 48 hours and concentration maximum (Cmax) of 4000- to 15,000-fold higher than their therapeutic/effective concentrations. These Cmax values were 4- to 15-fold lower than their cytotoxic concentrations observed in MT-2 cells. Compound II showed an enhanced area under the curve (0–last) and decreased plasma clearance over compound I and efavirenz, the standard of care NNRTI. Hence, the overall (PK) profile of compound II was excellent compared with that of compound I and efavirenz. Furthermore, both compounds were very well tolerated in BALB/c mice without any detectable acute toxicity. Taken together, these data suggest that compounds I and II possess improved anti-HIV-1 potency, remarkable in vivo safety, and prolonged in vivo circulation time, suggesting strong potential for further development as new NNRTIs for the potential treatment of HIV infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Mechanism of structural colors in binary mixtures of nanoparticle-based supraballs

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce noniridescent, saturated colors using nanoparticle assemblies. Nanoparticle mixtures varying in particle chemistry and size have additional emergent properties that affect the color produced. For complex multicomponent systems, understanding the assembled structure and a robust optical modeling tool can empower scientists to identify structure-color relationships and fabricate designer materials with tailored color. Here, we demonstrate how we can reconstruct the assembled structure from small-angle scattering measurements using the computational reverse-engineering analysis for scattering experiments method and use the reconstructed structure in finite-difference time-domain calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach that we present is useful for engineering synthetic materials with desired colors without laborious trial-and-error experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Model-based Dynamic Control of Speculative Forays in Parallel Computation

In simulations running in parallel, the processors would have to synchronize with other processors to maintain correct global order of computations. This can be done either by blocking computation until correct order is guaranteed, or by \textit{speculatively} proceeding with the best guess (based on local information) and later correcting errors if/as necessary. Since the gainful lengths of speculative forays depend on the dynamics of the application software and hardware at runtime, an online control system is necessary to dynamically choose and/or switch between the blocking and speculative strategies. In this paper, we formulate the reversible speculative computing in large-scale parallel computing as a dynamic linear feedback control (optimization) system model and evaluate its performance in terms of time and cost savings as compared to the traditional (forward) computing. We illustrate with an exact analogy in the form of vehicular travel under dynamic, delayed route information. The objective is to assist in making the optimal decision on what computational approach is to be chosen, by predicting the amount of time and cost savings (or losing) under different environments represented by different parameters and probability distribution functions. We consider the cases of Gaussian, exponential and log-normal distribution functions. The control system is intended for incorporating into speculative parallel applications such as optimistic parallel discrete event simulations to decide at runtime when and to what extent speculative execution can be performed gainfully.

Perumalla, Kalyan↗
DOE OSTI2020

SQUARE: Strategic Quantum Ancilla Reuse for Modular Quantum Programs via Cost-Effective Uncomputation

Compiling high-level quantum programs to machines that are size constrained (i.e. limited number of quantum bits) and time constrained (i.e. limited number of quantum operations) is challenging. In this paper, we present SQUARE (Strategic QUantum Ancilla REuse), a compilation infrastructure that tackles allocation and reclamation of scratch qubits (called ancilla) in modular quantum programs. At its core, SQUARE strategically performs uncomputation to create opportunities for qubit reuse. Current Noisy Intermediate-Scale Quantum (NISQ) computers and forward-looking Fault-Tolerant (FT) quantum computers have fundamentally different constraints such as data locality, instruction parallelism, and communication overhead. Our heuristic-based ancilla-reuse algorithm balances these considerations and fits computations into resource-constrained NISQ or FT quantum machines, throttling parallelism when necessary. To precisely capture the workload of a program, we propose an improved metric, the "active quantum volume," and use this metric to evaluate the effectiveness of our algorithm. Furthermore, our results show that SQUARE improves the average success rate of NISQ applications by 1.47X. Surprisingly, the additional gates for uncomputation create ancilla with better locality, and result in substantially fewer swap gates and less gate noise overall. SQUARE also achieves an average reduction of 1.5X (and up to 9.6X) in active quantum volume for FT machines.

compiler optimization↗
DOE OSTI2023

Two Circuits for Directing and Controlling Ballistic Fluxons

Reversible logic schemes using flux solitons (fluxons) on long Josephson junctions (LJJs) have recently been proposed. The attraction of the fluxon is that it propagates ballistically along an LJJ until it encounters a change in the character of the LJJ, often a designed circuit element. Logic gates involve fluxons interacting with circuit elements and with other fluxons. However, testing of ballistic fluxon circuits requires other circuits outside the logic family to direct and control fluxon motion. Here we discuss two such non-reversible fluxon control circuits. First, the polarity filter gate is a simple non-reversible gate that allows one polarity of fluxon to pass, while reflecting the other polarity. In the off state both polarities reflect. Second, the polarity separator generalizes on the polarity filter concept and allows separation of the two fluxon polarities into different LJJs. We discuss simulations of these structures and possible applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Qubit Assignment Using Time Reversal

As quantum computers with large numbers of qubits become increasingly available, experiments executed on a given device may not utilize all available qubits. In this case, the outcome of executing a quantum program will depend on the ability to efficiently select a subset of high-performing physical qubits. For any given quantum program and device there are many ways to assign physical qubits for execution of the program, and assignments will differ in performance due to the variability in quality across qubits and entangling operations on a single device. Evaluating the performance of each assignment using fidelity estimation introduces significant experimental overhead and will be infeasible for many applications, while relying on standard device benchmarks provides incomplete information about the performance of any specific program. Furthermore, the number of possible assignments grows combinatorially in the number of qubits on the device and in the program, motivating the use of heuristic optimization techniques. We demonstrate a practical solution to the problem of qubit assignment by using simulated annealing with a cost function based on the Loschmidt echo, a diagnostic that measures the reversibility of a quantum process. We provide theoretical justification for this choice of cost function by demonstrating that the optimal qubit assignment coincides with the optimal qubit assignment based on state fidelity in the weak error limit, and we provide experimental justification using diagnostics performed on Google’s superconducting qubit devices. We then establish the performance of simulated annealing for qubit assignment using classical simulations of noisy devices as well as optimization experiments performed on a quantum processor. Our results demonstrate that the use of Loschmidt echoes and simulated annealing provides a scalable and flexible approach to optimizing qubit assignment on near-term hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Generative Thermodynamic Computing

Here, we introduce a generative modeling framework for thermodynamic computing, in which structured data are synthesized from noise by the natural time evolution of a physical system governed by Langevin dynamics. While conventional diffusion models use neural networks to perform denoising, here the information needed to generate structure from noise is encoded by the dynamics of a thermodynamic system. Training proceeds by maximizing the probability with which the computer generates the reverse of a noising trajectory, which ensures that the computer generates data with minimal heat emission. We demonstrate this framework within a digital simulation of a thermodynamic computer. If realized in analog hardware, such a system would function as a generative model that produces structured samples without the need for artificially injected noise or active control of denoising.

Whitelam, Stephen [Lawrence Berkeley National Labo↗