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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A scalable planning framework of energy storage systems under frequency dynamics constraints

As the penetration of renewables increases in power systems, the declining system inertia can cause frequency stability issues. Battery energy storage systems (BESSs) respond fast and therefore can relieve the low inertia difficulty but need to be appropriately sized considering the associated cost. This paper presents a novel stochastic optimization model for economically planning BESS capacity while considering the spatial–temporal correlation of wind generation and generator outages under frequency stability constraints, which include the rate-of-change of frequency (RoCoF), frequency nadir (FN), and quasi-steady-state (QSS) frequency. A set of new FN constraints that can be easily linearized is developed. To account for renewable uncertainties, a realistic uncertainty modeling approach, Random Field, is adopted to generate wind generation scenarios by considering both spatial and temporal evolutions of wind speed profiles. The ESS sizing is formulated as a mixed-integer linear programming problem and solved by using a scalable decomposition-and-coordination approach, Surrogate Absolute Value Lagrangian Relaxation (SAVLR). To further improve the scalability and reduce computational burdens, a rolling-horizon-based update is developed and incorporated into SAVLR for providing a practical solution to the long-term planning of very large-scale power systems. Finally, a modified IEEE 118-bus system and the Polish system are used to validate the effectiveness and scalability of the model and solution methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A scalable and tunable thermoreversible polymer for 3D human pluripotent stem cell biomanufacturing

Human pluripotent stem cells (hPSCs) are an exciting and promising source to enable cell replacement therapies for a variety of unmet medical needs. Though hPSCs can be successfully derived into numerous physiologically relevant cell types, effective translation to the clinic is limited by challenges in scalable production of high-quality cells, cellular immaturity following the differentiation process, and the use of animal-derived components in culture. To address these limitations, we have developed a fully defined, reproducible, and tunable thermoreversible polymer for high-quality, scalable 3D cell production. Our reproducible synthesis method enables precise control of gelation temperature (24°C–32°C), hydrogel stiffness (100–4000 Pa), and the prevention of any unintended covalent crosslinking. After material optimization, we demonstrated hPSC expansion, pluripotency maintenance, and differentiation into numerous lineages within the hydrogel. Overall, this 3D thermoreversible hydrogel platform has broad applications in scalable, high-quality cell production to overcome the biomanufacturing burden of stem cell therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

A domain decomposition method of stochastic PDEs: An iterative solution techniques using a two-level scalable preconditioner

Recent advances in high performance computing systems and sensing technologies motivate computational simulations with extremely high resolution models with capabilities to quantify uncertainties for credible numerical predictions. A two-level domain decomposition method is reported in this investigation to devise a linear solver for the large-scale system in the Galerkin spectral stochastic finite element method (SSFEM). In particular, a two-level scalable preconditioner is introduced in order to iteratively solve the large-scale linear system in the intrusive SSFEM using an iterative substructuring based domain decomposition solver. The implementation of the algorithm involves solving a local problem on each subdomain that constructs the local part of the preconditioner and a coarse problem that propagates information globally among the subdomains. The numerical and parallel scalabilities of the two-level preconditioner are contrasted with the previously developed one-level preconditioner for two-dimensional flow through porous media and elasticity problems with spatially varying non-Gaussian material properties. A distributed implementation of the parallel algorithm is carried out using MPI and PETSc parallel libraries. The scalabilities of the algorithm are investigated in a Linux cluster.

42 ENGINEERING↗
DOE OSTI2015

Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem : given parameter inputs, solve the governing equations to determine output quantities of interest. In contrast, in this paper, we consider the broader question: given a (large-scale) model containing uncertain parameters, (possibly) noisy observational data, and a prediction quantity of interest, how do we construct efficient and scalable algorithms to (1) infer the model parameters from the data (the deterministic inverse problem ), (2) quantify the uncertainty in the inferred parameters (the Bayesian inference problem), and (3) propagate the resulting uncertain parameters through the model to issue predictions with quantified uncertainties (the forward uncertainty propagation problem )? We present efficient and scalable algorithms for this end-to-end, data-to-prediction process under the Gaussian approximation and in the context of modeling the flow of the Antarctic ice sheet and its effect on loss of grounded ice to the ocean. The ice is modeled as a viscous, incompressible, creeping, shear-thinning fluid. The observational data come from satellite measurements of surface ice flow velocity, and the uncertain parameter field to be inferred is the basal sliding parameter, represented by a heterogeneous coefficient in a Robin boundary condition at the base of the ice sheet. The prediction quantity of interest is the present-day ice mass flux from the Antarctic continent to the ocean. We show that the work required for executing this data-to-prediction process—measured in number of forward (and adjoint) ice sheet model solves—is independent of the state dimension, parameter dimension, data dimension, and the number of processor cores. The key to achieving this dimension independence is to exploit the fact that, despite their large size, the observational data typically provide only sparse information on model parameters. This property can be exploited to construct a low rank approximation of the linearized parameter-to-observable map via randomized SVD methods and adjoint-based actions of Hessians of the data misfit functional.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An adaptive scalable fully implicit algorithm based on stabilized finite element for reduced visco-resistive MHD

The magnetohydrodynamics (MHD) equations are continuum models used in the study of a wide range of plasma physics systems, including the evolution of complex plasma dynamics in tokamak disruptions. However, efficient numerical solution methods for MHD are extremely challenging due to disparate time and length scales, strong hyperbolic phenomena, and nonlinearity. Additionally, therefore the development of scalable, implicit MHD algorithms and high-resolution adaptive mesh refinement strategies is of considerable importance. In this work, we develop a high-order stabilized finite-element algorithm for the reduced visco-resistive MHD equations based on the MFEM finite element library (mfem.org). The scheme is fully implicit, solved with the Jacobian-free Newton-Krylov (JFNK) method with a physics-based preconditioning strategy. Our preconditioning strategy is a generalization of the physics-based preconditioning methods in Chacón et al. (2002) to adaptive, stabilized finite elements. Algebraic multigrid methods are used to invert sub-block operators to achieve scalability. A parallel adaptive mesh refinement scheme with dynamic load-balancing is implemented to efficiently resolve the multi-scale spatial features of the system. Our implementation uses the MFEM framework, which provides arbitrary-order polynomials and flexible adaptive conforming and non-conforming meshes capabilities. Results demonstrate the accuracy, efficiency, and scalability of the implicit scheme in the presence of large scale disparity. The potential of the AMR approach is demonstrated on an island coalescence problem in the high Lundquist-number regime (≥ 10 7 ) with the successful resolution of plasmoid instabilities and thin current sheets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Scalable Implicit Solvers with Dynamic Mesh Adaptation for a Relativistic Drift-Kinetic Fokker–Planck–Boltzmann Model

In this work we consider a relativistic drift-kinetic model for runaway electrons along with a Fokker–Planck operator for small-angle Coulomb collisions, a radiation damping operator, and a secondary knock-on (Boltzmann) collision source. Here, we develop a new scalable fully implicit solver utilizing finite volume and conservative finite difference schemes and dynamic mesh adaptivity. A new data management framework in the PETSc library based on the p4est library is developed to enable simulations with dynamic adaptive mesh refinement (AMR), distributed memory parallelization, and dynamic load balancing of computational work. This framework and the runaway electron solver building on the framework are able to dynamically capture both bulk Maxwellian at the low-energy region and a runaway tail at the high-energy region. To effectively capture features via the AMR algorithm, a new AMR indicator prediction strategy is proposed that is performed alongside the implicit time evolution of the solution. This strategy is complemented by the introduction of computationally cheap feature-based AMR indicators that are analyzed theoretically. Numerical results quantify the advantages of the prediction strategy in better capturing features compared with nonpredictive strategies; and we demonstrate trade-offs regarding computational costs. The robustness with respect to model parameters, algorithmic scalability, and parallel scalability are demonstrated through several benchmark problems including manufactured solutions and solutions of different physics models. We focus on demonstrating the advantages of using implicit time stepping and AMR for runaway electron simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A review of crosstalk polymorphic circuits and their scalability

Using a control variable, the functionality of Polymorphic circuits can be modified, making them adaptable and useful for reconfiguring circuit behavior — all the way from gate level to system level. State-of-the art polymorphic circuits are based on custom non-linear circuit design or emerging devices such as ambipolar FET, configurable magnetic devices etc. While some of these approaches are inefficient in performance, others involve exotic devices. The Crosstalk computing based polymorphic circuits offer a fresh perspective. In Crosstalk, the interconnect interference between nanoscale metal lines is intentionally engineered to exhibit the programmable Boolean logic behavior. This approach relies on the coupling between metal lines and not on the transistors for computing, resulting in better scalability, security by obscurity, and fault tolerance by reconfiguration. Our novel approach is backed by the mathematical formulation that conveys the rationale to generalize and achieve a wide variety of polymorphic circuits. Our experiments, including design, simulation, and Power Performance Area (PPA) characterization results indicate that crosstalk circuits provide significant improvement in transistor count (about 3x), switching energy (2x), and speed (1.5x) for polymorphic logic circuits. In the best-case scenario, the transistor count reduction is 5x. This paper presents Crosstalk computing’s fundamentals, polymorphism and the scalability aspects to compete/co-exist with CMOS for digital logic implementations below 10 nm. Our scalability study uses Open Source 7 nm PDK, considers all process variation aspects and accommodates worst-case scenarios. The study results for various benchmark circuits show that the Crosstalk technology is a viable alternative to CMOS for digital logic implementations below 10 nm, having 48% density, 57% power, and 10% performance gains over equivalent CMOS counterparts. Finally, we compare Crosstalk Polymorphic Circuit design technique with similar approaches described in related works and discuss its features and constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Massively scalable Kerr comb-driven silicon photonic link

Abstract The growth of computing needs for artificial intelligence and machine learning is critically challenging data communications in today’s data-centre systems. Data movement, dominated by energy costs and limited ‘chip-escape’ bandwidth densities, is perhaps the singular factor determining the scalability of future systems. Using light to send information between compute nodes in such systems can dramatically increase the available bandwidth while simultaneously decreasing energy consumption. Through wavelength-division multiplexing with chip-based microresonator Kerr frequency combs, independent information channels can be encoded onto many distinct colours of light in the same optical fibre for massively parallel data transmission with low energy. Although previous high-bandwidth demonstrations have relied on benchtop equipment for filtering and modulating Kerr comb wavelength channels, data-centre interconnects require a compact on-chip form factor for these operations. Here we demonstrate a massively scalable chip-based silicon photonic data link using a Kerr comb source enabled by a new link architecture and experimentally show aggregate single-fibre data transmission of 512 Gb s −1 across 32 independent wavelength channels. The demonstrated architecture is fundamentally scalable to hundreds of wavelength channels, enabling massively parallel terabit-scale optical interconnects for future green hyperscale data centres.

Rizzo, Anthony (ORCID:000000034752797X)↗
DOE OSTI2021

Scalable synthesis of nanoporous atomically thin graphene membranes for dialysis and molecular separations via facile isopropanol-assisted hot lamination

Scalable graphene synthesis and facile large-area membrane fabrication are imperative to advance nanoporous atomically thin membranes (NATMs) for molecular separations. Although chemical vapor deposition (CVD) allows for roll-to-roll high-quality monolayer graphene synthesis, facile transfer with atomically clean interfaces to porous supports for large-area NATM fabrication remains extremely challenging. Sacrificial polymer scaffolds commonly used for graphene transfer typically leave polymer residues detrimental to membrane performance and transfers without polymer scaffolds suffer from low yield resulting in high non-selective leakage through NATMs. In this study, we systematically study the factors influencing graphene NATM fabrication and report on a novel roll-to-roll manufacturing compatible isopropanol-assisted hot lamination (IHL) process that enables scalable, facile and clean transfer of CVD graphene on to polycarbonate track etched (PCTE) supports with coverage ≥99.2%, while preserving support integrity/porosity. We demonstrate fully functional centimeter-scale graphene NATMs that show record high permeances (~2–3 orders of magnitude higher) and better selectivity than commercially available state-of-the-art polymeric dialysis membranes, specifically in the 0–1000 Da range. Our work highlights a scalable approach to fabricate graphene NATMs for practical applications and is fully compatible with roll-to-roll manufacturing processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Rapid scalable fabrication of roll-to-roll slot-die coated flexible perovskite solar cells using intense pulse light annealing

Perovskite solar cells have achieved high efficiencies at the lab scale using batch processes that often may not be scalable. Processes that can be integrated into a continuous manufacturing platform, such as roll-to-roll, where several processes can be carried out sequentially are necessary to develop a new cost-competitive solar technology. In this work, intense pulsed light thermal post-processing is coupled with scalable slot-die coating and air knife evaporation in a high-speed roll-to-roll process of up to 2 m min –1 . The optimization of IPL energy parameters, web speeds, solution flow rates, and concentration of precursor inks yielded a champion power conversion efficiency of 11.24% for a 1 cm 2 roll-to-roll coated device. The overall processing time for a single layer in this process is reduced from minutes to seconds and the overall footprint of the manufacturing process is decreased. Furthermore, this demonstrates the promising capabilities for intensed pulsed light to reduce manufacturing costs and paves a roadmap for scalable and efficient PSCs on a roll-to-roll platform.

14 SOLAR ENERGY↗
DOE OSTI2026

Scalable, low-cost ink-based processing of high-performance silver selenide thermoelectrics

The growing global energy demand and its accelerating contribution to climate change emphasize the urgent need for sustainable energy conversion/harvesting technologies. Thermoelectric (TE) devices offer a compelling route to directly convert waste heat into electricity and enable solid-state cooling without moving parts or harmful refrigerants. Achieving their full potential requires not only higher TE performance (zT) but also scalable, low-cost manufacturing processes. Here, we introduce a transformative ink-based processing approach for scalable manufacturing of high-performance silver selenide-based TE materials and devices. Using a simple, high-throughput ink-mixing and blade coating strategy, our Ag 2 Se-based materials under the optimized composition and processing conditions yield an ultrahigh room-temperature power factor of 2.8 mW m −1 K −2 , over 100% higher than baseline samples and a reproducible figure of merit zT of 1 at room temperature. A thermoelectric generator (TEG) achieves a very competitive power density of 112 mW cm −2 at a 90 °C temperature difference between the hot and cold sides of the device, which is among the highest reported for silver selenide-based TE devices to date. This facile, scalable ink-based processing establishes a practical pathway toward industrial-scale manufacturing and widespread adoption of thermoelectric devices, advancing sustainable energy technologies.

Bappy, Md. Omarsany [University of Notre Dame, IN ↗
DOE OSTI2024

HPC resources for CMS offline computing: An integration and scalability challenge for the Submission Infrastructure

The computing resource needs of LHC experiments are expected to continue growing significantly during the Run 3 and into the HL-LHC era. The landscape of available resources will also evolve, as High Performance Computing (HPC) and Cloud resources will provide a comparable, or even dominant, fraction of the total compute capacity. The future years present a challenge for the experiments’ resource provisioning models, both in terms of scalability and increasing complexity. The CMS Submission Infrastructure (SI) provisions computing resources for CMS workflows. This infrastructure is built on a set of federated HTCondor pools, currently aggregating 400k CPU cores distributed worldwide and supporting the simultaneous execution of over 200k computing tasks. Incorporating HPC resources into CMS computing represents firstly an integration challenge, as HPC centers are much more diverse compared to Grid sites. Secondly, evolving the present SI, dimensioned to harness the current CMS computing capacity, to reach the resource scales required for the HLLHC phase, while maintaining global flexibility and efficiency, will represent an additional challenge for the SI. To preventively address future potential scalability limits, the SI team regularly runs tests to explore the maximum reach of our infrastructure. In this note, the integration of HPC resources into CMS offline computing is summarized, the potential concerns for the SI derived from the increased scale of operations are described, and the most recent results of scalability test on the CMS SI are reported.

Pérez-Calero Yzquierdo, Antonio↗
DOE OSTI2026

Scalable quantum computational science: A perspective from block-encodings and polynomial transformations

Significant developments made in quantum hardware and error correction recently have been driving quantum computing toward practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented, including the construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this perspective serves as a gentle introduction to state-of-the-art quantum algorithms for the computational science community and inspires future development of scalable quantum computational science methodologies that bridge theory and practice.

Bayesian inference↗
DOE OSTI2019

SNAPPy: A snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing

Abstract Human immunodeficiency virus 1 (HIV-1) genome sequencing is routinely done for drug resistance monitoring in hospitals worldwide. Subtyping these extensive datasets of HIV-1 sequences is a critical first step in molecular epidemiology and evolution studies. The clinical relevance of HIV-1 subtypes is increasingly recognized. Several studies suggest subtype-related differences in disease progression, transmission route efficiency, immune evasion, and even therapeutic outcomes. HIV-1 subtyping is mainly done using web-servers. These tools have limitations in scalability and potential noncompliance with data protection legislation. Thus, the aim of this work was to develop an efficient method for large-scale local HIV-1 subtyping. We designed SNAPPy: a snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing. It contains several tasks of phylogenetic inference and BLAST queries, which can be executed sequentially or in parallel, taking advantage of multiple-core processing units. Although it was built for subtyping, SNAPPy is also useful to perform extensive HIV-1 alignments. This tool facilitates large-scale sequence-based HIV-1 research by providing a local, resource efficient and scalable alternative for HIV-1 subtyping. It is capable of analyzing full-length genomes or partial HIV-1 genomic regions (GAG, POL, and ENV) and recognizes more than ninety circulating recombinant forms. SNAPPy is freely available at: https://github.com/PMMAraujo/snappy/releases.

Araújo, Pedro M. M.↗
DOE OSTI2025

Surrogate-constructed scalable-circuits adaptive variational quantum eigensolver in the Schwinger model

Inspired by recent advancements in simulating periodic systems on quantum computers, we develop an approach to further advance the simulation of these systems, named (SC) 2 -ADAPT-VQE. Our approach extends the scalable-circuits ADAPT-VQE framework, which builds an ansatz from a pool of coordinate-invariant operators defined for arbitrarily large, though not arbitrarily small, volumes. Our method uses a classically tractable “surrogate constructed” method to remove irrelevant operators from the pool, reducing the minimum size for which the scalable circuits are defined. Bringing together the scalable circuits and the surrogate constructed approaches forms the core of the (SC) 2 methodology. Our approach allows for a wider set of classical computations on small volumes, which can be used for a more robust extrapolation protocol. While developed in the context of lattice models, the surrogate construction portion is applicable to a wide variety of problems where information about the relative importance of operators in the pool is available. As an example, we use it to compute the properties of the Schwinger model—quantum electrodynamics for a single, massive fermion in 1 +1 dimensions—and show that our method can be used to accurately extrapolate to the continuum limit.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Scalable measurement error mitigation via iterative bayesian unfolding

Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead. Published by the American Physical Society 2024

Pokharel, Bibek (ORCID:0000000342841353)↗
DOE OSTI2016

DISP: Optimizations towards Scalable MPI Startup

Despite the popularity of MPI for high performance computing, the startup of MPI programs faces a scalability challenge as both the execution time and memory consumption increase drastically at scale. We have examined this problem using the collective modules of Cheetah and Tuned in Open MPI as representative implementations. Previous improvements for collectives have focused on algorithmic advances and hardware off-load. In this paper, we examine the startup cost of the collective module within a communicator and explore various techniques to improve its efficiency and scalability. Accordingly, we have developed a new scalable startup scheme with three internal techniques, namely Delayed Initialization, Module Sharing and Prediction-based Topology Setup (DISP). Our DISP scheme greatly benefits the collective initialization of the Cheetah module. At the same time, it helps boost the performance of non-collective initialization in the Tuned module. We evaluate the performance of our implementation on Titan supercomputer at ORNL with up to 4096 processes. The results show that our delayed initialization can speed up the startup of Tuned and Cheetah by an average of 32.0% and 29.2%, respectively, our module sharing can reduce the memory consumption of Tuned and Cheetah by up to 24.1% and 83.5%, respectively, and our prediction-based topology setup can speed up the startup of Cheetah by up to 80%.

Fu, Huansong↗
DOE OSTI2021

A Multiport DC Transformer to Enable Flexible Scalable DC as a Service

The rapid adoption of new DC loads and sources, including photovoltaic arrays, DC fast charging of electric vehicle, battery energy storage and data centers, requires a large amount of DC power conversion. A majority of new deployments also necessitate the integration of multiple DC loads and sources at one site. The traditional approach to serve these new applications relies on multiple standard power converters, each dedicated to a source or load, and results in highly customized systems, with challenging control coordination, complex protection strategies, and poor scalability. Instead, this paper proposes the concept of a multiport DC transformer (MDCT) as a modular building block for realizing a flexible, scalable DC as a Service system and address this rapidly growing need. The MDCT uses the S4T to achieve very tight control of cycle-by-cycle energy exchange between multiple ports, with high efficiency. A single multiport converter replaces 4-6 distinct converters, integrates all energy flows and manages protection. As a result, a new layered control architecture is introduced to ensure stability and scalability of the MDCT, with multiple S4T power converter building blocks connected in parallel to realize a fully modular system and reach the target power levels.

30 DIRECT ENERGY CONVERSION↗