Search NASA⌕ Search

SEARCH · Search NASA

Results for “scalable”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Scalable Generation of High-fidelity Synthetic Population Ensembles

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the U.S. via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. The study involves two scenarios: creating ensembles for (1) 17 U.S. metropolitan areas in 2019 and (2) full U.S. Census Divisions in 2023, with each scenario consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system within a research cloud, comprised of virtual containerizations, GPU-enhanced functionality, and orchestrated deployments of UrbanPop’s maturing Likeness Python ecosystem. Results demonstrate we maintained high-fidelity approximations of residential totals by areas of interest and the demographic characteristics of neighborhoods while reducing manual workflow burdens. Finally, we discuss plans to fine-tune and further develop our automated workflows for truly distributed job orchestration to increase computational efficiency, as well as provide an outlook for broadening applications of the ensembles.

Cluster computing↗
DOE OSTI2025

Scalability and Effectiveness of Smart Charge Management

The rise in electric vehicle (EV) adoption presents growing challenges for power grids, particularly from simultaneous residential charging, which can cause voltage fluctuations and increase feeder peak loads. Baltimore Gas and Electric (BGE), with support from the U.S. Department of Energy, initiated a pilot program to evaluate managed residential EV charging through Smart Charge Management (SCM). This study analyzes real-world charging behavior data from the pilot and feeder-level base loads from BGE to simulate residential charging scenarios through 2035 across the Washington, DC–Baltimore region. Grid impacts under unmanaged charging are compared to three SCM strategies: TOU-immediate, TOU-distributed, and Load Balancing. Results show that the magnitude of peak reduction is highly feeder-dependent. Some feeders achieve reductions of more than 40% at high enrollment levels, while others show improvements closer to 10–15%. This heterogeneity reflects differences in baseline feeder load shapes, EV penetration, and plug-in behavior across customers. Results also highlight trade-offs between shifting load away from peak periods and minimizing secondary demand peaks, offering practical insights for future utility program design.

Electric vehicle↗
DOE OSTI2024

Scalable semidefinite programming approach to variational embedding for quantum many-body problems

In quantum embedding theories, a quantum many-body system is divided into localized clusters of sites which are treated with an accurate ‘high-level’ theory and glued together self-consistently by a less accurate ‘low-level’ theory at the global scale. The recently introduced variational embedding approach for quantum many-body problems combines the insights of semidefinite relaxation and quantum embedding theory to provide a lower bound on the ground-state energy that improves as the cluster size is increased. The variational embedding method is formulated as a semidefinite program (SDP), which can suffer from poor computational scaling when treated with black-box solvers. Here, we exploit the interpretation of this SDP as an embedding method to develop an algorithm which alternates parallelizable local updates of the high-level quantities with updates that enforce the low-level global constraints. Moreover, we show how translation invariance in lattice systems can be exploited to reduce the complexity of projecting a key matrix to the positive semidefinite cone.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A scalable multidimensional fully implicit solver for Hall magnetohydrodynamics

We propose an optimally performant fully implicit algorithm for the Hall magnetohydrodynamics (HMHD) equations based on multigrid-preconditioned Jacobian-free Newton-Krylov methods. HMHD is a challenging system to solve numerically because it supports stiff fast dispersive waves. The preconditioner is formulated using an operator-split approximate block factorization (Schur complement), informed by physics insight. We use a vector-potential formulation (instead of a magnetic field one) to allow a clean segregation of the problematic $\nabla$ x $\nabla$ x operator in the electron Ohm's law subsystem. This segregation allows the formulation of an effective damped block-Jacobi smoother for multigrid. We demonstrate by analysis that our proposed block-Jacobi iteration is convergent and has the smoothing property. The resulting HMHD solver is verified linearly with wave propagation examples, and nonlinearly with the GEM challenge reconnection problem by comparison against another HMHD code. We demonstrate the excellent algorithmic and parallel performance of the algorithm up to 16384 MPI tasks in two dimensions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Performance evaluation of automated data-driven feature extraction and selection methods for practical and scalable building energy consumption prediction models

Here, this study quantifies the impact of automated feature engineering methods (feature extraction and selection) on the quality and accuracy of machine learning models that predict building energy consumption. The case study compares model performance for three main scenarios: baseline (no feature extraction and selection), feature extraction only, and feature extraction combined with feature selection (filter and/or wrapper methods) for fully trained machine learning models for 200 metered/sub-metered energy measurements across 118 real buildings. For consistency, the same machine learning model architecture (a black box deep learning neural network with probabilistic forecast output) was used for all scenarios. Based on results, all feature engineering methods provided noticeable prediction accuracy improvements (e.g., 29%-68% median prediction improvement) compared to baseline scenarios. However, in this application, feature selection methods provide little practical value due to their limited performance gains and high computational cost. Smarter algorithm development supported by better computational environments will be needed before feature selection methods can reliably and efficiently improve predictive model performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Amorphous complexion-aided sintering enables scalable processing of bulk nanocrystalline Cu-Zr with high strength and compressive plasticity

Nanocrystalline alloys can have exceptional strengths, yet due to limited microstructural stability it is difficult to fabricate bulk pieces through traditional processing routes that retain nanosized grains. In this study, centimeter-sized Cu-Zr alloy pellets were fabricated via a simple and improved powder metallurgy processing route. Different consolidation temperatures and times were employed to investigate the effect of amorphous grain boundary complexions on densification and the resulting mechanical properties. Bulk compression tests were carried out, with the samples that were hot pressed at 900 °C for 10 h exhibiting an excellent combination of average yield strength of 722 ± 45 MPa and average failure strain of 25.3 ± 2.4 %. Furthermore, we find that a powder processing route which enables amorphous complexion-assisted sintering leads to samples that (1) reach full density without requiring quenching treatments or other complex processing, (2) demonstrate appreciable plasticity, and (3) have strength that competes with commercially available high-strength Cu alloys.

Bulk nanocrystalline alloy↗
DOE OSTI2025

Scalable Implementation of Mean-Field and Correlation Methods Based on Lie-Algebraic Similarity Transformation of Spin Hamiltonians in the Jordan–Wigner Representation

Recent work has highlighted that the strong correlation inherent in spin Hamiltonians can be effectively reduced by mapping spins to Fermions via the Jordan−Wigner transformation (JW). The Hartree−Fock method is straightforward in the Fermionic domain and may provide a reasonable approximation to the ground state. Correlation with respect to the Fermionic mean field can be recovered based on Lie-algebraic similarity transformation (LAST) with two-body correlators. Specifically, a unitary LAST variant eliminates the dependence on site ordering, while a nonunitary LAST yields size-extensive correlation energies. Whereas the first recent demonstration of such methods was restricted to small spin systems, we present efficient implementations using analytical gradients for the optimization with respect to the mean-field reference and the LAST parameters, thereby enabling the treatment of larger clusters, including systems with local spins s > $\frac{1}{2}$.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Scalable Quantum Monte Carlo Method for Polariton Chemistry via Mixed Block Sparsity and Tensor Hypercontraction Method

We present a reduced-scaling auxiliary-field quantum Monte Carlo (AFQMC) framework designed for large molecular systems and ensembles, with or without coupling to optical cavities. Our approach leverages the natural block sparsity of the Cholesky decomposition (CD) of electron repulsion integrals in molecular ensembles and employs tensor hypercontraction (THC) to efficiently compress low-rank Cholesky blocks. By representing the Cholesky vectors in a mixed format, keeping high-rank blocks in block-sparse form and compressing low-rank blocks with THC, we reduce the scaling of exchange-energy evaluation from quartic to robust cubic in the number of molecular orbitals N, while lowering memory from cubic toward quadratic. Benchmark analyses on one-, two-, and three-dimensional molecular ensembles (up to ∼1,200 orbitals) show that (a) the number of nonzeros in Cholesky tensors grows linearly with system size across dimensions; (b) the average numerical rank increases sublinearly and does not saturate at these sizes; and (c) rank heterogeneity─some blocks nearly full rank and many low rank, naturally motivates the proposed mixed block sparsity and THC scheme for efficient calculation of exchange energy. In conclusion, we demonstrate that the mixed scheme yields cubic wall-time scaling with favorable prefactors and preserves AFQMC accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Black Phosphorus for Mid-Infrared Optoelectronics: Photophysics, Scalable Processing, and Device Applications

High efficiency mid-infrared (λ = 3-8 μm) light emitters and photodetectors are pivotal for advancing next-generation optoelectronics. However, narrow-bandgap semiconductors face fundamental challenges such as pronounced nonradiative carrier recombination and thermally generated noise, which impede device performance. Recently, two-dimensional layered black phosphorus (BP) and its alloys have attracted substantial interest for mid-infrared device applications, demonstrating superior performance relative to conventional III-V and II-VI semiconductors with similar bandgaps. In this review, we discuss the optical properties of BP, contrasting these with those of covalent compounds. Owing to its inherently self-terminated surface structure and reduced nonradiative recombination, BP exhibits high performance in light emission and photodetection at room temperature. Furthermore, this review highlights recent advances in the large-area processing of BP thin films, paving the way for practical device applications and integration. Finally, we explore ongoing challenges and emerging opportunities in the utilization of BP for functional mid-infrared devices.

Higashitarumizu, Naoki↗
DOE OSTI2026

Electrode Modification by Laser Ablation to Overcome Mechanical and Scalability Limitations of Solid-State Prealkylation of Electrode Architectures

In this work, laser ablation of Si electrode surfaces is utilized to provide localized strain relief during the Solid State Prealkylation of Electrode Architectures (SPEAR) process, in addition to volumetric expansion relief in the form of free volume channels across the electrode. A hexagonal mesh patterning was employed via laser ablation, where the hexagonal diameter (pitch) was varied from 250 to 1000 µm. It was demonstrated that these channels serve as expansion voids, accommodating the increase in electrode thickness observed during SPEAR and preventing cracking at the electrode surface. Optical microscopy and Raman mapping confirm that Li diffusion is the rate-limiting solid-state process, requiring a 24 h rest period to achieve homogeneous LixSi alloying through the electrode bulk. Electrochemical evaluations in full cells show that a 1000 μm pitch pattern yields a high initial discharge capacity of 2485 mA h g–1 and 86.4% capacity retention after 80 cycles, whereas smaller pitches (<500 μm) result in lithium plating at 4.1 V in full cells paired with NMC 811 due to excessive current collector exposure from the ablation process. XPS analysis reveals that the laser treatment modifies the electrode surface by increasing the Si–O and C–O species, changing the SEI composition observed via an increase in fluorophosphates upon cycling.

Musgrove, Amanda [ORNL] (ORCID:0009000220910389)↗
DOE OSTI2024

Fluorine-Tuned Carbon-Based Nickel Single-Atom Catalysts for Scalable and Highly Efficient CO 2 Electrocatalytic Reduction

Electrocatalytic CO 2 reduction is garnering significant interest due to its potential applications in mitigating CO 2 and producing fuel. However, the scaling up of related catalysis is still hindered by several challenges, including the cost of the catalytic materials, low selectivity, small current densities to maintain desirable selectivity. In this study, Fluorine (F) atoms were introduced into an N-doped carbon-supported single nickel (Ni) atom catalyst via facile polymer-assisted pyrolysis. This method not only maintains the high atom utilization efficiency of Ni in a cost-effective and sustainable manner but also effectively manipulates the electronic structure of the active Ni-N 4 site through F doping. The catalyst has also been further optimized by controlling the F states, including convalent and semi-ionic states, by adjusting the fluorine sources involved. Consequently, this catalyst with unique structure exhibited comparable electrocatalytic performance for CO 2 -to-CO conversion, achieving a Faradaic efficiency (FE) of over 99% across a wide potential range and an exceptional CO evolution rate of 9.5 x 10 4 h -1 at -1.16 V vs reversible hydrogen electrode (RHE). It also delivered a practical current of 400 mA cm -2 while maintaining more than 95% CO FE. Experimental analysis combined with density functional theory (DFT) calculations have also shown that F-doping modifies the electron configuration at the central Ni-N 4 sites. In conclusion, this modification lowers the energy barrier for CO 2 activation, thereby facilitating the production of the crucial *COOH intermediate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗