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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Macroscopic modeling of gas permeability in hierarchical micro/nanoporous media: A unified characterization of rarefaction using Klinkenberg theory and equivalent diameter

Estimating gas transport through a hierarchical micro/nanoporous system is challenging due to non-equilibrium gas dynamics. The primary difficulty lies in determining the rarefaction level, because identifying a representative flow dimension in a complex porous system with multiple pore scales is not straightforward. Our study performed a pore-level analysis for gas permeability in dual-scale porous media with varying porosity, throat size, and secondary pore size under different rarefaction conditions. We found that secondary porosity negatively affects permeability due to increased friction forces, with this influence growing as the secondary pore size and porosity increase until the secondary pore becomes comparable to the throat. However, rarefaction reduces the effects of secondary pores due to boundary slip. Traditional Knudsen number (Kn) calculations based on Darcy-defined height failed to accurately describe the rarefaction effects on gas permeability. Instead, we introduced an equivalent diameter to calculate the Kn, which provided an accurate normalization of apparent gas permeability independent of pore geometry. Furthermore, the extended Kozeny–Carman–Klinkenberg model developed in our previous study successfully yielded a macroscopic model for apparent gas permeability in hierarchical micro/nanoporous systems as a function of the traditional Darcy height and porosity.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2021

Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)

Traditionally, watershed-scale hydrology is simulated by distributed hydrological models with offline meteorological forcing data, or by regional regional climate models that link atmospheric and land hydrology interactions. Global climate models (GCMs) are rarely used to study watershed-scale hydrology due to the relatively coarse grid resolution, computationally expensive downscaling, and simplified physical processes. Recently, however, watershed-scale hydrology analysis has become possible in GCMs due to the development of variable-resolution GCMs that dynamically couple the hydrological processes between atmosphere and land systems at fine resolutions in selected regions and coarse resolution elsewhere. In this study, we used the variable-resolution Community Earth System Model (VR-CESM) with refined-resolution (14 km) in the western U.S. and eastern China to evaluate watershed-scale hydrology. Furthermore, we compared the historical VR-CESM outputs with gauge measurements and other hydrological models (e.g., National Water Model in the U.S.) and calibrated the subsurface runoff capacities in four mountainous watersheds. An RCP8.5 projection from 2007 to 2050 is used to estimate the impact of changing precipitation and snow climatology on watershed hydrology. We also analyzed the long-term runoff variability and the possibility of extreme runoff events as simulated by the VR-CESM. Although calibration is not possible in larger-scale watersheds, VR-CESM simulates the long-term annual variability of watersheds and provides insights on climate change impacts on hydrology. We conclude that refined-resolution VR-CESM can be used for watershed-scale hydrology analysis to understand water resources and water balance, in addition to traditional watershed-scale hydrological models. It enables hydrological analysis at multiple watersheds in one simulation and can help understand the two-way dynamics between land surface hydrology and atmospheric processes, and is especially practical for projecting climate change impacts. However, it is challenging to apply VR-CESM for hydrologic analysis in regulated watersheds as human factors (e.g., pumping, irrigation, water diversion) have not been fully addressed in VR-CESM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling and Automation Framework for High IBR Integration in Large-Scale Power Systems

The increasing prevalence of power electronics- interfaced renewable generation sources is leading to a gradual replacement of traditional thermal generation-based synchronous machines. In this context, the modeling of a large-scale power grid that incorporates a significant number of inverter-based resources is crucial for understanding the dynamics and effects of these resources on the power system. This study investigates the positive sequence model of grid-following and grid-forming inverters. Additionally, this work explores the integration of distributed energy resources using population as an indicator of their relative geographic locations. To address challenge to integrate these inverter based resources into a realistic grid of the US Western interconnection, automation scripts are developed to streamline the process of replacing conventional generators with grid-following and grid-forming inverters, as well as allocating distributed energy resources. Different penetration levels of these inverters are considered, and their frequency regulation support following a disturbance is compared through dynamic simulations.

Lyu, Xue↗
DOE OSTI2025

Accuracy Guarantees and Quantum Advantage in Analog Open Quantum Simulation with and without Noise

Many-body open quantum systems, described by Lindbladian master equations, are a rich class of physical models that display complex equilibrium and out-of-equilibrium phenomena which remain to be understood. In this paper, we theoretically analyze noisy analog quantum simulation of geometrically local open quantum systems and provide evidence that this problem both is hard to simulate on classical computers and could be approximately solved on near-term quantum devices. First, given a noiseless quantum simulator, we show that the dynamics of local observables and the fixed-point expectation values of rapidly mixing local observables in geometrically local Lindbladians can be obtained to a precision of ϵ in time that is poly ( ϵ − 1 ) and uniform in system size. Furthermore, we establish that the quantum simulator would provide a superpolynomial advantage, in run-time scaling with respect to the target precision and either the evolution time (when simulating dynamics) or the Lindbladian’s decay rate (when simulating fixed points), over any classical algorithm for these problems, assuming BQP ≠ BPP . We then consider the presence of noise in the quantum simulator in the form of additional geometrically local Lindbladian terms. We show that the simulation tasks considered in this paper are stable to errors; i.e., they can be solved to a noise-limited, but system-size independent, precision. Finally, we establish that, assuming BQP ≠ BPP , there are stable geometrically local Lindbladian simulation problems such that, as the noise rate on the simulator is reduced, classical algorithms must take time superpolynomially longer in the inverse noise rate to attain the same precision as the analog quantum simulator. Published by the American Physical Society 2025

Kashyap, Vikram (ORCID:0000000208195207)↗
DOE OSTI2021

A Hybrid Climate Modeling System Using AI-assisted Process Emulators

This white paper addresses Focus Area II. We advocate developing a hybrid modeling system to improve the understanding of decadal- and longer-scale predictability of high impact water cycle components. This hybrid model combines a partial differential equation (PDE)-based dynamic core with AI/ML based emulators to represent many of the computationally expensive processes in Earth’s climate models. The hybrid modeling system has the potential to exploit emerging graphics processing unit (GPU)-accelerated architectures and allows for the generation of large ensemble (~1000’s) simulations to better characterize the model uncertainty and understand predictability.

58 GEOSCIENCES↗
DOE OSTI2023

A reduced-order model of concentration polarization in reverse osmosis systems with feed spacers

Feed spacers in reverse osmosis systems generate complex fluid flows that limit computational fluid dynamics (CFD) simulations to small length and time scales. That limits our ability to simulate mineral scaling and other membrane fouling phenomena, which occur over longer length and time scales. Thus motivated, we develop a reduced model that replaces the CFD simulation of the velocity field with an analytical model that mimics spacers. This focuses the remaining numerical effort on simulating the advection–diffusion equation governing solute transport. We motivate and validate the model with CFD simulations and bench-scale experiments of spacer filaments in three different arrangements, including cases of unsteady vortex shedding. We show that the model produces a roughly 10,000-fold speedup compared to CFD, and accurately reproduces CFD predictions of not only the average and maximum concentrations, but also the local concentration distribution along the membrane. Here, we also demonstrate the model for simulating a feed channel with a length-to-height ratio of 200. The model provides a simple testbed for exploratory studies of multispecies transport, precipitation, and membrane fouling phenomena for which simulating spacers is often prohibitive.

Computational fluid dynamics↗
DOE OSTI2023

Phonon-enhanced nonlinearities in hexagonal boron nitride

Abstract Polar crystals can be driven into collective oscillations by optical fields tuned to precise resonance frequencies. As the amplitude of the excited phonon modes increases, novel processes scaling non-linearly with the applied fields begin to contribute to the dynamics of the atomic system. Here we show two such optical nonlinearities that are induced and enhanced by the strong phonon resonance in the van der Waals crystal hexagonal boron nitride (hBN). We predict and observe large sub-picosecond duration signals due to four-wave mixing (FWM) during resonant excitation. The resulting FWM signal allows for time-resolved observation of the crystal motion. In addition, we observe enhancements of third-harmonic generation with resonant pumping at the hBN transverse optical phonon. Phonon-induced nonlinear enhancements are also predicted to yield large increases in high-harmonic efficiencies beyond the third.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Simulations of the IR and Raman spectra of water confined in amorphous silica slit pores

Water in nano-scale confining environments is a key element in many biological, material, and geological systems. The structure and dynamics of the liquid can be dramatically modified under these conditions. Probing these changes can be challenging, but vibrational spectroscopy has emerged as a powerful tool for investigating their behavior. A critical, evolving component of this approach is a detailed understanding of the connection between spectroscopic features and molecular-level details. In this paper, this issue is addressed by using molecular dynamics simulations to simulate the linear infrared (IR) and Raman spectra for isotopically dilute HOD in D 2 O confined in hydroxylated amorphous silica slit pores. The effect of slit-pore width and hydroxyl density on the silica surface on the vibrational spectra is also investigated. The primary effect of confinement is a blueshift in the frequency of OH groups donating a hydrogen bond to the silica surface. This appears as a slight shift in the total (measurable) spectra but is clearly seen in the distance-based IR and Raman spectra. Analysis indicates that these changes upon confinement are associated with the weaker hydrogen-bond accepting properties of silica oxygens compared to water molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The origin of the Stokes–Einstein relation in simple dense liquids

Here, we investigate the origin of the universal relation between structural relaxation and diffusion in simple dense liquids, known as the Stokes–Einstein (SE) relation. The fact that this relation, originally derived from a hydrodynamic model of a macroscopic particle in a viscous medium, can describe the microscopic-scale liquid dynamics still eludes understanding. We introduce a new universal measure of structural relaxation in a system of N identical particles based on an explicit decomposition of the configuration space into N! congruent convex polyhedra. This measure makes it possible to quantify the correlation between two distinct particle configurations in terms of their minimal Euclidean distance, optimized with respect to particle permutations. Using this measure alongside a model of independent random walkers under the single-occupancy constraint, we derive a master equation that quantifies the SE relation. It allows us to demonstrate that the universal relation between structural relaxation and diffusion in simple dense liquids is caused by two conditions: (a) the confinement of the dominant density fluctuations to the first coordination shell, manifested by de Gennes narrowing, and (b) Gaussianity of the diffusion process; the former is shown to be violated in low-density fluids, and the latter is known to be violated in supercooled liquids.

Physics - Condensed matter physics↗
DOE OSTI2026

Mixed stochastic-deterministic density functional theoretic decomposition of Kubo–Greenwood conductivities in the projector augmented wave formalism

Pairing the accuracy of the Kohn–Sham density-functional framework with the efficiency of a stochastic algorithmic approach, mixed stochastic-deterministic density functional theory (mDFT) achieves a favorable computational scaling with system sizes and electronic temperatures. We employ the recently developed mDFT formalism to investigate the dynamic charge-transport properties of systems in the warm dense matter regime. The optical conductivity spectra are computed for single- and multi- component mixtures of carbon, hydrogen, and beryllium using two complementary approaches: Kubo–Greenwood in the mDFT picture and real-time time-dependent mDFT. We further devise a decomposition of the Onsager coefficients leading up to the Kubo–Greenwood spectra to exhibit contributions from the deterministic, stochastic, and mixed electronic state transitions at different incident photon energies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Inertia-induced mixing and reaction maximization in laminar porous media flows

Solute transport and biogeochemical reactions in porous and fractured media flows are controlled by mixing, as are subsurface engineering operations such as contaminant remediation, geothermal energy production, and carbon sequestration. Porous media flows are generally regarded as slow, so the effects of fluid inertia on mixing and reaction are typically ignored. Here, we demonstrate through microfluidic experiments and numerical simulations of mixing-induced reaction that inertial recirculating flows readily emerge in laminar porous media flows and dramatically alter mixing and reaction dynamics. An optimal Reynolds number that maximizes the reaction rate is observed for individual pore throats of different sizes. This reaction maximization is attributed to the effects of recirculation flows on reactant availability, mixing, and reaction completion, which depend on the topology of recirculation relative to the boundary of the reactants or mixing interface. Recirculation enhances mixing and reactant availability, but a further increase in flow velocity reduces the residence time in recirculation, leading to a decrease in reaction rate. The reaction maximization is also confirmed in a flow channel with grain inclusions and randomized porous media. Interestingly, the domain-wide reaction rate shows a dramatic increase with increasing Re in the randomized porous media case. This is because fluid inertia induces complex three-dimensional flows in randomized porous media, which significantly increases transverse spreading and mixing. This study shows how inertial flows control reaction dynamics at the pore scale and beyond, thus having major implications for a wide range of environmental systems.

Chen, Michael A. (ORCID:0000000294614710)↗
DOE OSTI2020

Enhancing Graphene Plasmonic Device Performance via its Dielectric Environment

Graphene plasmons provide a compelling avenue toward chip-scale dynamic tuning of infrared light. Dynamic tunability emerges through controlled alterations in the optical properties of the system defining graphene’s plasmonic dispersion. Typically, electrostatic induced alterations of the carrier concentration in graphene working in conjunction with mobility have been considered the primary factors dictating plasmonic tunability. We find here that the surrounding dielectric environment also plays a primary role, dictating not just the energy of the graphene plasmon but so too the magnitude of its tuning and spectral width. To arrive at this conclusion, poles in the imaginary component of the reflection coefficient are used to efficiently survey the effect of the surrounding dielectric on the tuning of the graphene plasmon. By investigating several common polar materials, optical phonons (i.e., the Reststrahlen band) of the dielectric substrate are shown to appreciably affect not only the plasmon’s spectral location but its tunability, and its resonance shape as well. In particular, tunability is maximized when the resonances are spectrally distant from the Reststrahlen band, whereas sharp resonances (i.e., high-Q) are achievable at the band’s edge. Overall, these observations both underscore the necessity of viewing the dielectric environment in aggregate when considering the plasmonic response derived from two-dimensional materials and provide heuristics to design dynamically tunable graphene-based infrared devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Compiler and Runtime Approaches to Enable Large-Scale Irregular Programs. Final report, July 2013 - July 2019

While regular algorithms, characterized by operations on dense matrices and arrays, have long been the mainstay of scientific, high-performance computing, irregular algorithms, which feature unpredictable accesses to pointer-based data structures, are becoming increasingly common in high performance computing, arising in graph analysis, data mining and visualization, among other domains. Unfortunately, the defining characteristics of irregular applications, their dynamic, unpredictable, data-dependent access patterns and data layouts, make achieving high performance on large scale systems difficult. Scaling applications to peta- and exa-scale requires carefully controlling communication and data movement and placement, an inherently difficult task when access patterns and data layouts are unpredictable! Most irregular applications that attain high performance must be painstakingly hand-written and hand-tuned, with few common principles or paradigms uniting various implementations and easing future development. Despite the increasing importance of irregular applications, there is little programmer knowledge, and even less compiler ability, devoted to optimizing them. This project aims to solve these problems. By allowing programmers to write irregular applications in high level forms, with at most a few annotations highlighting key structural properties, programmers can focus on developing their algorithms and methods. The compiler and run-time system can take on the tedious task of optimizing the application for execution at large scales, and can automatically provide efficient implementations. This will provide portability and ease maintenance for existing irregular applications, but, more importantly, open up whole new domains of computational science to large-scale, high-performance simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Atomic Ejecta Source Optical Probe (AESOP) (L3 Milestone Report FY2021 Work Package)

As part of its mission to ensure the reliability of the nation's nuclear weapons stockpile, the NNSA has funded a broad range of projects to characterize the mass of material ejected from shocked metal surfaces with special emphasis placed on determining the size distribution of macroscopic "ejecta" particles. Substantial work has been done for particles with diameters down to roughly 1 μm, but little data is available for smaller particles, and no data exist for the amount of atomic-scale ejecta produced in such dynamic environments. Such data are important due to the implications for weapons systems behavior but also for distinguishing between different potential ejecta production mechanisms, such as Rayleight-Taylor instability or the shallow-bubble-collapse phenomenon recently proposed by G. Maskally.

36 MATERIALS SCIENCE↗
DOE OSTI2023

MAGNET Scaling and Methodology

The purpose of this study was to analyze the heat transfer of the Microreactor Agile Non-nuclear Experimental Testbed (MAGNET) within the Dynamic Energy Transport and Integration Laboratory (DETAIL) and develop scaling equations and models to couple with other systems. Hierarchical Two-Tiered Scaling (H2TS) methodologies were applied to DETAIL’s MAGNET facility to scale and project data sets while conserving the observed behavior based on first principles. The MAGNET system was successfully scaled using H2TS and multiple system parameters were determined or calculated from experimental data including steady state and transient data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Diagnosing Barren Plateaus with Tools from Quantum Optimal Control

Variational Quantum Algorithms (VQAs) have received considerable attention due to their potential for achieving near-term quantum advantage. However, more work is needed to understand their scalability. One known scaling result for VQAs is barren plateaus, where certain circumstances lead to exponentially vanishing gradients. It is common folklore that problem-inspired ansatzes avoid barren plateaus, but in fact, very little is known about their gradient scaling. In this work we employ tools from quantum optimal control to develop a framework that can diagnose the presence or absence of barren plateaus for problem-inspired ansatzes. Such ansatzes include the Quantum Alternating Operator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others. With our framework, we prove that avoiding barren plateaus for these ansatzes is not always guaranteed. Specifically, we show that the gradient scaling of the VQA depends on the degree of controllability of the system, and hence can be diagnosed through the dynamical Lie algebra $\mathfrak{g}$ obtained from the generators of the ansatz. We analyze the existence of barren plateaus in QAOA and HVA ansatzes, and we highlight the role of the input state, as different initial states can lead to the presence or absence of barren plateaus. Taken together, our results provide a framework for trainability-aware ansatz design strategies that do not come at the cost of extra quantum resources. Moreover, we prove no-go results for obtaining ground states with variational ansatzes for controllable system such as spin glasses. Our work establishes a link between the existence of barren plateaus and the scaling of the dimension of $\mathfrak{g}$.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Time-series learning of latent-space dynamics for reduced-order model closure

In this work, we study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equations for an advection-dominated problem given by the viscous Burgers equation. Our formulation is devised in a nonintrusive manner with an equation-free evolution of dynamics in a reduced space with the latter being obtained through a proper orthogonal decomposition. In addition, we leverage the sequential nature of learning for both LSTMs and NODEs to demonstrate their capability for closure in systems that are not completely resolved in the reduced space. We assess our hypothesis for two advection-dominated problems given by the viscous Burgers equation. We observe that both LSTMs and NODEs are able to reproduce the effects of the absent scales for our test cases more effectively than does intrusive dynamics evolution through a Galerkin projection. This result empirically suggests that time-series learning techniques implicitly leverage a memory kernel for coarse-grained system closure as is suggested through the Mori–Zwanzig formalism.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A convection-permitting dynamically downscaled dataset over the Midwestern United States

Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.

54 ENVIRONMENTAL SCIENCES↗