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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Comparison of GPU-Accelerated Multiphase CFD Solvers on the Polaris Supercomputer: Part 1

This report is in support of the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program sponsored by the U.S. Department of Energy (USDOE). With INCITE-level resources, one project, titled BubblyFlow, was granted computational resources for the 2025 calendar year on the Polaris supercomputer at the Argonne Leadership Computing Facility (ALCF). The project aims to conduct simulations to understand the fundamental characteristics of turbulent bubbly flow phenomena in nature. Staff at the ALCF and Argonne’s Computational Science division, along with collaborators at the City College of New York and University of Illinois at Chicago, helped a summer student to assess the accuracy and performance of two high performance computing (HPC) codes. Both codes, ImExLBM and FluTAS, are fundamentally different in their mathematical and numerical modeling. However, both may be used to solve the same physical problem. The collaboration sought to better understand the differences between both codes in terms of accuracy and efficiency. This would ultimately help the BubblyFlow project better utilize resources and establish a knowledge-base of code capabilities in future simulation campaigns. We compare ImExLBM and FluTAS, two high-performance multiphase computational fluid dynamics (CFD) solvers, in terms of physical fidelity, time-to-solution, and parallel efficiency. We validate ImExLBM (Implicit-Explicit Lattice Boltzmann Method) against a canonical benchmark and assess it’s performance relative to FluTAS (Fluid Transport Accelerated Solver), a well-established open-source CFD code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High Volume Production of Nanoscale Elements

The utility of nanoscale elements spans numerous potential markets including healthcare, communications, energy, optics, and structures. The energy market in particular will require large volumes and is price sensitive. In this talk we will discuss strategies to scale nanoscale elements, including nanoparticle inks and nanoscale thin films, for the large area deposition (hundreds of square meters) using high through put processes. Historically, roll-to-roll production of thin films has been carried out at very high speeds over large areas using solution phase inks. Nanoparticles are produced in a bottom-up approach in solution phase that simplifies the processing and is amenable to ink formulations. Sintering and annealing of the thin films can be increased by using radiation thermal processing that integrates easily into roll-to-roll manufacturing. Results of printed films on the order of tens of nanometers to hundreds of nanometers are presented including studies of morphologies and uniformity over large areas. Solar cells devices using the roll-to-roll setup are demonstrated and a techno-economic analysis of the manufacturing is proposed. These results are presented with a comparison to the current photovoltaic technologies in production.

Druffel, Thad↗
DOE OSTI2024

Nonlinear solution of classical three-wave interaction via finite-dimensional quantum model

The quantum three-wave interaction, the lowest-order nonlinear interaction in plasma physics, describes energy–momentum transfer between three resonant waves in the quantum regime. We describe how it may also act as a finite-degree-of-freedom approximation to the classical three-wave interaction in certain circumstances. By promoting the field variables to operators, we quantize the classical system, show that the quantum system has more free parameters than the classical system and explain how these parameters may be selected to optimize either initial or long-term correspondence. We then numerically compare the long-time quantum–classical correspondence far from the fixed point dynamics. We discuss the Poincaré recurrence of the system and the mitigation of quantum scrambling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

On the replica structure of Sachdev-Ye-Kitaev model

We investigate existence of replica off-diagonal solutions in the field-theoretical description of Sachdev-Ye-Kitaev model. To this end we evaluate a set of local and non-local dynamic correlation functions in the long time limit. We argue that the structure of the soft-mode Schwarzian action is qualitatively different in replica-diagonal vs. replica-off-diagonal scenarios, leading to distinct long-time predictions for the correlation functions. We then evaluate the corresponding correlation functions numerically and compare the simulations with analytical predictions of replica-diagonal and replica-off-diagonal calculations. We conclude that all our numerical results are in a quantitative agreement with the theory based on the replica-diagonal saddle point plus Schwarzian and massive Gaussian fluctuations (the latter do contain replica off-diagonal components). This seems to exclude any contributions from replica-off-diagonal saddle points, at least on the time scales shorter than the inverse many-body level spacing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Accelerated steady-state electrostatic particle-in-cell simulation of Langmuir probes

First-principles particle-in-cell (PIC) simulation is a powerful tool for understanding plasma behavior, but this power often comes at great computational expense. Artificially reducing the ion/electron mass ratio is a time-honored practice to reduce simulation costs. Usually, this is a severe approximation. However, for steady-state collisionless, electrostatic (Vlasov–Poisson) systems, the solution with reduced mass ratio can be scaled to the solution for the real mass ratio, with no approximation. This “scaled mass” method, which works with already-existing PIC codes, can reduce the computation time for a large class of electrostatic PIC simulations by the square root of the mass ratio. The particle distributions of the resulting steady state must be trivially rescaled to yield the true distributions, but the self-consistent electrostatic field is independent of the mass ratio. This method is equivalent to “numerical timestepping,” an approach that evolves electron and ion populations with different time steps. Numerical timestepping can be viewed as a special case of the speed-limited PIC (SLPIC) method, which is not restricted to steady-state phenomena. Although the scaled-mass approach is simplest, numerical timestepping and SLPIC more easily generalize to include other effects, such as collisions. The equivalence of these new approaches is demonstrated by applying them to simulate a cylindrical Langmuir probe in electron–argon plasma, speeding up simulation by two orders of magnitude. Methods such as SLPIC can therefore play an invaluable role in interpreting probe measurements by including geometric effects, collisions, secondary emission, and non-Maxwellian distributions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

i-SPin: an integrator for multicomponent Schrödinger-Poisson systems with self-interactions

Abstract We provide an algorithm and a publicly available code to numerically evolve multicomponent Schrödinger-Poisson (SP) systems with a SO( n ) symmetry, including attractive or repulsive self-interactions in addition to gravity. Focusing on the case where the SP system represents the non-relativistic limit of a massive vector field, non-gravitational self-interactions (in particular spin-spin interactions) introduce complexities related to mass and spin conservation which are not present in purely gravitational systems. We address them with an analytical solution for the `kick' step in the algorithm, where we are able to decouple the multicomponent system completely. Equipped with this analytical solution, the full field evolution is second order accurate, preserves spin and mass to machine precision, and is reversible. Our algorithm allows for an expanding universe relevant for cosmology, and the inclusion of external potentials relevant for laboratory settings.

Astronomy & Astrophysics↗
DOE OSTI2021

Variational Self-Consistent Theory for Beam-Loaded Cavities

A new variational theory is presented for beam loading in microwave cavities. The beam{field interaction is formulated as a dynamical interaction whose stationarity according to Hamilton’s principle will naturally lead to steady-state solutions that indicate how a cavity’s resonant frequency, Q and optimal coupling coefficient will detune as a result of the beam loading. A driven cavity Lagrangian is derived from first principles, including the effects of cavity wall losses, input power and beam interaction. The general formulation is applied to a typical klystron input cavity to predict the appropriate detuning parameters required to maximize the gain (or modulation depth) in the average Lorentz factor boost, (Δ γ). Numerical examples are presented, showing agreement with the general detuning trends previously observed in the literature. The developed formulation carries several advantages for beam-loaded cavity structures. It provides a self-consistent model for the dynamical (nonlinear) beam{field interaction, a procedure for maximizing gain under beam-loading conditions, and a useful set of parameters to guide cavity-shape optimization during the design of beam-loaded systems. Enhanced clarity of the physical picture underlying the problem seems to be gained using this approach, allowing straightforward inclusion or exclusion of different field configurations in the calculation and expressing the final results in terms of measurable quantities. Two field configurations are discussed for the klystron input cavity, using finite magnetic confinement or no confinement at all. Formulating the problem in a language that is directly accessible to the powerful techniques found in Hamiltonian dynamics and canonical transformations may potentially carry an additional advantage in terms of analytical computational gains, under suitable conditions

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

A Low-Rank Solver for the Navier--Stokes Equations with Uncertain Viscosity

In this work, we study an iterative low-rank approximation method for the solution of the steady-state stochastic Navier--Stokes equations with uncertain viscosity. The method is based on linearization schemes using Picard and Newton iterations and stochastic finite element discretizations of the linearized problems. For computing the low-rank approximate solution, we adapt the nonlinear iterations to an inexact and low-rank variant, where the solution of the linear system at each nonlinear step is approximated by a quantity of low rank. This is achieved by using a tensor variant of the GMRES method as a solver for the linear systems. We explore the inexact low-rank nonlinear iteration with a set of benchmark problems, using a model of flow over an obstacle, under various configurations characterizing the statistical features of the uncertain viscosity, and we demonstrate its effectiveness by extensive numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.

Artificial intelligence↗
DOE OSTI2019

Epi-Regularization of Risk Measures

Uncertainty pervades virtually every branch of science and engineering, and in many disciplines, the underlying phenomena can be modeled by partial differential equations (PDEs) with uncertain or random inputs. This work is motivated by risk-averse stochastic programming problems constrained by PDEs. These problems are posed in infinite dimensions, which leads to a significant increase in the scale of the (discretized) problem. In order to handle the inherent nonsmoothness of, for example, coherent risk measures and to exploit existing solution techniques for smooth, PDE-constrained optimization problems, we propose a variational smoothing technique called epigraphical (epi-)regularization. Here, we investigate the effects of epi-regularization on the axioms of coherency and prove differentiability of the smoothed risk measures. In addition, we demonstrate variational convergence of the epi-regularized risk measures and prove the consistency of minimizers and first-order stationary points for the approximate risk-averse optimization problem. We conclude with numerical experiments confirming our theoretical results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Statistical Distributions for Mesh Independent Solutions in ALEGRA

The representation of material heterogeneity (also referred to as "spatial variation") plays a key role in the material failure simulation method used in ALEGRA. ALEGRA is an arbitrary Lagrangian-Eulerian shock and multiphysics code developed at Sandia National Laboratories and contains several methods for incorporating spatial variation into simulations. A desirable property of a spatial variation method is that it should produce consistent stochastic behavior regardless of the mesh used (a property referred to as "mesh independence"). However, mesh dependence has been reported using the Weibull distribution with ALEGRA's spatial variation method. This report describes efforts towards providing additional insight into both the theory and numerical experiments investigating such mesh dependence. In particular, we have implemented a discrete minimum order statistic model with properties that are theoretically mesh independent.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Comparative Study of Variations in Quantum Approximate Optimization Algorithms for the Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most often-used NP-hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem. By adopting an improved qubit encoding strategy and a layer-wise learning optimization protocol, we present numerical results obtained from the gate-based digital quantum simulator, specifically targeting TSP instances with 3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixer designs, considering their performances in terms of numerical accuracy and optimization cost. Notably, we find that a well-balanced QAOA mixer design exhibits more promising potential for gate-based simulators and realistic quantum devices in the long run, an observation further supported by our noise model simulations. Furthermore, we investigate the sensitivity of the simulations to the TSP graph. Overall, our simulation results show that the digital quantum simulation of problem-inspired ansatz is a successful candidate for finding optimal TSP solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Solution-Processed Composite Transparent Metal Network Conductors for Next Generation Scalable PV

Transparent conductors (TCs) are essential components of thin film PV cells. Inherently, ITO and other related TC oxides, have fundamental trade-offs that balance conductivity versus optical transmission losses from metallic absorption and reflectivity. Furthermore, the ITO sputtering, materials and patterning costs represent one of the highest cost elements in the thin film cell process. As we consider emerging technologies such as roll to roll processing on lower temperature substrates, the problem is compounded by high ITO processing temperatures. Low temperature alternatives do exist, however, they cannot reach the metrics for high efficiency PV on their own. Numerical modeling has shown that combining high transparency, modestly conducting films with printed conductive grids can provide a TC with a 30% reduction in power loss relative to ITO. During this talk, current work on nanowire and printed TC films will be presented and the scale-up pathways for these technologies will be discussed.

14 SOLAR ENERGY↗
DOE OSTI2024

A simple solution to the Rietveld refinement recipe problem

Rietveld refinements are widely used for many purposes in the physical sciences. Conducting a Rietveld refinement typically requires expert input because correct results may require that parameters be added to the fit in the proper order. This order will depend on the nature of the data and the initial parameter values. A mechanism for computing the next parameter to add to the refinement is shown. The fitting function is evaluated with the current parameter value set and each parameter incremented and decremented by a small offset. This provides the partial derivatives with respect to each parameter, along with information to discriminate meaningful values from numerical computational errors. The implementation of this mechanism in the open-source GSAS-II program is discussed. This new method is discussed as an important step towards the development of automated Rietveld refinement technology.

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

Leptogenesis in automatic Nelson-Barr models

In this study, we numerically show that automatic Nelson-Barr models with new chiral fermions can simultaneously solve the strong CP problem and generate the observed baryon asymmetry via high-scale leptogenesis. In these models, all CP violation arises from a single spontaneous symmetry-breaking scale, linking the origin of quark and lepton CP phases. Using conservative assumptions and minimal dynamics, we identify a viable parameter window where successful leptogenesis occurs without spoiling the quality of the strong CP solution. Models with vector-like fermions face tension within this leptogenesis scenario. A key prediction is a correlation between the baryon asymmetry and the induced QCD vacuum angle shift. Remarkably, we find that the majority of the available parameter space is within reach of current and future nucleon EDM experiments.

CP violation↗
DOE OSTI2022

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the “demand-driven” properties of the SP and DRO solutions. Furthermore, our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Fast Computational Algorithms for Partial Differential Equations and Uncertainty Quantifications

This project concerned the construction, testing and analysis of computational algorithms for solving parameterized and stochastic partial differential equations. The study and understanding of equations of this type is of fundamental importance in numerous engineering and scientific applications. Examples include simulation of plasma dynamics in models of electric propulsion and nuclear fusion, simulation of multiphase flows, such as the flow of water, gas and oil in reservoirs, and structural analysis of the dependence of structures on materials. Parametrization is used in such settings when properties of the models such as viscosity of fluids or electric resistivity of materials are not precisely understood and instead are treated as random variables. The resulting solutions are themselves random, and having such solutions will enable engineers to use probabilistic methods to assess the likelihood of events, for example, whether a pollutant in a liquid will exceed a limit, and to use such analyses to develop ways to ensure positive outcomes. Construction of accurate (high resolution) computational solutions is expensive, requiring significant computer time and computational resources, and there is need to reduce computational cost to make simulation useful and effective. The aim of the project was to construct algorithms to efficiently compute surrogate solutions to parameterized problems to allow for efficient and accurate simulation. The technical approach used focused on two related strategies, based on rank-reduction methods and reduced-order models. These methods construct surrogate solutions of parameter-dependent models by projection or interpolation into low-dimensional approximation spaces. Cost savings are achieved if the low-dimensional spaces can be identified and constructed efficiently and if the resulting low-dimensional algebraic systems can be solved cheaply. Accomplishments include: Theoretical and empirical demonstration of the effectiveness of fast multigrid solution strategies for computing low-rank representations of parameter-dependent solutions to discrete partial differential equations, including the first proof establishing so-called textbook convergence properties for low-rank methods. Development of efficient solution algorithms for solving nonlinear parameter-dependent partial differential equations used in models of fluid dynamics. Developent of efficient algorithms for low-rank representation of solutions of time-dependent simulations of fluid dynamics using multi-dimensional tensor representations of solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Mutually beneficial outcomes for hydropower expansion and environmental protection at a basin scale

Reshaping the scale of planning for hydropower development, from reaches to basin-scales, has been recommended as a more effective way to ameliorate the environmental impacts of hydropower. One approach is identifying mutually exclusive areas where development is precluded for conservation purposes and areas of low conservation value that present fewer barriers to development. This strategy, however, is less adoptable in developed countries where hydropower is already widespread and large-scale construction of new dams is unlikely. To broaden the adoption of basin-scale planning, alternative approaches and planning tools are needed for identifying mutually beneficial opportunities for simultaneous increases in hydropower capacity while improving environmental conditions. In this study, we present the Basin Scale Opportunity Assessment as a methodology to improve environmental conditions through either direct (on-site) or indirect (off-site) mitigation. We assess whether direct or indirect mitigation activities lead to optimal results in terms of added hydropower, environmental improvement, and monetary cost at a basin scale. We present two case studies for the Connecticut River and Roanoke River Basins, USA. Significant opportunities for expanding hydropower generating capacity are numerous in both basins. Results suggest that total hydropower capacity could be increased 4 to 7 % in the Roanoke and Connecticut Basins, respectively, without new dam construction and with net improvements in environmental conditions. We found that environmentally and economically optimal win-win strategies for increasing hydropower capacity and improving environmental conditions included improving environmental conditions in rivers downstream of existing dams. Off-site mitigation opportunities, such as dam removal and wetland mitigation, were identified as optimum solutions for achieving net environmental improvements only when they were associated with new hydropower construction. Our results demonstrate that opportunities to increase hydropower capacity and improve environmental conditions are expanded by viewing cumulative benefits at basin scales; however, increasing regulatory flexibility may be required to realize these opportunities.

54 ENVIRONMENTAL SCIENCES↗