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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

An integral-factorized implementation of the driven similarity renormalization group second-order multireference perturbation theory

We report an efficient implementation of a second-order multireference perturbation theory based on the driven similarity renormalization group (DSRG-MRPT2) [C. Li and F. A. Evangelista, J. Chem. Theory Comput. 11, 2097 (2015)]. Our implementation employs factorized two-electron integrals to avoid storage of large four-index intermediates. It also exploits the block structure of the reference density matrices to reduce the computational cost to that of second-order Møller–Plesset perturbation theory. Our new DSRG-MRPT2 implementation is benchmarked on ten naphthyne isomers using basis sets up to quintuple-ζ quality. We find that the singlet-triplet splittings (Δ{sub ST}) of the naphthyne isomers strongly depend on the equilibrium structures. For a consistent set of geometries, the Δ{sub ST} values predicted by the DSRG-MRPT2 are in good agreements with those computed by the reduced multireference coupled cluster theory with singles, doubles, and perturbative triples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Parafermions, induced edge states, and domain walls in fractional quantum Hall effect spin transitions

Search for parafermions and Fibonacci anyons, which are excitations obeying non-Abelian statistics, is driven both by the quest for deeper understanding of nature and prospects for universal topological quantum computation. However, physical systems that can host these exotic excitations are rare and hard to realize in experiments. Here we study the domain walls and the edge states formed in spin transitions in the fractional quantum Hall effect. Effective theory approach and exact diagonalization in a disk and torus geometries proves the existence of the counterpropagating edge modes with opposite spin polarizations at the boundary between the two neighboring regions of the two-dimensional electron liquid in spin-polarized and spin-unpolarized phases. By analytical and numerical analysis, we argue that these systems can host parafermions when coupled to an 𝑠-wave superconductor and are experimentally feasible. We investigate settings based on 𝜈 = 2/3, 4/3, and 5/3 spin transitions and analyze spin-flipping interactions that hybridize counterpropagating modes. Lastly, we discuss spin-orbit interactions of composite fermions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Automated pipeline processing X-ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRA III

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Progress in fast, accurate multi-scale climate simulations

We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Analysis and modeling of Nannochloropsis growth in lab, greenhouse, and raceway experiments

Efficient production of algal biofuels could reduce dependence on foreign oil by providing a domestic renewable energy source. Moreover, algae-based biofuels are attractive for their large oil yield potential despite decreased land use and natural resource (e.g., water and nutrients) requirements compared to terrestrial energy crops. Important factors controlling algal lipid productivity include temperature, nutrient availability, salinity, pH, and the light-to-biomass conversion rate. Here, computational approaches allow for inexpensive predictions of algae growth kinetics for various bioreactor sizes and geometries without the need for multiple, expensive measurement systems. Parametric studies of algal species include serial experiments that use off-line monitoring of growth and lipid levels. Such approaches are time consuming and usually incomplete, and studies on the effect of the interaction between various parameters on algal growth are currently lacking. However, these are the necessary precursors for computational models, which currently lack the data necessary to accurately simulate and predict algae growth. In this work, we conduct a lab-scale parametric study of the marine alga Nannochloropsis salina and apply the findings to our physics-based computational algae growth model. We then compare results from the model with experiments conducted in a greenhouse tank and an outdoor, open-channel raceway pond. Results show that the computational model effectively predicts algae growth in systems across varying scale and identifies the causes for reductions in algal productivities. Applying the model facilitates optimization of pond designs and improvements in strain selection.

09 BIOMASS FUELS↗
DOE OSTI2025

Surrogate models for linear response

Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field vacuum; however, its high computational cost limits model calibration and uncertainty quantification studies. Here, we present two complementary QRPA surrogate models and apply them to study response functions of finite nuclei. One is a reduced-order model that exploits the underlying QRPA structure, while the other utilizes the recently developed parametric matrix model algorithm to construct a map between the system’s Hamiltonian and observables. Our benchmark applications, the calculation of the electric dipole polarizability of 180 Yb and the 𝛽-decay half-life of 80 Ni, show that both emulators can achieve 0.1%–1% accuracy while offering a 6–7 orders of magnitude speedup compared to state-of-the-art QRPA solvers. These results demonstrate that the developed QRPA emulators are well positioned to enable Bayesian calibration and large-scale studies of computationally expensive physics models describing the properties of many-body systems.

Beta decay↗
DOE OSTI2023

Computational projects with the Landau–Zener problem in the quantum mechanics classroom

The Landau–Zener problem, where a minimum energy separation is passed with constant rate in a two-state quantum-mechanical system, is an excellent model quantum system for a computational project. It requires a low-level computational effort, but has a number of complex numerical and algorithmic issues that can be resolved through dedicated work. It can be used to teach computational concepts, such as accuracy, discretization, and extrapolation, and it reinforces quantum concepts of time-evolution via a time-ordered product and of extrapolation to infinite time via time-dependent perturbation theory. Additionally, we discuss the concept of compression algorithms, which are employed in many advanced quantum computing strategies, and easy to illustrate with the Landau–Zener problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Parallel Simulation of Beam Dynamics in Particle Accelerators [Slides]

Particle accelerators are among the most versatile and important tools of scientific discovery. The Nation's accelerators are responsible for a wealth of advances in materials science, chemistry, the biosciences, particle physics, and nuclear physics. They also have important applications to national security, the environment, energy, medicine, and on the quality of people's lives. LANL has a long history of making pioneering contributions to Accelerator Science including key contributions to the field of Computational Accelerator Physics. These include the development of early beam dynamics codes with space charge (such as PARMILA and PARMELA), the development of rf cavity codes and magnet codes (including Poisson and Superfish), and the development and distribution of codes to the accelerator community through the Los Alamos Accelerator Code Group. LANL researchers also helped pioneer the development of massively parallel space-charge codes. In project t22_accelsim we have moved beyond electrostatic models of collective effects (i.e., solving the Poisson equation in the bunch frame) to fully electromagnetic models based on the Lienard-Wiechert formalism. This approach enables the large-scale simulation of radiation production and collective effects in high brightness electron beams. This is highly relevant to LANL given its future goal of developing an X-ray Free Electron Laser (XFEL). It also directly impacts a LANL LDRD project to develop an undulator-based non-invasive beam profile monitor for beams created in laser-plasma accelerator systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Interrelationship between Lab, Space, Astrophysical, Magnetic Fusion, and Inertial Fusion Plasma Experiments

The objectives of this review are to articulate geospace, heliospheric, and astrophysical plasma physics issues that are addressable by laboratory experiments, to convey the wide range of laboratory experiments involved in this interdisciplinary alliance, and to illustrate how lab experiments on the centimeter or meter scale can develop, through the intermediary of a computer simulation, physically credible scaling of physical processes taking place in a distant part of the universe over enormous length scales. The space physics motivation of laboratory investigations and the scaling of laboratory plasma parameters to space plasma conditions, having expanded to magnetic fusion and inertial fusion experiments, are discussed. Examples demonstrating how laboratory experiments develop physical insight, validate or invalidate theoretical models, discover unexpected behavior, and establish observational signatures for the space community are presented. The various device configurations found in space-related laboratory investigations are outlined.

Koepke, Mark E.↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
DOE OSTI2015

High performance techniques for micro channel plate computations in photo multipliers

Monte Carlo - MC methods, requiring heavy computations of large data volume, can be used to predict the amplitude distribution and variance of outputted electron charge in Micro Channel Plates - MCPs, which are fed with primary emitted electrons in their inputs. The concept of parallel and serial combination of separate stages of such stochastic processes, which can be applied in MCP systems and simulated using MC computations, has recently proposed and shown interesting theorems and results. Separating the electron multiplication procedures, occurring in the channels of MCP systems, into sub-procedures, which are similar in nature and occur either in parallel or in consecutive manners, is equivalent to splitting the relevant stochastic system calculations into smaller ones, which can be performed concurrently and/or sequentially. In this way, calculations similar in nature, can be executed only once, thus, eliminating a great percentage of heavy computations. However, when designing and implementing the calculation inferences of the derived theorems using High Performance Computation - HPC techniques, the overall computation cost can further and significantly be reduced. In this paper, the hardware mappings of the relevant theorem inferences onto parallel computing architectures, resulting in circuits of computing blocks for calculation purposes of MCP system stochastic processes, are investigated. Each of the designed and implemented computing circuits corresponds to a theorem inference or to a combination of inferences. The parallelism of the proposed circuits consists of both the concurrent functioning of all the system blocks and of the simultaneous operation of each block, which is processing pieces of data, for many parts of the whole circuit in which it belongs. All the circuits are designed and tested using Altera Quartus II environment and can be implemented as chips, which can be incorporated in computing devices to assist Monte Carlo computations for the estimation of different MCP electron multiplication system performances.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Partnership for Edge Physics (EPSI), University of Texas Final Report

Simulations of tokamak plasmas require a number of inputs whose values are uncertain. The effects of these input uncertainties on the reliability of model predictions is of great importance when validating predictions by comparison to experimental observations, and when using the predictions for design and operation of devices. However, high fidelity simulation of tokamak plasmas, particular those aimed at characterization of the edge plasma physics, are computationally expensive, so lower cost surrogates are required to enable practical uncertainty estimates. Two surrogate modeling techniques have been explored in the context of tokamak plasma simulations using the XGC family of plasma simulation codes. The first is a response surface surrogate, and the second is an augmented surrogate relying on scenario extrapolation. In addition, to reduce the costs of the XGC simulations, a particle resampling algorithm was developed, which allows marker particle distributions to be adjusted to maintain optimal importance sampling. This means that the total number of particles in and therefore the cost of a simulation can be reduced while maintaining the same accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute

At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. The International Neuroinformatics Coordinating Facility (INCF) was inaugurated in 2007 as an initiative to improve this situation. The INCF has since pursued its mission to help the development of standards and best practices. In a community paper published this very same year, Brette et al. tried to assess the state of the field and to establish a scientific approach to simulation technology, addressing foundational topics, such as which simulation schemes are best suited for the types of models we see in neuroscience. In 2015, a Frontiers Research Topic “Python in neuroscience” by Muller et al. triggered and documented a revolution in the neuroscience community, namely in the usage of the scripting language Python as a common language for interfacing with simulation codes and connecting between applications. The review by Einevoll et al. documented that simulation tools have since further matured and become reliable research instruments used by many scientific groups for their respective questions. Open source and community standard simulators today allow research groups to focus on their scientific questions and leave the details of the computational work to the community of simulator developers. A parallel development has occurred, which has been barely visible in neuroscientific circles beyond the community of simulator developers: Supercomputers used for large and complex scientific calculations have increased their performance from ~10 TeraFLOPS (10 13 floating point operations per second) in the early 2000s to above 1 ExaFLOPS (10 18 floating point operations per second) in the year 2022. This represents a 100,000-fold increase in our computational capabilities, or almost 17 doublings of computational capability in 22 years. Moore's law (the observation that it is economically viable to double the number of transistors in an integrated circuit every other 18–24 months) explains a part of this; our ability and willingness to build and operate physically larger computers, explains another part. It should be clear, however, that such a technological advancement requires software adaptations and under the hood, simulators had to reinvent themselves and change substantially to embrace this technological opportunity. It actually is quite remarkable that—apart from the change in semantics for the parallelization—this has mostly happened without the users knowing. The current Research Topic was motivated by the wish to assemble an update on the state of neuroscientific software (mostly simulators) in 2022, to assess whether we can see more clearly which scientific questions can (or cannot) be asked due to our increased capability of simulation, and also to anticipate whether and for how long we can expect this increase of computational capabilities to continue.

biophysically detailed models↗
DOE OSTI2025

Computational Fluid Dynamics Modeling to Facilitate Qualification of Stack Sampling Probe Location

Computational fluid dynamics (CFD) modeling was used to help evaluate modifications to a radiological effluent stack and assist with establishing a stack sampling location that met the mixing criteria for qualification. Requirements for stack sampling location are listed in the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-2021 standard. Modeling was performed to help develop a suitable design for increasing building ventilation for the radiological effluent stack. The ANSI/HPS N13.1-2021 criteria for the air monitoring probe location are that the coefficient of variation of velocity uniformity, gaseous tracer uniformity, and particulate tracer uniformity must be less than or equal to 20%. Furthermore, no point in the sampling location may have a gaseous tracer concentration that varies from the mean concentration by more than 30%. Additionally, the flow angle at the sampling location must not be more than 20 degrees. The ANSI/HPS N13.1-2021 standard allows for models (physical or computational) to be employed to perform the full suite of qualification tests, followed by a more limited set of verification tests on the actual stack to qualify the stack sampling location. Here, a series of computational model simulations were employed to evaluate the stack qualification criteria. Significant time and re-source savings are achieved using CFD modeling. CFD modeling demonstrated that the stack meets the criteria at the sample probe location. Verification tests were performed on the modified stack to measure the velocity uniformity and flow angle at the stack sampling location, and results demonstrated that the CFD model results may be used to support the qualification of the stack sampling location.

Air Monitoring↗
DOE OSTI2025

CFD Simulation of Aerobic Gas Fermentation to Enable Commercial Conversion of CO 2 into Aquaculture and Animal Feed: Cooperative Research and Development (Final Report)

NovoNutrients’ fermentation technology uses energy from hydrogen to transform industrial CO2 emissions into premium animal feed ingredients and other valuable products. A single NovoNutrients’ commercial manufacturing plant will capture and convert over 200,000 tons/yr of CO2 into over 100,000 tons/yr of high-protein feed. Key to the rapid and widespread deployment of the technology is maximization of its productivity and energy efficiency. Robust, physically based computational models of the technology will significantly increase productivity and efficiency, accelerating NovoNutrients’ technology to manufacturing scale. NREL has unique capabilities for creating and running such computational models. NREL's existing aerobic bioreaction computational fluid dynamics (CFD) models will be adapted to NovoNutrients’ gas fermentation (CO2, H2, O2) technology. The multiphysics CFD simulations require thousands of high-performance computing (HPC) node hours to simulate the complex geometries and contents of NovoNutrients’ industrial bioreactors. The experimentally validated CFD models were used to identify optimally efficient and productive bioreactor designs and operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Development of Labview based data acquisition and multichannel analyzer software for radioactive particle tracking system

A DAQ (data acquisition) software called RPTv2.0 has been developed for Radioactive Particle Tracking System in Malaysian Nuclear Agency. RPTv2.0 that features scanning control GUI, data acquisition from 12-channel counter via RS-232 interface, and multichannel analyzer (MCA). This software is fully developed on National Instruments Labview 8.6 platform. Ludlum Model 4612 Counter is used to count the signals from the scintillation detectors while a host computer is used to send control parameters, acquire and display data, and compute results. Each detector channel consists of independent high voltage control, threshold or sensitivity value and window settings. The counter is configured with a host board and twelve slave boards. The host board collects the counts from each slave board and communicates with the computer via RS-232 data interface.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗