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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stars in the local galactic thick disc and halo in Gaia EDR3: a catalogue of half a million local main-sequence stars with photometric metallicities

We present a catalogue of 551 214 main-sequence stars in the local (d < 2 kpc) Galactic thick disc and halo, based on a search of stars with large proper motions ($\mu _{\rm tot} \gt 40.0~\rm mas\, yr^{-1}$) in the Gaia Early Data Release 3. We derive photometric metallicity calibrated from the colour–luminosity–metallicity distribution of 20 047 stars with spectroscopic metallicities, collected from various spectroscopic surveys, including SDSS SEGUE/APOGEE, GALAH DR3, and LAMOST DR6. We combine these results to construct an empirical colour–magnitude–metallicity grid, which can be used to estimate photometric metallicities for low-mass metal-poor stars of K and M subtypes from their absolute G magnitude and colour values. We find that low-mass, high-velocity stars in our catalogue share similar kinematics as reported in recent studies of more luminous Galactic halo stars. The pseudo-kinematic analysis of our sample recovers the main local halo structures, including the Gaia–Enceladus Stream and the Helmi stream; aside from these, the local halo stars appear to show a remarkably smooth distribution in velocity space. Since the future Gaia data release will provide radial velocity measurements for only a small number of our sample, our catalogue provides targets of high interest for the future spectroscopic observation programs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Spatiotemporal control of structure and dynamics in a polar active fluid

We apply optimal control theory to drive a polar active fluid into new behaviors: relocating asters, reorienting waves, and on-demand switching between states. This study reveals general principles to program active matter for useful functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

STEPS: A Portable Numerical Simulation Toolkit for Electrical Power System Dynamic Studies

Numerical simulation is the key technique for large scale power system analysis. Redistribution of global renewable power via international interconnections requires new simulation tools to study the interconnected systems with different nominal frequencies as a whole. In this paper we introduce an open source simulation toolkit for electrical power systems (STEPS) which is hosted at Github. Its kernel is coded in C++ with major functions of power flow and electro-mechanical dynamic simulation. Flexible options are provided and configurable to improve power flow solution and dynamic simulation. Common devices and models are supported in STEPS for AC/DC hybrid system studies. Studies of interconnected systems with different nominal frequencies is supported in STEPS for research of international interconnection. Application program interfaces are provided and wrapped with Python to enable high-level interfaces for general applications. STEPS is thread safe and parallel computation is supported in both kernel and script levels to accelerate simulation. It is portable and works on Windows and GNU/Linux platforms. Cases from small to large scale systems are thoroughly tested to validate the toolkit with commercial packages as benchmarks.

42 ENGINEERING↗
DOE OSTI2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from a small dataset of recorded observations of the perturbed system's dynamics and the control law is optimized offline by interaction with the learned system model. The DPC method is based on two sequential steps, i) system identification using a constrained neural state-space model, and ii) optimization of an explicit control law parametrized by another neural network in closed-loop simulation with the identified neural state-space model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law's parameters directly by backpropagating economic MPC loss through the learned system model. By incorporating domain knowledge and leveraging established techniques from optimal control, our method leverages deep neural networks as nonlinear function approximators for system identification and control while avoiding concomitant costs of intractably large datasets, and computationally expensive over-parametrized models. The scalability, data efficiency, and constrained optimal control capability of the proposed DPC method are demonstrated in simulation using a multi-zone building emulator.

Drgona, Jan↗
DOE OSTI2023

Using Methyl Bromide for Interspecies Cell–Cell Signaling and As a Reporter in a Model Soil Consortium

Synthetic microbial communities are emerging as model systems for studying consortia-scale phenotypes. To establish synthetic biology tools for studying these communities in hard-to-image environmental materials, we evaluated whether a single member of a model soil consortium (MSC) can be programmed to report on gene expression without requiring matrix disruption. For these studies, we targeted a five member MSC that includes Dyadobacter fermentans, Ensifer adhaerens, Rhodococcus sp003130705, Streptomyces sp001905665, and Variovorax beijingensis. By coupling the expression of a methyl halide transferase to a constitutive promoter, we show that Variovorax beijingensis can be programmed to synthesize methyl halides that accumulate in the soil headspace at levels that are ≥24-fold higher than all other MSC members across a range of environmentally-relevant hydration conditions. We find that methyl halide production can report on a MSC promoter that is activated by changes in water potential, and we demonstrate that a synthetic gas signal can be read out directly using gas chromatography and indirectly using a soil-derived Methylorubrum that is programmed to produce a visual output in response to methyl halides. Finally, these tools will be useful for future studies that investigate how MSC respond to dynamic hydration conditions, such as drought and flood events induced by climate change, which can alter soil water potential and induce the release of stored carbon.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Traversable wormhole dynamics on a quantum processor

The holographic principle, theorized to be a property of quantum gravity, postulates that the description of a volume of space can be encoded on a lower-dimensional boundary. The anti-de Sitter (AdS)/conformal field theory correspondence or duality is the principal example of holography. The Sachdev–Ye–Kitaev (SYK) model of N >> 1 Majorana fermions has features suggesting the existence of a gravitational dual in AdS 2 , and is a new realization of holography. Here, we invoke the holographic correspondence of the SYK many-body system and gravity to probe the conjectured ER=EPR relation between entanglement and spacetime geometry through the traversable wormhole mechanism as implemented in the SYK model. A qubit can be used to probe the SYK traversable wormhole dynamics through the corresponding teleportation protocol. This can be realized as a quantum circuit, equivalent to the gravitational picture in the semiclassical limit of an infinite number of qubits. Here we use learning techniques to construct a sparsified SYK model that we experimentally realize with 164 two-qubit gates on a nine-qubit circuit and observe the corresponding traversable wormhole dynamics. Despite its approximate nature, the sparsified SYK model preserves key properties of the traversable wormhole physics: perfect size winding, coupling on either side of the wormhole that is consistent with a negative energy shockwave, a Shapiro time delay, causal time-order of signals emerging from the wormhole, and scrambling and thermalization dynamics. Our experiment was run on the Google Sycamore processor. By interrogating a two-dimensional gravity dual system, our work represents a step towards a program for studying quantum gravity in the laboratory. Future developments will require improved hardware scalability and performance as well as theoretical developments including higher-dimensional quantum gravity duals and other SYK-like models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Single electron in a storage ring: a probe into the fundamental properties of synchrotron radiation and a powerful diagnostic tool

An experimental study of undulator radiation generated by a single electron was carried out at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. Photons were detected with a single-photon avalanche diode (SPAD) at an average rate of 20 kHz, corresponding to one detection every few hundred revolutions. Events were continuously recorded by a picosecond event timer for as long as 5 minutes at a time. The collected data were used to directly test the predictions of classical Poisson photostatistics. The study was motivated in part by a previous observation of sub-Poissonian statistics in a similar experiment with synchrotron radiation. In addition, we show that the time series of recorded events can be used to study the synchrotron motion of a single electron, yielding dynamical parameters such as the phase jitter of the radiofrequency cavity and the dependence of the synchrotron period on amplitude. Research plans for further experiments are also discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Neural Lyapunov Differentiable Predictive Control

We present a learning-based predictive control methodology using the differentiable programming framework with sufficient Lyapunov-based guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by constructing a computational graph encompassing the system dynamics, state, and input constraints, and the necessary Lyapunov certification constraints, and thereafter using the automatic differentiation to update the neural policy parameters. In conjunction, our approach jointly learns a Lyapunov function that certifies the regions of state-space with stable dynamics. We also provide an initial condition sampling-based statistical guarantee for the training of NLDPC. Our offline training approach provides a computationally efficient and scalable alternative to classical model predictive control solutions, which now directly comes with Lyapunov based guarantees as shown in this paper. We substantiate the advantages of the proposed approach with simulations to stabilize the double integrator model and on an example of controlling an aircraft model.

Mukherjee, Sayak↗
DOE OSTI2021

FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking

In this work, we present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our simplicial spacetime meshing scheme that generalizes both regular and unstructured spatial meshes to spacetime while tessellating spacetime mesh elements into simplices. The benefits of using simplicial spacetime meshes include (1) reducing ambiguity cases for feature extraction and tracking, (2) simplifying the handling of degeneracies using symbolic perturbations, and (3) enabling scalable and parallel processing. The use of simplicial spacetime meshing simplifies and improves the implementation of several feature-tracking algorithms for critical points, quantum vortices, and isosurfaces. As a software framework, FTK provides end users with VTK/ParaView filters, Python bindings, a command line interface, and programming interfaces for feature-tracking applications. We demonstrate use cases as well as scalability studies through both synthetic data and scientific applications including tokamak, fluid dynamics, and superconductivity simulations. We also conduct end-to-end performance studies on the Summit supercomputer. FTK is open sourced under the MIT license: https://github.com/hguo/ftk.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Extreme-Scale Algorithms & Software Resilience (EASIR) Architecture-Aware Algorithms for Scalable Performance and Resilience on Heterogeneous Architectures

This project addresses both communication-avoiding algorithms, and reproducible floating-point computation. Communication, i.e. moving data, either between levels of memory or processors over a network, is much more expensive per operation than arithmetic (measured in time or energy), so we seek algorithms that greatly reduce communication. We developed many new algorithms for both dense and sparse, and both direct and iterative linear algebra, attaining new communication lower bounds, and getting large speedups in many cases. We also extended this work in several ways: (1) We minimize writes separately from reads, since writes may be much more expensive than reads on emerging memory technologies, like Flash, sometimes doing asymptotically fewer writes than reads. (2) We extend the lower bounds and optimal algorithms to arbitrary algorithms that may be expressed as perfectly nested loops accessing arrays, where the array subscripts may be arbitrary affine functions of the loop indices (eg A(i), B(i,j+k, k+3*m-7, …) etc.). (3) We extend our communication-avoiding approach to some machine learning algorithms, such as support vector machines. This work has won a number of awards. We also address reproducible floating-point computation. We define reproducibility to mean getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should ideally not change the answer. Many users depend on reproducibility for debugging or correctness. However, dynamic scheduling of parallel computing resources, combined with nonassociativity of floating point addition, makes attaining reproducibility a challenge even for simple operations like summing a vector of numbers, or more complicated operations like the Basic Linear Algebra Subprograms (BLAS). We describe an algorithm that computes a reproducible sum of floating point numbers, independent of the order of summation. The algorithm depends only on a subset of the IEEE Floating Point Standard 754-2008, uses just 6 words to represent a “reproducible accumulator,” and requires just one read-only pass over the data, or one reduction in parallel. New instructions based on this work are being considered for inclusion in the future IEEE 754-2018 floating-point standard, and new reproducible BLAS are being considered for the next version of the BLAS standard.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Introduction to Finite Element Analysis: Abaqus Tutorial [Slides]

Abaqus is a suite of powerful engineering simulation programs, based on the finite element method, that can solve a variety of problems, ranging from simple to complex, including the fields of: Structural mechanics, Structural dynamics, Thermodynamics, Heat transfer, and more

42 ENGINEERING↗
DOE OSTI2023

Resilient Distribution System Restoration with Equitable Load Shedding

A methodology is proposed for the improvement of electric distribution system resilience to high-impact, low-probability catastrophic events. An approach for dynamic network reconfiguration and coordination of distributed energy resources is introduced to assist in restoration efforts. The problem is formulated as a mixed-integer linear program that minimizes generation costs, the cost of lost load, and costs associated with equitable load shedding, while respecting operational limits of generation, loads, and the network. Constraints are imposed on binary switching variables to ensure equitable load shedding in emergency situations. Numerical validation of the proposed approach is conducted on an example distribution feeder, and case studies are performed to analyze the impact of various parameters in the optimization problem formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Generating and Analyzing Program Call Graphs using Ontology

Call graph or caller-callee relationships have been used for various kinds of static program analysis, performance analysis and profiling, and for program safety or security analysis such as detecting anomalies of program execution or code injection attacks. However, different tools generate call graphs in different formats, which prevents efficient reuse of call graph results. In this paper, we present an approach of using ontology and resource description framework (RDF) to create knowledge graphs for specifying call graphs to facilitate the construction of full-fledged and complex call graphs of computer programs, realizing more interoperable and scalable program analyses than conventional approaches. We create a formal ontology-based specification of call graph information to capture concepts and properties of both static and dynamic call graphs so different tools can collaboratively contribute to more comprehensive analysis results. Our experiments show that ontology enables merging of call graphs generated from different tools and flexible queries using a standard query interface. Index Terms—Callgraph, ontology, knowl

Dorta, E.↗
DOE OSTI2020

Real-time Ridesharing for Transportation Hubs with Demand and Supply Uncertainty

Transportation hubs in major cities generate a significant amount of trips by taxis and for-hail vehicles (FHV), with many of the trips sharing similar destinations. This suggests promising opportunities to leverage the collective travel needs with dedicated ridesharing solutions to reduce the externalities of excessive traffic at transportation hubs. In this study, we develop a novel dynamic ridesharing approach to serve trips from the transportation hub by considering (1) demand (new passengers) and supply (newly available vehicles) in the near future and (2) the uncertainty of future predictions. Our approach consists of two stages. In the first stage, we develop a data structure called hub mobility tree to generate potential combinations of shareable trips as candidate schedules efficiently. Then the generated schedules are used in the second stage to formulate the stochastic hub-based ridesharing problem (SHRP), which is a stochastic integer programming problem with the objective to maximize the total expected ridesharing profit over time. Due to the prohibitive number of shareable trips, we then approximately solve SHRP by the sample average approximate method (SAA), and a dual Lagrangian technique is implemented to further improve the scalability of the solution approach. We demonstrate the performances of the proposed method by simulating the ridesharing service at JFK airport using NYC taxi and FHV data. The results indicate that the proposed method outperforms the myopic ridesharing (maximize profit for a single time step) and the rolling horizon method with point estimation of future demand and supply.

dynamic ridesharing↗
DOE OSTI2023

LLNL SFA OBER FY23 Program Management and Performance Report: BioGeoChemistry at Interfaces

The focus of the BioGeoChemistry at Interfaces SFA has been to identify and quantify the biogeochemical processes and the underlying mechanisms that control actinide mobility in an effort to reliably predict and control the cycling and migration of actinides in the environment. The research approach has included: (1) Field Studies that capture actinide behavior on the timescale of decades (Research Thrust 1), and (2) Fundamental Laboratory Studies that isolate specific biogeochemical processes observed in the field (Research Thrust 2). These Research Thrusts are underpinned by the unique capabilities and staff expertise at Lawrence Livermore National Laboratory, allowing the BioGeoChemistry at Interfaces SFA to advance our understanding of actinide migration behavior in the environment, and serve as a resource for environmental radiochemistry research internationally (Figure 1). In FY23, our SFA research focused on transient redox gradients across stratified waters, sediment-water interfaces, and mineral-water interfaces to address processes controlling cycling of redox-sensitive metals. Nevertheless, Research Thrusts 1 and 2 are guided by the following broad central hypotheses that were developed during our last program review held at the end of FY18: Thrust 1 Hypothesis: Biogeochemical processes occurring on the timescale of years to decades lead to greater actinide recalcitrance in sediments and limits their migration in surface and groundwater. Thrust 2 Hypothesis: Long-term biogeochemical processes include mineral and surface alteration, which leads to stabilization of actinide surface associations or incorporation into mineral precipitates. Our strategic goal is to use the knowledge gained from our Science Plan to advance our understanding of the behavior of actinides and other radionuclides (e.g. Cs) and provide DOE with the scientific basis for remediation and long-term stewardship of DOE’s legacy sites. More broadly, we are improving our understanding of transport phenomena in environmental systems sciences with a particular emphasis on environmentally relevant (long-term) timescales. While we retained some of our historical focus on actinide biogeochemistry this past fiscal year, biogeochemical processes occurring at unique Test Bed locations associated with this SFA provide fundamental information on abiotic and biotic redox processes that control the cycling of redox sensitive metals under dynamic and transient conditions. Furthermore, in collaboration with SFA teams at Argonne National Laboratory, our SFA has begun to transition away from the current research focus and develop a new research program in terrestrial wetland systems. The Terrestrial Wetland Function and Resilience SFA program plan will be delivered to the Environmental System Science (ESS) program within BER at the end of FY23.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Plasma Surface Interactions: Bridging from the Surface to the Micron Frontier through Leadership Class Computing (Final Technical Progress Report)

The performance demands on plasma-facing components (PFCs) in future fusion power plants are beyond the capability of current materials. In defining the plasma-surface interaction (PSI), it is evident that three coupled spatial regions influence PFC materials evolution and performance. These regions consist of (1) the edge and scrape-off layer region of the plasma, (2) the near-surface material response to extreme thermal and particle fluxes under the influence of, and feedback to, the plasma sheath, and (3) the structural material’s response to an intense, 14 MeV peaked neutron spectrum, which produces very high concentrations of transmuted elements through (n,p) and (n,α) reactions and structural material property degradation. The objective of this project is to develop and deploy validated, high-performance simulation tools capable of predicting the performance of tungsten-based plasma-facing components (PFCs) in a burning fusion plasma environment, which includes modeling surface morphology evolution in either erosion or re-deposition regimes, and the recycling of hydrogen species. This requires the development of a leadership-scale computational code to predict PFC behavior that is well integrated to a suite of multiscale modeling techniques to bridge the scales needed to address complex physical and computational issues at the plasma surface interface and the transition region below the surface where neutron damage processes in the bulk dominate material behavior. Successful completion of this project will provide PFC simulation tools to evaluate steady-state performance of tungsten-based PFC and divertor components in burning plasma environments. This will enable the identification of critical experiments to confirm whether practical PFC solutions exist for magnetic fusion energy beyond ITER. More specifically, the research activities within this project focus on two broadly defined research thrusts: 1) Developing a new simulation code, Xolotl-PSI to predict PFC operating lifetime and performance, that is specifically designed to take advantage of leadership class computing facilities; and 2) Integrating and applying discrete particle-based, as well as continuum-based, multiscale modeling techniques to provide scientific discovery of the mechanisms controlling PFC and bulk materials evolution under fusion plasma and 14-MeV neutron exposure; in this thrust, the challenge of scale-bridging between atomistic/microstructural modeling and the continuum-scale of PFC device performance is achieved through multiscale modeling techniques. Throughout the grant period, research at UMass Amherst focused on the atomic-scale analysis of the dynamics and kinetics (transport and reactions) of small mobile helium clusters near surfaces and grain boundaries in plasma-exposed tungsten, as well as the proper parameterization and incorporation of these processes into our SciDAC team’s continuum-level cluster dynamics code (Xolotl). Our work also included characterization of large-scale molecular-dynamics (MD) simulation results of helium implantation into tungsten based on the findings of our analysis and comparisons of Xolotl simulation predictions with the large-scale MD simulation results for Xolotl validation purposes; emphasis was placed on the impact of helium cluster dynamics/kinetics on tungsten surface morphology, near-surface tungsten structure, and helium retention. Furthermore, our work included initiation of a research program on evaluating the impact of structural defects in tungsten due to plasma exposure (such as helium nanobubbles) on tungsten’s thermophysical properties, focusing on lattice thermal conductivity. Our studies contributed to a fundamental understanding of the effects of plasma-surface interactions on the dynamical response of plasma-facing materials in nuclear fusion devices and the development of predictive hierarchical multiscale computational tools for the quantitative description of the material’s dynamical response to plasma exposure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ni5Ga3 Catalysts for CO2 Reduction to Methanol: Exploring the Role of Ga Surface Oxidation/reductions on Catalytic Activity

Ad-Ni5Ga3/SiO2 catalyst, which is highly active and stable for thermal CO2 hydrogenation to methanol, was investigated to understand its surface dynamics during reaction conditions. The catalyst was prepared, tested and characterized using a multitude of techniques, including ex-situ XRD, TEM, H2-TPR, CO chemisorption and in-situ ETEM and APXPS. The formation of Ga2O3 upon air exposure was observed and the consequences on catalytic activity were investigated. Results revealed that upon air exposure Ga migrates from the subsurface region to the surface of the nanoparticles forming a Ga-oxide shell surrounding a metallic core. The oxide shell can be reduced completely at high temperatures and the temperature of the reduction activation treatment plays a crucial role on the catalytic activity. This air exposure-reduction procedure results in a redistribution of the Ga species in the surface/subsurface region, promoted by a higher Ga affinity for oxygen. XRD analysis of the post-reaction samples provide evidence for the partial transformation of a Ni5Ga3 intermetallic phase into a Ni-rich Ni3Ga phase. APXPS data indicate that the Ni-rich phase forms in the subsurface region. A Ga-rich phase exists on the surface, where the Ni/Ga ratio reaches 2/3 upon activation in hydrogen. At low reduction temperatures an amorphous Ga2O3 shell remains, indicating a promotion effect on methanol synthesis activity.

Ni-Ga, intermetallic, methanol synthesis, in-situ ↗
DOE OSTI2017

Impact of vegetation dynamics on hydrological processes in a semi-arid basin by using a land surface-hydrology coupled model

Land surface models (LSMs) are widely used to understand the interactions between hydrological processes and vegetation dynamics, which is important for the attribution and prediction of regional hydrological variations. However, most LSMs have large uncertainties in their representations of ecohydrological processes due to deficiencies in hydrological parameterizations. In this study, the Community Land Model version 4 (CLM4) LSM was modified with an advanced runoff generation and flow routing scheme, resulting in a new land surface-hydrology coupled model, CLM-GBHM. Both models were implemented in the Wudinghe River Basin (WRB), which is a semi-arid basin located in the middle reaches of the Yellow River, China. Compared with CLM, CLM-GBHM increased the Nash Sutcliffe efficiency for daily river discharge simulation (1965–1969) from 0.03 to 0.23 and reduced the relative bias in water table depth simulations (2010–2012) from 32.4% to 13.4%. The CLM-GBHM simulations with static, remotely sensed and model-predicted vegetation conditions showed that the vegetation in the WRB began to recover in the 2000s due to the Grain for Green Program but had not reached the same level of vegetation cover as regions in natural eco-hydrological equilibrium. Compared with a simulation using remotely sensed vegetation cover, the simulation with a dynamic vegetation model that considers only climate-induced change showed a 10.3% increase in evapotranspiration, a 47.8% decrease in runoff, and a 62.7% and 71.3% deceleration in changing trend of the outlet river discharge before and after the year 2000, respectively. We conclude this result suggests that both natural and anthropogenic factors should be incorporated in dynamic vegetation models to better simulate the eco-hydrological cycle.

54 ENVIRONMENTAL SCIENCES↗