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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evidence for the 3D Radiative Effects of Boundary-Layer Clouds From Observations of Direct and Diffuse Surface Solar Fluxes

Numerical experiments have revealed the importance of horizontal transport of light in the presence of clouds (“3D effects”), with consequences for climate, weather, and solar resource availability predictions. Yet, analysis of 3D effects from observations remain sparse because of the difficulty to isolate the effect of horizontal transport in radiation measurements. In this study, we provide observational evidence for 3D effects based on the direct-diffuse partition of surface solar fluxes. It is compared to outputs from the ecRad radiative transfer scheme run on retrieved cloud profiles. Furthermore, the direct-beam calculation takes careful account of the field-of-view of the pyrheliometer to ensure consistency between observed and modeled direct fluxes. Only the solver that accounts for 3D effects is able to reproduce the observed mean direct-diffuse partition as a function of solar zenith angle and cloud cover, in particular at large solar zenith angles where cloud sides intercept most of the direct beam.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Reassessing the Market—Computation Interface to Enhance Grid Security and Efficiency

The goal of this project is to reconsider core market and reliability processes that can potentially yield to transformative advances in power grid security, reliability, and efficiency. Current electric power market designs are strongly a function of computing capabilities and limitations that were available in the mid-to-late 1990s, circa deregulation. This includes constructs such as: (1) a 2-tiered day-ahead/real-time market construct; and (2) linearized (“DC”) real power flow approximations in dispatch and pricing. At that time, state-of-the-art computational capabilities could at the limit address deterministic mixed-integer programming formulations of unit commitment (UC) and linear programming formulations of economic dispatch (ED) at limited fidelity and scale. Such constraints forced limited look-ahead time-horizons, crude approximations of AC power flow physics and operations, and artificial partitioning between day-ahead markets, hour(s)-ahead reliability processes, and real-time markets. Consequently, these limitations have resulted in limited security and reliability with increasing out-of-market payments, particularly as uncertainty associated with renewables and distributed energy resources grows.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Overexpression of plasma membrane SUT1 in poplar alters lateral sucrose partitioning in stem and promotes leaf necrosis

Abstract In Populus and many other tree species, photoassimilate sucrose diffuses down a concentration gradient via symplastically connected mesophyll cells to minor vein phloem for long‐distance transport. There is no evidence for apoplastic phloem‐loading in Populus . However, plasma membrane sucrose transporters (SUT1 and SUT3) orthologous to those associated with apoplastic phloem loading are expressed in vascular tissues of poplar. While SUT3 functions in sucrose import into developing xylem, the role of SUT1 remains unclear. Here, we overexpressed PtaSUT1 in Populus tremula x P. alba to examine the effects on sucrose partitioning in transgenic plants. Overall leaf sucrose levels were similar between wild type and transgenic lines. Stem sucrose levels were not changed in bark but were significantly reduced in the adjacent xylem, suggesting hindered intercellular sucrose trafficking from the phloem to the developing xylem. Fully expanded leaves of transgenic plants deteriorated prematurely with declining photosynthesis prior to severe necrotic spotting. Necrotic spotting advanced most rapidly in the distal portion of mature leaves and was accompanied by sharp hexose increases and sharp sucrose decreases there. Leaf transcriptome profiling and network inference revealed the down‐regulation of copper proteins and elevated expression of copper microRNAs prior to noticeable leaf injury. Our results suggest ectopic expression of PtaSUT1 altered sucrose partitioning in stems with systemic effects on leaf health and copper homeostasis mediated in part by sucrose‐sensitive copper miRNAs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Novel Zwitterionic Chromatography Approach to Separate Lithium from Unconventional Resources

Lithium (Li) is a key element for clean energy technologies, and, accordingly, the global lithium demand has been increasing rapidly. Therefore, to meet the Li demand and maintain supply chain stability, it is critical to develop efficient lithium extraction technologies that allow exploitation of unconventional lithium resources, such as geothermal brines and inland brine streams. However, the recovery of Li from these resources is challenging due to low Li concentration, low ratios of Li/Na, Li/Mg, or Li/Ca, and complex feed compositions. To address this, we introduced a new Direct Lithium Extraction (DLE) process using Zwitterionic Chromatography (ZIC) to separate Li from other salts. Since salts are partitioned on ZIC under water elution, no reagent chemicals are needed, and the Li separation is not limited by the adsorption capacity. We prepared 13 different zwitterionic (ZI) resins to investigate the salt retention on various ZI groups and then screened out promising sorbents for efficient Li separation. It was found that salt retention was synergistically affected by the pore size and ZI configurations. Using carboxybetaine (QAC3CA) sorbents, multicomponent separations showed that Li can be partitioned from divalent salts or Na with selectivities of 1.8 or 1.9, respectively. Although the selectivity is relatively low, in real brine tests, Li was separated from Ca and Mg with 79.2 % yield, showing the potential for a continuous process to achieve high productivity and high yield. Simulation studies suggest the salt elution mechanism is related to the hydration reaction energy and the effective hydrated radius of cations.

critical minerals↗
DOE OSTI2023

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

Here this paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Quantum Tensor-Product Decomposition from Choi-State Tomography

The Schmidt decomposition is the go-to tool for measuring bipartite entanglement of pure quantum states. Similarly, it is possible to study the entangling features of a quantum operation using its operator-Schmidt or tensor-product decomposition. While quantum technological implementations of the former are thoroughly studied, entangling properties on the operator level are harder to extract in the quantum computational framework because of the exponential nature of sample complexity. Here, we present an algorithm for unbalanced partitions into a small subsystem and a large one (the environment) to compute the tensor-product decomposition of a unitary the effect of which on the small subsystem is captured in classical memory, while the effect on the environment is accessible as a quantum resource. This quantum algorithm may be used to make predictions about operator nonlocality and effective open quantum dynamics on a subsystem, as well as for finding low-rank approximations and low-depth compilations of quantum circuit unitaries. We demonstrate the method and its applications on a time-evolution unitary of an isotropic Heisenberg model in two dimensions. Published by the American Physical Society 2024

Mansuroglu, Refik (ORCID:000000017352513X)↗
DOE OSTI2021

Adaptive bandwidth management for entanglement distribution in quantum networks

Flexible grid wavelength division multiplexing is a powerful tool in lightwave communications to maximize spectral efficiency. In the emerging field of quantum networking, the need for effective resource provisioning is particularly acute, given the generally lower power levels, higher sensitivity to loss, and inapplicability of optical detection and retransmission. In this letter, we leverage flex grid technology to demonstrate reconfigurable distribution of quantum entanglement in a four-user tabletop network. By adaptively partitioning bandwidth with a single wavelength-selective switch, we successfully equalize two-party coincidence rates that initially differ by over two orders of magnitude. Our scalable approach introduces loss that is fixed with the number of users, offering a practical path for the establishment and management of quality-of-service guarantees in large quantum networks.

42 ENGINEERING↗
DOE OSTI2025

Affinity of LDR Organics to Cementitious Materials: Sorption and Leaching Tests

SRNL is working to identify and test the physiochemical interactions of organic contaminants of potential concern with minerals in grout/cementitious materials. Organic species may interact with cementitious minerals including slag, fly ash, cement, and other components/dopants like carbon in fly ash. The identification of such interactions will support the solidification process design, performance assessments for the chemicals of concern, and a proposed Resource Conservation and Recovery Act (RCRA) treatment variance specified for the Land Disposal Restriction (LDR) organics associated with Hanford tank waste. Inably expected to be present LDR organics associated with Hanford tank waste (RPP-RPT-63493, Rev 1a) was screened by functional groups, octanol-water partitioning coefficients, and detection frequency in Hanford tank waste samples. A subset of 10 compounds, spanning the identified properties, were tested using sorption and leachate tests. These compounds were subjected to traditional batch sorption tests and leaching tests on/from Cast Stone cementitious material with and without activated carbon (a potential organic adsorption additive). The interactions between organic compounds were observed in traditional batch sorption and modified Toxic Characteristic Leaching Procedure (TCLP) leachate tests for a surrogate Cast Stone material with and without an activated carbon addition. Specifically, phthalic acids (negatively charged) and 4-chloroaniline (polar) sorbed strongly to unmodified Cast Stone and a group of 3 phenolic compounds (2,4,6-Trichlorophenol, Pentachlorophenol, o-Cresol) were sorbed to and were retained by Cast Stone with a 1% activated carbon amendment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. Additionally, the diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A seamless approach for evaluating climate models across spatial scales

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantum simulations of SO(5) many-fermion systems using qudits

The structure and dynamics of many-body systems are the result of a delicate interplay between underlying interactions. Fermionic pairing, for example, plays a central role in various physical systems, ranging from condensed matter to nuclear systems, where it can lead to collective phenomena such as superconductivity and superfluidity. In atomic nuclei, the interplay between pairing and particle-hole interactions leads to a high degree of complexity and intricate entanglement structures. Despite this apparent complexity, symmetries emerge and manifest themselves in observable regular patterns. These symmetries and their breakings have long been used to determine relevant degrees of freedom and simplify classical descriptions of many-body systems. Here, this work explores the potential utility of quantum computers with arrays of qudits in simulating interacting fermionic systems, when the qudits can naturally map the relevant degrees of freedom determined by an underlying symmetry group. The Agassi model of fermions interacting via particle-hole and pairing interactions is based on an underlying so(5) algebra. Such systems can intuitively be partitioned into pairs of modes with five basis states, which thus naturally map to arrays of d = 5 qudits (qu5its). Classical noiseless simulations of the time evolution of systems with up to twelve qu5its are performed, by implementing quantum circuits that are developed herein, using PYTHON codes invoking Google's CIRQ software. The resource requirements of the qu5it circuits are analyzed and compared with two different mappings to qubit systems: a physics-aware Jordan-Wigner mapping requiring four qubits per mode pair and a state-to-state mapping requiring three qubits per mode pair. While the dimensionality of Hilbert spaces in mappings to qu5it systems are less than those for the corresponding qubit systems, the number of entangling operations, depending on the available hardware, can either be greater or smaller than for the physics-aware Jordan-Wigner mapping. The state-to-state mapping, while having a smaller Hilbert space than Jordan-Wigner mappings, appears to be the least efficient in gate counts. Further, a previously unknown sign problem has been identified from Trotterization errors in time evolving high-energy excitations. There appear to be advantages in employing quantum computers with arrays of qudits to perform simulations of many-body dynamics that exploit the role of underlying symmetries, specifically in lowering the required quantum resources and in reducing anticipated errors that take the simulation out of the physical space. If the necessary entangling gates are not directly supported by the hardware, physics-aware mappings to qubits may, however, be advantageous for other aspects.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

IRIS: A Performance-Portable Framework for Cross-Platform Heterogeneous Computing

From edge to exascale, computer architectures are becoming more heterogeneous and complex. The systems typically have fat nodes, with multicore CPUs and multiple hardware accelerators such as GPUs, FPGAs, and DSPs. This complexity is causing a crisis in programming systems and performance portability. Several programming systems are working to address these challenges, but the increasing architectural diversity is forcing software stacks and applications to be specialized for each architecture. As we show, all of these approaches critically depend on their software framework for discovery, execution, scheduling, and data orchestration. To address this challenge, we believe that a more agile and proactive software framework is essential to increase performance portability and improve user productivity. To this end, we have designed and implemented IRIS: a performance-portable framework for cross-platform heterogeneous computing. IRIS can discover available resources, manage multiple diverse programming platforms (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, OpenMP) simultaneously in the same execution, respect data dependencies, orchestrate data movement proactively, and provide for user-configurable scheduling. To simplify data movement, IRIS introduces a shared virtual device memory with relaxed consistency among different heterogeneous devices. IRIS also adds an automatic kernel workload partitioning technique using the polyhedral model so that it can resize kernels for a wide range of devices. Our evaluation on three architectures, ranging from Qualcomm Snapdragon to a Summit supercomputer node, shows that IRIS improves portability across a wide range of diverse heterogeneous architectures with negligible overhead.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing

A coloring of a graph is an assignment of colors to vertices such that no two neighboring vertices have the same color. The need for memory-efficient coloring algorithms is motivated by their application in computing clique partitions of graphs arising in quantum computations where the objective is to map a large set of Pauli strings into a compact set of unitaries. We present Picasso, a randomized memory-efficient iterative parallel graph coloring algorithm with theoretical sublinear space guarantees under practical assumptions. The parameters of our algorithm provide a trade-off between coloring quality and resource consumption. To assist the user, we also propose a machine learning model to predict the coloring algorithm’s parameters considering these trade-offs. We provide a sequential and a parallel implementation of the proposed algorithm. We perform an experimental evaluation on a 64-core AMD CPU equipped with 512 GB of memory and an Nvidia A100 GPU with 40GB of memory. For a small dataset where existing coloring algorithms can be executed within the 512 GB memory budget, we show up to 68× memory savings. On massive datasets we demonstrate that GPU-accelerated Picasso can process inputs with 49.5× more Pauli strings (vertex set in our graph) and 2,478× more edges than state-of-the-art parallel approaches.

artificial intelligence, quantum computing↗
DOE OSTI2024

Affinity of LDR Organics to Cementitious Materials: Sorption and Leaching Tests

SRNL is working to identify and test the physiochemical interactions of organic contaminants of potential concern with minerals in grout/cementitious materials. Organic species may interact with cementitious minerals including slag, fly ash, cement, and other components/dopants like carbon in fly ash. The identification of such interactions will support the solidification process design, performance assessments for the chemicals of concern, and a proposed Resource Conservation and Recovery Act (RCRA) treatment variance the technology-based treatment standard specified for the Land Disposal Restriction (LDR) organics associated with Hanford tank waste. In FY24, a list of the 132 reasonably expected to be present LDR organics associated with Hanford tank waste (RPP-RPT-63493, Rev 1a) was screened by functional groups, octanol-water partitioning coefficients, and detection frequency in Hanford tank waste samples. A subset of 10 compounds spanning the identified properties were tested using sorption and leachate tests. These compounds were subject to traditional batch sorption tests and leaching tests on/from Cast Stone cementitious material with and without activated carbon (a potential organic adsorption additive). The tests have been completed and analysis is underway but as yet currently unavailable. The analytical results (and conclusions) will be included in a forthcoming revision to this report as soon as they are available

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Distributed Tomographic Reconstruction with Quantization

Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by partitioning data across multiple nodes, reducing server load and enhancing privacy. However, these algorithms often encounter challenges related to memory constraints and communication overhead between nodes. In this paper, we introduce a decentralized Alternating Directions Method of Multipliers (ADMM) with configurable quantization. By distributing local objectives across nodes, our approach is highly scalable and can efficiently reconstruct images while adapting to available resources. To overcome communication bottlenecks, we propose two quantization techniques based on K-means clustering and JPEG compression. Numerical experiments with benchmark images illustrate the tradeoffs between communication efficiency, memory use, and reconstruction accuracy.

Miao, Runxuan↗
DOE OSTI2022

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-driven activation and bio-inspired learning algorithms. SNN-based machine learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called a crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of the Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine learning programs. Our results demonstrate that DFSynthesizer provides a much tighter performance guarantee compared to current mapping approaches.

Computer Science↗
DOE OSTI2020

Ion separations with membranes

Ion separations are important for resource recovery, water treatment, and energy production and storage. Techniques such as chemical precipitation, selective adsorption, and solvent extraction are effective, but membranes may separate ions continuously with less waste and lower energy costs. Separation of monovalent and multivalent ions with nanofiltration or electrodialysis membranes already enables water softening and edible salt purification. Similar membranes are attractive as separators in vanadium redox flow batteries. Selective partitioning of divalent counter-ions into ion-exchange membranes even allows transport of these ions against their concentration gradients in salt mixtures. However, separations of ions with the same charge is more challenging. Recent research demonstrated highly selective ion “sieving” at small scales. Here, separations using electrical potentials and differences in ion electrophoretic mobilities are promising, but relatively unexplored. Carrier-mediated transport affords high selectivity in liquid membranes, but these systems are not very stable, and selective transport via hopping between anchored carriers has proven elusive. Finally, this paper discusses how concentration polarization decreases selectivities in many membrane processes. Although development of selective, inexpensive ion-separation membranes is a work in progress, successes in water softening and edible salt purification suggests that future selective membranes will serve as complementary methods to traditional purification techniques.

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

Fully gauge-fixed SU(2) Hamiltonian for quantum simulations

Here, we demonstrate how to construct a fully gauge-fixed lattice Hamiltonian for a pure SU(2) gauge theory. Our work extends upon previous work, where a formulation of an SU(2) lattice gauge theory was developed that is efficient to simulate at all values of the gauge coupling. That formulation utilized maximal-tree gauge, where all local gauge symmetries are fixed and a residual global gauge symmetry remains. By using the geometric picture of an SU(2) lattice gauge theory as a system of rotating rods, we demonstrate how to fix the remaining global gauge symmetry. In particular, the quantum numbers associated with total charge can be isolated by rotating between the lab and body frames using the three Euler angles. The Hilbert space in this new “sequestered” basis partitions cleanly into sectors with differing total angular momentum, which makes gauge-fixing to a particular total charge sector trivial, particularly for the charge-zero sector. In addition to this sequestered basis inheriting the property of being efficient at all values of the coupling, we show that, despite the global nature of the final gauge-fixing procedure, this Hamiltonian can be simulated using quantum resources scaling only polynomially with the lattice volume.

Lattice gauge theory↗