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DOE OSTI2020

MARBLE: A Multi-GPU Aware Job Scheduler for Deep Learning on HPC Systems

Deep learning (DL) has become a key tool for solving complex scientific problems. However, managing the multi-dimensional large-scale data associated with DL, especially atop extant multiple graphics processing units (GPUs) in modern supercomputers poses significant challenges. Moreover, the latest high-performance computing (HPC) architectures bring different performance trends in training throughput compared to the existing studies. Existing DL optimizations such as larger batch size and GPU locality-aware scheduling have little effect on improving DL training throughput performance due to fast CPU-to-GPU connections. Additionally, DL training on multiple GPUs scales sublinearly. Thus, simply adding more GPUs to a system is ineffective. To this end, we design MARBLE, a first-of-its-kind job scheduler, which considers the non-linear scalability of GPUs at the intra-node level to schedule an appropriate number of GPUs per node for a job. By sharing the GPU resources on a node with multiple DL jobs, MARBLE avoids low GPU utilization in current multi-GPU DL training on HPC systems. Our comprehensive evaluation in the Summit supercomputer shows that MARBLE is able to improve DL training performance by up to 48.3% compared to the popular Platform Load Sharing Facility (LSF) scheduler. Compared to the state-of-the-art of DL scheduler, Optimus, MARBLE reduces the job completion time by up to 47%.

Han, Jingoo↗

DOE OSTI2021

FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica

Numerical simulations of Greenland and Antarctic ice sheets involve the solution of large-scale highly nonlinear systems of equations on complex shallow geometries. This work is concerned with the construction of Schwarz preconditioners for the solution of the associated tangent problems, which are challenging for solvers mainly because of the strong anisotropy of the meshes and wildly changing boundary conditions that can lead to poorly constrained problems on large portions of the domain. Here, two-level GDSW (Generalized Dryja–Smith–Widlund) type Schwarz preconditioners are applied to different land ice problems, i.e., a velocity problem, a temperature problem, as well as the coupling of the former two problems. We employ the MPI-parallel implementation of multi-level Schwarz preconditioners provided by the package FROSch (Fast and Robust Schwarz)from the Trilinos library. The strength of the proposed preconditioner is that it yields out-of-the-box scalable and robust preconditioners for the single physics problems. To our knowledge, this is the first time two-level Schwarz preconditioners are applied to the ice sheet problem and a scalable preconditioner has been used for the coupled problem. The pre-conditioner for the coupled problem differs from previous monolithic GDSW preconditioners in the sense that decoupled extension operators are used to compute the values in the interior of the sub-domains. Several approaches for improving the performance, such as reuse strategies and shared memory OpenMP parallelization, are explored as well. In our numerical study we target both uniform meshes of varying resolution for the Antarctic ice sheet as well as non uniform meshes for the Greenland ice sheet are considered. We present several weak and strong scaling studies confirming the robustness of the approach and the parallel scalability of the FROSch implementation. Among the highlights of the numerical results are a weak scaling study for up to 32 K processor cores (8 K MPI-ranks and 4 OpenMP threads) and 566 M degrees of freedom for the velocity problem as well as a strong scaling study for up to 4 K processor cores (and MPI-ranks) and 68 M degrees of freedom for the coupled problem.

58 GEOSCIENCES↗

DOE OSTI2020

Measuring the effectiveness of high-performance Co-Optima biofuels on suppressing soot formation at high temperature

Soot emissions in combustion are unwanted consequences of burning hydrocarbon fuels. The presence of soot during and following combustion processes is an indication of incomplete combustion and has several negative consequences including the emission of harmful particulates and increased operational costs. Efforts have been made to reduce soot production in combustion engines through utilizing oxygenated biofuels in lieu of traditional nonoxygenated feedstocks. The ongoing Co-Optimization of Fuels and Engines (Co-Optima) initiative from the US Department of Energy (DOE) is focused on accelerating the introduction of affordable, scalable, and sustainable biofuels and high-efficiency, low-emission vehicle engines. The Co-Optima program has identified a handful of biofuel compounds from a list of thousands of potential candidates. In this study, a shock tube was used to evaluate the performance of soot reduction of five high-performance biofuels downselected by the Co-Optima program. Current experiments were performed at test conditions between 1,700 and 2,100 K and 4 and 4.7 atm using shock tube and ultrafast, time-resolve laser absorption diagnostic techniques. The combination of shock heating and nonintrusive laser detection provides a state-of-the-art test platform for high-temperature soot formation under engine conditions. Soot reduction was found in ethanol, cyclopentanone, and methyl acetate; conversely, an α-diisobutylene and methyl furan produced more soot compared to the baseline over longer test times. For each biofuel, several reaction pathways that lead towards soot production were identified. The data collected in these experiments are valuable information for the future of renewable biofuel development and their applicability in engines.

09 BIOMASS FUELS↗

DOE OSTI2026

Single‐Step Recovery of Water from Stable Crude Oil‐Water Emulsion Using Surface Engineered Hybrid Inorganic‐Polymer Membranes

ABSTRACT Separating crude oil from water remains one of the most stubborn challenges in environmental remediation, especially for surfactant‐stabilized emulsions that resist conventional demulsification methods. Here, we report a scalable strategy for achieving near‐zero‐discharge separation of crude oil emulsions using a single superhydrophilic membrane. By applying low‐temperature atomic layer deposition (ALD) of various metal oxides onto activated polyvinylidene fluoride (PVDF) membranes, we create atomically precise surface‐engineered (SE) membranes that maintain an exceptionally strong hydration layer at the membrane‐feed interface, even at high oil loadings. Among the various metal oxides, TiO 2 ‐modified SE membranes exhibit superior interfacial water stability, enabling sustained dewatering of complex crude oil‐water emulsions with >98% separation efficiency and >97% water recovery, compared to only 24.6% water recovery for the pristine membrane. This separation performance surpasses conventional hydrophilic membranes and is comparable to complex Janus channel membrane systems, demonstrating near‐complete emulsion separation using a single membrane. This low‐temperature membrane surface engineering process with atomic‐level precision and potential for scalability via roll‐to‐roll fabrication is promising for industrial‐scale, energy‐efficient water treatment and oil spill remediation applications.

Sengupta, Bratin [Applied Materials Division Argon↗

DOE OSTI2022

Scalable synthesis of supported catalysts using fluidized bed atomic layer deposition

Overcoating layers deposited on the surface of heterogeneous catalysts using atomic layer deposition (ALD) have been shown to increase catalyst activity, lifetime, and selectivity. In this study, we performed Al 2 O 3 ALD and Pd ALD in a commercial fluidized bed reactor on high surface area mesoporous powder supports to create overcoated catalysts with high precursor utilization. We investigated the reaction mechanism for both Al 2 O 3 ALD and Pd ALD using in situ mass spectrometry and developed a mathematical model to understand the precursor saturation behaviors. We characterized the catalyst samples using a variety of techniques to measure the surface area, porosity, composition, and surface chemistry of the overcoated catalysts. Finally, we used propane dehydrogenation as a probe reaction to evaluate the performance of the catalysts prepared by fluidized bed ALD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Enabling Scalable VQE Simulation on Leading HPC Systems

Large-scale simulations of quantum circuits pose significant challenges, especially in the context of quantum chemistry, due to the number of qubits, circuit depth, and the number of circuits needed per problem. High-performance computing (HPC) systems offer massive computational capabilities that could help overcome these obstacles. We developed a high-performance quantum circuit simulator, called NWQ-Sim, and demonstrate its capability to simulate large quantum chemistry problems on NERSC's Perlmutter supercomputer. Integrating NWQ-Sim with XACC, we have executed QPE and VQE algorithms for downfolded quantum chemistry systems at unprecedented scales. Our work demonstrates the potential of leveraging HPC resources to advance quantum chemistry and other applications of near-term quantum devices.

Wang, Meng↗

DOE OSTI2022

Environmental, Economic, and Scalability Consideration of Selected Biomass-Derived Blendstocks for Mixing-Controlled Compression Ignition (MCCI) Engines

Co-Optimization of Fuels & Engines (Co-Optima) explores how simultaneous innovations in fuels and engines can boost fuel economy and vehicle performance while reducing emissions. The Analysis of Sustainability, Scale, Economics, Risk and Trade (ASSERT) team supports Co-Optima goals by evaluating environmental and economic drivers and the potential of bioblendstock candidates. The team assesses potential benefits or drawbacks of deploying co-optimized fuels and engines in the transportation sector. This presentation provides guidance into which Co-Optima bioblendstocks may be most economically and environmentally viable when scaled up.

biomass↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2020

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Drop-in sustainable aviation fuels enabled by feedstock-agnostic lignin deoxygenation

Current sustainable aviation fuels (SAFs) require blending with petroleum-derived fuels due to incomplete hydrocarbon distributions, most notably a lack of aromatics. Lignin, the most abundant renewable source of aromatics, is a promising feedstock for addressing this limitation. Here, we demonstrate a sequential reductive catalytic fractionation and continuous hydrodeoxygenation process that converts multiple woody feedstocks into aromatic hydrocarbons at up to 93% of the theoretical carbon yield. Blending these products with commercial SAFs produces drop-in compatible fuels with elastomer swell performances equivalent to conventional aviation fuels. The process is adaptable across multiple biomass sources, yielding aromatic hydrocarbons with consistent enthalpic efficiencies and fuel properties. These findings establish a scalable route to 100% drop-in SAFs, leveraging lignin-derived aromatics within the existing biofuels infrastructure.

09 BIOMASS FUELS↗

DOE OSTI2023

Insights From Dayflow: A Historical Streamflow Reanalysis Dataset for the Conterminous United States

Abstract Reconstructed historical streamflow time series can supplement limited streamflow gauge observations. However, there are common challenges of typical modeling approaches: process‐based hydrologic models can be data/computation‐intensive, and statistics‐based models can be region/stream‐specific. Here we present a nationally scalable modeling framework integrating the simulated runoff from the Variable Infiltration Capacity (VIC) model with the Routing Application for Parallel computatIon of Discharge (RAPID) routing model leveraging high‐performance computing. We demonstrate an efficient method of assimilating streamflow at US Geological Survey (USGS) streamflow monitoring sites using a simple hierarchical approach in the VIC‐RAPID framework. The result is a reconstructed 36‐year (1980–2015) daily and monthly streamflow dataset (Dayflow) at ∼2.7 million NHDPlusV2 stream reaches in the conterminous US (CONUS). We perform a comprehensive evaluation at 7,526 USGS sites and characterize their error statistics. The results demonstrate that 49% of the USGS sites demonstrate Kling–Gupta Efficiency (KGE) > 0.5 and 58% of the sites show percentage bias within ±20% for the daily naturalized streamflow. Streamflow data assimilation across CONUS shows an overall improvement over naturalized streamflow, notably in the western semiarid‐to‐arid regions. Comparison to other national and global streamflow reanalysis datasets such as the National Water Model and Global Reach‐scale A priori Discharge Estimates for SWOT demonstrates improved KGE, reduced bias, and directions for Dayflow improvements. Investigations of error statistics with key hydrologic, hydroclimatic, and geomorphologic basin characteristics reveal region‐specific patterns which may help improve future framework applications. Overall, Dayflow may enable a better understanding of hydrologic conditions in a changing environment, especially in locations currently not represented by streamflow monitoring networks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Enhanced power density in zero-vacuum-gap thermophotovoltaic devices

Thermophotovoltaic (TPV) devices, which convert infrared thermal radiation from a hot emitter into electricity, hold great promise for applications in energy storage and waste heat recovery. While recent advancements have developed TPV devices with high efficiency, much less attention has been focused on improving the power density. Current TPV methods face challenges in significantly boosting the power density using emitters at very high temperatures (>2000 °C) or using complex, costly architectures such as near-field TPV. Here, we present the first experimental demonstration of a novel far-field TPV concept called “zero-vacuum-gap TPV” that eliminates the vacuum or gas-filled gap in conventional designs. By incorporating a high-index, infrared-transparent, and thermally insulating fused quartz spacer, we achieved a two-fold increase in power density compared to the far-field counterpart under identical conditions. Notably, in our experiment, the zero-vacuum-gap far-field design transforms a less-optimized, low-power-density far-field device into one with one of the highest power densities reported at moderate temperatures (700–1100 °C). Moreover, our measurements using a graphite emitter surpass the blackbody limit for gap-integrated far-field devices and match the performance of near-field TPV devices with an ultrathin 200-nm gap. Our findings suggest that zero-vacuum-gap TPV offers potential for cost-effective, scalable manufacturing using current technologies. Additionally, our modelling predicts that further power enhancements over one order of magnitude are possible with other spacer materials.

14 SOLAR ENERGY↗

DOE OSTI2024

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Cooper, Emma C. (ORCID:0000000190554098)↗

DOE OSTI2025

Scalable Parallel Measurement of Individual Nitrogen-Vacancy Centers

The nitrogen-vacancy (NV) center in diamond is a solid-state spin defect that has been widely adopted for quantum sensing and quantum information processing applications. Typically, experiments are performed either with a single isolated NV center or with an unresolved ensemble of many NV centers, resulting in a trade-off between measurement speed and spatial resolution or control over individual defects. In this work, we introduce an experimental platform that bypasses this trade-off by addressing multiple optically resolved NV centers in parallel. We perform charge- and spin-state manipulations selectively on multiple NV centers from within a larger set, and we manipulate and measure the electronic spin states of over 100 NV centers in parallel. We show that the high signal-to-noise ratio of the measurements enables the detection of shot-to-shot pairwise correlations between the spin states of 108 NV centers, corresponding to the simultaneous measurement of 5778 unique correlation coefficients. We discuss how our platform can be scaled to parallel experiments with thousands of individually resolved NV centers. These results enable parallelized high-throughput sensing experiments that retain the spatial resolution of single defects and will, thereby, help to unlock advances in applications such as single-molecule NMR and characterization of integrated circuits. In addition, our approach to multiplexing provides a natural platform for the application of recently developed correlated sensing techniques.

NV centers↗

DOE OSTI2021

Processing of SOFC Anodes for Enhanced Intermediate Temperature Catalytic Activity at High Fuel Utilization (Final Report)

The overall objective was to infiltrate anodes with nanoparticle catalysts so that anode catalytic performance can be improved as anode operation temperature is reduced from 800°C to intermediate temperatures of 700°C and 600°C and as anode fuel utilization is increased. The addition of nickel nanoparticle catalysts into the anode improves performance by providing additional triple phase boundaries (TPBs), increasing the density of electrochemical reaction sites. To achieve this goal, both liquid phase and vapor phase methods for depositing nickel nanoparticles in the anode active layer, the region of the anode near the electrolyte where the electrochemical reaction occurs. It is imperative that nanoparticles are stable against coarsening during long-term operation, so the mechanisms and kinetics of nanoparticle stability during cell operation will be investigated. Finally, priority is given to processes that are scalable and easily transferable to industry, leveraging existing Ni-YSZ cermet anode technology. Utilizing nickel nanoparticles at different temperatures has different considerations that will be addressed. At the upper end of the intermediate temperature range (800°C), it is important that performance improvement is maintained over long periods by mitigating the instability of nanoparticles. At the middle of the temperature range (700°C), it is important that performance improvement is maintained at high fuel utilizations. At the lower end of the temperature range (600°C), where the performance of cells is poor due to increased resistances, it is important to improve anode performance by significantly increasing the electrochemical reaction site density, while nanoparticle durability is less of an issue. Finally, the objective was to explore mixed ionic and electronic (MIEC) nanoparticle catalysts like GDC to see if 2-phase boundaries can be created to increase the electrochemical reactions at the anode.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Aerogel with Unparalleled Performance and Cost-Effectiveness for All Insulation Needs (CRADA Final Report)

Westwood Aerogel investigated the reproducible production of an ambiently or electromagnetic spectrum assisted drying of extremely porous aerogel materials. Traditional aerogel processing is limited by low throughput and scalability due to supercritical drying. Westwood Aerogel’s advanced manufacturing process enables commercialization and deployment at scale, making aerogel materials more accessible to a range of industries and applications. We focused on shifting from the lab scale environment to manufacturing prototypes. The initial manufacturing model is based on the float glass process, which is the industry standard for glass window production, and the paper drying process. By incorporating an ambient drying process a continuous line model can be used from start to finish. The project team worked to develop a continuous manufacturing system to scale-up and reduce costs while maintaining the lightweight and insulative properties of aerospace-grade aerogel. While aerogels are extremely insulative, lightweight, and thin materials (making them ideal insulators across application spaces), high costs and stagnant innovation have hampered their wider adoption. Traditional manufacturing of aerogel requires supercritical extraction of pore fluid to obtain highly insulating materials, which is an energy intensive batch process and limits aerogel production output. Westwood Aerogel’s proprietary ambient drying process enables the continuous production of highly insulative aerogel materials without the use of supercritical extraction.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Watching the Watchers with Verified Formal-Assurance Tools (Abbreviated Final Report)

The “Watching the Watchers” project studied the problem of establishing assurance cases for tools that are used to assure other things. Specifically, we were interested in understanding the tools and techniques one could apply to software to build an assurance case to evaluate their applicability, difficulty, level of assurance provided, and scalability. To do so we chose a set of use cases of relevance to LLNL and our various DOE and non-DOE partners and developed demonstrators to perform this evaluation. Our key focal point was around additive manufacturing problems and assurance gaps that we identified in the additive manufacturing workflow from start to completion. We also explored other areas related to AI, data analysis, and concurrent programming. Follow-on research is planned to take our prototypes from this project and adapt and mature them to fit LLNL mission applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

UltraSep Acoustic Separation Platform

UltraSep is an intelligent ultrasonic separation platform that transforms solid–liquid separation through real-time eigenfrequency resonance locking and ultra-low power energy optimization. By dynamically matching ultrasonic output to system resonance while maximizing particulate removal per unit of applied energy, UltraSep replaces centrifugation and fouling-prone filtration with precision-controlled acoustic forces that significantly reduce power consumption, mechanical complexity, and operating cost while improving recovery performance. This integrated platform unites patented resonance-based acoustic control and energy-per-removal optimization with chemistry-enhanced separation and proprietary system software into a scalable, high-impact commercial technology.

42 ENGINEERING↗