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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Recent Experience with the CMS Data Management System

The CMS[1] experiment manages a large-scale data infrastructure, currently handling over 200 PB of disk and 500 PB of tape storage and transferring more than 1 PB of data per day on average between various WLCG[2] sites. Utilizing Rucio[3] for high-level data management, FTS[4] for data transfers, and a variety of storage and network technologies at the sites, CMS confronts inevitable challenges due to the system’s growing scale and evolving nature. Key challenges include managing transfer and storage failures, optimizing data distribution across different storages based on production and analysis needs, implementing necessary technology upgrades and migrations, and efficiently handling user requests. The data management team has established comprehensive monitoring to supervise this system and has successfully addressed many of these challenges. The team’s efforts aim to ensure data availability and protection, minimize failures and manual interventions, maximize transfer throughput and resource utilization, and provide reliable user support. This paper details the operational experience of CMS with its data management system in recent years, focusing on the encountered challenges, the effective strategies employed to overcome them and the ongoing challenges as we prepare for future demands.

Öztürk, Hasan [CERN]↗
DOE OSTI2022

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Projecting Future Energy Production from Operating Wind Farms in North America. Part III: Variability

Abstract Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes. Significance Statement Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.

Coburn, Jacob↗
DOE OSTI2017

Radiation Source Mapping with Bayesian Inverse Methods

In this work, we present a method to map the spectral and spatial distributions of radioactive sources using a limited number of detectors. Locating and identifying radioactive materials is important for border monitoring, in accounting for special nuclear material in processing facilities, and in cleanup operations following a radioactive material spill. Most methods to analyze these types of problems make restrictive assumptions about the distribution of the source. In contrast, the source mapping method presented here allows an arbitrary three-dimensional distribution in space and a gamma peak distribution in energy. To apply the method, the problem is cast as an inverse problem where the system’s geometry and material composition are known and fixed, while the radiation source distribution is sought. A probabilistic Bayesian approach is used to solve the resulting inverse problem since the system of equations is ill-posed. The posterior is maximized with a Newton optimization method. The probabilistic approach also provides estimates of the confidence in the final source map prediction. A set of adjoint, discrete ordinates flux solutions, obtained in this work by the Denovo code, is required to efficiently compute detector responses from a candidate source distribution. These adjoint fluxes form the linear mapping from the state space to the response space. The test of the method’s success is simultaneously locating a set of 137 Cs and 60 Co gamma sources in a room. This test problem is solved using experimental measurements that we collected for this purpose. Because of the weak sources available for use in the experiment, some of the expected photopeaks were not distinguishable from the Compton continuum. However, by supplanting 14 flawed measurements (out of a total of 69) with synthetic responses computed by MCNP, the proof-of-principle source mapping was successful. The locations of the sources were predicted within 25 cm for two of the sources and 90 cm for the third, in a room with an ~4-x 4-m floor plan. Finally, the predicted source intensities were within a factor of ten of their true value.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Reinforcement Learning Control for Enhancing Marine Hydrokinetic Turbine Energy Generation

This paper proposes a reinforcement learning-based method to maximize power generation for a direct-drive marine hydrokinetic turbine. A high levelized cost of energy (LCOE) is preventative in the widespread adoption of many marine energy conversion technologies. A straightforward way to reduce LCOE is to increase conversion efficiency and ensure maximum energy generation. The proposed method utilizes a damping control methodology, varying applied generator torque via a linear relationship between the applied damping coefficient and rotor speed. A state-action-reward-state-action (SARSA) algorithm has been used to learn the optimal control action for a given flow velocity. The proposed SARSA methodology uses Gaussian radial basis functions to create a three-dimensional surface to estimate the relationship between damping coefficient, incoming flow velocity, and coefficient of power (C p ). Here, the SARSA algorithm was compared against a baseline optimal tip speed ratio controller over a year-long flow velocity case profile while considering the effects of biofouling on the turbine system, where the proposed RL method generated 0.92% more energy than the baseline.

Damp↗
DOE OSTI2021

Kinetic Properties of Microbial Exoenzymes Vary With Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile

Current knowledge of the mechanisms driving soil organic matter (SOM) turnover and responses to warming is mainly limited to surface soils, although over 50% of global soil carbon is contained in subsoils. Deep soils have different physicochemical properties, nutrient inputs, and microbiomes, which may harbor distinct functional traits and lead to different SOM dynamics and temperature responses. We hypothesized that kinetic and thermal properties of soil exoenzymes, which mediate SOM depolymerization, vary with soil depth, reflecting microbial adaptation to distinct substrate and temperature regimes. We determined the Michaelis-Menten (MM) kinetics of three ubiquitous enzymes involved in carbon (C), nitrogen (N) and phosphorus (P) acquisition at six soil depths down to 90 cm at a temperate forest, and their temperature sensitivity based on Arrhenius/ Q 10 and Macromolecular Rate Theory (MMRT) models over six temperatures between 4–50°C. Maximal enzyme velocity ( V max ) decreased strongly with depth for all enzymes, both on a dry soil mass and a microbial biomass C basis, whereas their affinities increased, indicating adaptation to lower substrate availability. Surprisingly, microbial biomass-specific catalytic efficiencies also decreased with depth, except for the P-acquiring enzyme, indicating distinct nutrient demands at depth relative to microbial abundance. These results suggested that deep soil microbiomes encode enzymes with intrinsically lower turnover and/or produce less enzymes per cell, reflecting distinct life strategies. The relative kinetics between different enzymes also varied with depth, suggesting an increase in relative P demand with depth, or that phosphatases may be involved in C acquisition. V max and catalytic efficiency increased consistently with temperature for all enzymes, leading to overall higher SOM-decomposition potential, but enzyme temperature sensitivity was similar at all depths and between enzymes, based on both Arrhenius/ Q 10 and MMRT models. In a few cases, however, temperature affected differently the kinetic properties of distinct enzymes at discrete depths, suggesting that it may alter the relative depolymerization of different compounds. We show that soil exoenzyme kinetics may reflect intrinsic traits of microbiomes adapted to distinct soil depths, although their temperature sensitivity is remarkably uniform. These results improve our understanding of critical mechanisms underlying SOM dynamics and responses to changing temperatures through the soil profile.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

Experimental and Computational Investigation of a Translating-Throat Single-Expansion-Ramp Nozzle

An experimental and computational study was conducted on a high-speed, single-expansion-ramp nozzle (SERN) concept designed for efficient off-design performance. The translating-throat SERN concept adjusts the axial location of the throat to provide a variable expansion ratio and allow a more optimum jet exhaust expansion at various flight conditions in an effort to maximize nozzle performance. Three design points (throat locations) were investigated to simulate the operation of this concept at subsonic-transonic, low supersonic, and high supersonic flight conditions. The experimental study was conducted in the jet exit test facility at the Langley Research Center. Internal nozzle performance was obtained at nozzle pressure ratios (NPR's) up to 13 for six nozzles with design nozzle pressure ratios near 9, 42, and 102. Two expansion-ramp surfaces, one concave and one convex, were tested for each design point. Paint-oil flow and focusing schlieren flow visualization techniques were utilized to acquire additional flow data at selected NPR'S. The Navier-Stokes code, PAB3D, was used with a two-equation k-e turbulence model for the computational study. Nozzle performance characteristics were predicted at nozzle pressure ratios of 5, 9, and 13 for the concave ramp, low Mach number nozzle and at 10, 13, and 102 for the concave ramp, high Mach number nozzle.

Deere, Karen A.↗
NASA NTRS2013

Trash-to-Gas: Converting Space Trash into Useful Products

NASA's Logistical Reduction and Repurposing (LRR) project is a collaborative effort in which NASA is determined to reduce total logistical mass through reduction, reuse and recycling of various wastes and components of long duration space missions and habitats. LRR is focusing on four distinct advanced areas of study: Advanced Clothing System, Logistics-to-Living, Heat Melt Compactor and Trash to Supply Gas (TtSG). The objective of TtSG is to develop technologies that convert material waste, human waste and food waste into high-value products. High-value products include life support oxygen and water, rocket fuels, raw material production feedstocks, and other energy sources. There are multiple pathways for converting waste to products involving single or multi-step processes. This paper discusses thermal oxidation methods of converting waste to methane. Different wastes, including food, food packaging, Maximum Absorbent Garments (MAGs), human waste simulants, and cotton washcloths have been evaluated in a thermal degradation reactor under conditions promoting pyrolysis, gasification or incineration. The goal was to evaluate the degradation processes at varying temperatures and ramp cycles and to maximize production of desirable products and minimize high molecular weight hydrocarbon (tar) production. Catalytic cracking was also evaluated to minimize tar production. The quantities of CO2, CO, CH4, and H2O were measured under the different thermal degradation conditions. The conversion efficiencies of these products were used to determine the best methods for producing desired products.

Caraccio, Anne J.↗
NASA NTRS2015

High Thrust-to-Power Annular Engine Technology

Gridded ion engines have the highest efficiency and total impulse of any mature electric propulsion technology, and have been successfully implemented for primary propulsion in both geocentric and heliocentric environments with excellent ground-in-space correlation of performance. However, they have not been optimized to maximize thrust-to-power, an important parameter for Earth orbit transfer applications. This publication discusses technology development work intended to maximize this parameter. These activities include investigating the capabilities of a non-conventional design approach, the annular engine, which has the potential of exceeding the thrust-to-power of other EP technologies. This publication discusses the status of this work, including the fabrication and initial tests of a large-area annular engine. This work is being conducted in collaboration among NASA Glenn Research Center, The Aerospace Corporation, and the University of Michigan.

electric propulsion↗
NASA NTRS2013

Trash to Gas: Converting Space Trash into Useful Products

NASA's Logistical Reduction and Repurposing (LRR) project is a collaborative effort in which NASA is determined to reduce total logistical mass through reduction, reuse and recycling of various wastes and components of long duration space missions and habitats. LRR is focusing on four distinct advanced areas of study: Advanced Clothing System, Logistics-to-Living, Heat Melt Compactor and Trash to Supply Gas (TtSG). The objective of TtSG is to develop technologies that convert material waste, human waste and food waste into high-value products. High-value products include life support oxygen and water, rocket fuels, raw material production feedstocks, and other energy sources. There are multiple pathways for converting waste to products involving single or multi-step processes. This paper discusses thermal oxidation methods of converting waste to methane. Different wastes, including food, food packaging, Maximum Absorbent Garments (MAGs), human waste simulants, and cotton washcloths have been evaluated in a thermal degradation reactor under conditions promoting pyrolysis, gasification or incineration. The goal was to evaluate the degradation processes at varying temperatures and ramp cycles and to maximize production of desirable products and minimize high molecular weight hydrocarbon (tar) production. Catalytic cracking was also evaluated to minimize tar production. The quantities of C02, CO, CH4, and H20 were measured under the different thermal degradation conditions. The conversion efficiencies of these products were used to determine the best methods for producing desired products.

Trash-to-Gas↗
DOE OSTI2026

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation from MODMD, taking into account distinct sources of error. In the ideal case, we prove that the spectral error scales as exp (−Δ⁢𝐸⁢𝑡 max ), where Δ⁢𝐸 is the Hamiltonian spectral gap and 𝑡 max is the maximal simulation time. This analysis provides a rigorous justification of the rapid convergence observed across simulations. To demonstrate the utility of our framework, we consider its application to fundamental tasks, such as determining the low-lying, i.e., ground or excited, energies of representative many-body systems. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices.

quantum algorithms & computation↗
DOE OSTI2021

Second-harmonic generation in nonlinear plasmonic lattices enhanced by quantum emitter gain medium

In this work, we report on a theoretical study of second-harmonic generation (SHG) in plasmonic nanostructures interacting with two-level quantum emitters (QEs) under incoherent energy pump. We generalize the driven-dissipative Tavis–Cummings model by introducing the anharmonic surface plasmon-polariton (SPP) mode coupled to QEs and examine physical properties of corresponding SPP-QE polariton states. Our calculations of the SHG efficiency for strong QE-SPP coupling demonstrate orders of magnitude enhancement facilitated by the polariton gain. We further discuss time-domain numerical simulations of SHG in a square lattice comprising Ag nanopillars coupled to QEs utilizing a fully vectorial nonperturbative nonlinear hydrodynamic model for conduction electrons coupled to Maxwell–Bloch equations for QEs. The simulations support the idea of gain enhanced SHG and show orders of magnitude increase in the SHG efficiency as the QEs are tuned in resonance with the lattice plasmon mode and brought above the population inversion threshold by incoherent pumping. By varying pump frequency and tuning QEs to a localized plasmon mode, we demonstrate further enhancement of the SHG efficiency facilitated by strong local electric fields. The incident light polarization dependence of the SHG is examined and related to the symmetries of participating plasmon modes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty

This work applies an efficient optimization technique based on a multivariate adaptive regression spline (MARS) technique to determine the optimal design and engineering of a potential geothermal production operation at a prospect near Superstition Mountain in Southern California, USA. The faster MARS-based statistical model is used as a surrogate for higher-fidelity physical models within the intensive optimization process. Its use allows for the exploration of the impacts of specific engineering design parameters in the context of geologic uncertainty as a means to both understand and maximize profitability of the production operation. The MARS model is initially developed from a training dataset generated by a finite set of computationally complex hydrothermal models applied to the prospect. Its application reveals that the optimal engineering design variables can differ considerably assuming different choices of hydrothermal flow properties, which, in turn, indicates the importance of reducing the uncertainty of key geologic properties. The major uncertainty sources in the natural-system are identified and ranked first by an efficient MARS-enabled total order sensitivity quantification, which is then used to assist evaluating the effect of geological uncertainties on optimized results. At the Southern California prospect, this parameter sensitivity analysis suggests that groundwater circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method during geothermal extraction. Reservoir histories simulated using optimal parameters with different constraints are analyzed and compared to investigate the longevity and maximum profit of the geothermal resources. The comparison shows that the longevity and profit are very likely to be overestimated by optimizations without appropriate constraints on natural conditions. In addition to geothermal energy production, this optimization approach can also be used to manage other geologic resource operations, such as hydrocarbon production or CO 2 sequestration, under uncertain reservoir conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Geometric dependencies of the mean E × B shearing rate in negative triangularity tokamaks

Abstract This paper presents a comparative study of the poloidal distribution of the mean E × B shearing rate for positive triangularity (PT) and negative triangularity (NT) tokamaks. The effects of flux surface up–down asymmetry due to asymmetric upper and lower triangularities are also considered. Both direct eddy straining and the effects on Shafranov shift feedback loops are examined. Shafranov shift increases the shearing rate at all poloidal angles for all triangularities, due to flux surface compression. The maximum shearing rate bifurcates at a critical triangularity δ crit ( ≲ 0 ) . Thus, the shearing rate is maximal off the outboard midplane for NT, while it is maximal on the outboard midplane for PT. For up–down asymmetric triangularity, the usual up–down symmetry of the shearing rate is broken. The shearing rate at the outboard midplane is lower for NT than for PT, suggesting that the shearing efficiency in NT is reduced. Implications for turbulence stabilisation and confinement improvement in high- β p NT and internal transport barrier discharges are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Beam delivery and beamstrahlung considerations for ultra-high energy linear colliders

As part of the Snowmass'21 community planning excercise, the Advanced Accelerator Concepts (AAC) community proposed future linear colliders with center-of-mass energies up to 15 TeV and luminosities up to 50 × 1034 cm -2 s -1 in a compact footprint. In addition to being compact, these machines must also be energy efficient. We identify two challenges that must be addressed in the design of these machines. First, the Beam Delivery System (BDS) must not add significant length to the accelerator complex. Second, beam parameters must be chosen to mitigate beamstrahlung effects and maximize the luminosity-per-power of the machine. In this paper, we review advances in plasma lens technology that will help to reduce the length of the BDS system, and we detail new Particle-in-Cell simulation studies that will provide insight into beamstrahlung mitigation techniques. We apply our analysis to both $e$ + $e$ - and $γγ$ colliders. The challenges and solutions described in this paper are considered independently. In conclusion, a unified, self-consistent concept for a BDS system for a 15 TeV linear collider will be the subject of future work.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

cuAlign: Scalable Network Alignment on GPU Accelerators

Given two graphs, the objective of network alignment is to find the best one-to-one mapping of vertices in one graph (??) to vertices in the other (??), such that the number of overlaps is maximized. We say that edges(??, ??) ???and(??', ??') ??? are overlapped if ?? is mapped to ??' and ?? is mapped to??'. Network alignment is an important optimization problem with several applications in bioinformatics, computer vision and ontology matching. Since it is an NP-hard problem, efficient heuristics and scalable implementations are necessary. In this work, we introduce a new framework that combines the concepts of intra-network proximity using vertex embedding,Belief Propagation (BP) and approximate weighted matching, and provides qualitative improvements up to22%over state-of-the-art approaches. We also provide scalable implementations on GPU accelerators, demonstrating up to19×speedup for Belief Propagation and 3× speedup for approximate weighted matching relative to previous multithreaded implementation. A combination of combinatorial and algebraic kernels within the network alignment algorithm poses significant hurdles for parallelization. Load imbalance and irregular DRAM traffic limit achievable performance on GPUs. Our novel approach identifies and exploits unique structural proper-ties of the BP-based algorithm and employs code fusion to reduce data movement between different steps of the algorithm. Using a diverse set of inputs, we demonstrate qualitative improvements of our algorithms, and performance gains of our GPU-accelerated implementation. We believe that our work will enable algorithmic improvements and practical applications of network alignment.

Xiang, Lizhi↗
DOE OSTI2014

Waste-to-Energy: Hawaii and Guam Energy Improvement Technology Demonstration Project

The National Renewable Energy Laboratory (NREL) and the U.S. Navy have worked together to demonstrate new or leading-edge commercial energy technologies whose deployment will support the U.S. Department of Defense (DOD) in meeting its energy efficiency and renewable energy goals while enhancing installation energy security. This is consistent with the 2010 Quadrennial Defense Review report1 that encourages the use of 'military installations as a test bed to demonstrate and create a market for innovative energy efficiency and renewable energy technologies coming out of the private sector and DOD and Department of Energy laboratories,' as well as the July 2010 memorandum of understanding between DOD and the U.S. Department of Energy (DOE) that documents the intent to 'maximize DOD access to DOE technical expertise and assistance through cooperation in the deployment and pilot testing of emerging energy technologies.' As part of this joint initiative, a promising waste-to-energy (WTE) technology was selected for demonstration at the Hickam Commissary aboard the Joint Base Pearl Harbor-Hickam (JBPHH), Hawaii. The WTE technology chosen is called high-energy densification waste-to-energy conversion (HEDWEC). HEDWEC technology is the result of significant U.S. Army investment in the development of WTE technology for forward operating bases.

09 BIOMASS FUELS↗