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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Broadening of the drumhead-mode spectrum due to in-plane thermal fluctuations of two-dimensional trapped ion crystals in a Penning trap

Two-dimensional crystals of ions stored in Penning traps are a leading platform for quantum simulation and sensing experiments. For small amplitudes, the out-of-plane motion of such crystals can be described by a discrete set of normal modes called the drumhead modes, which can be used to implement a range of quantum information protocols. However, experimental observations of crystals with Doppler-cooled and even nearground- state-cooled drumhead modes reveal an unresolved drumhead-mode spectrum. In this work, we establish in-plane thermal fluctuations in ion positions as a major contributor to the broadening of the drumhead-mode spectrum. In the process, we demonstrate how the confining magnetic field leads to unconventional in-plane normal modes, whose average potential and kinetic energies are not equal. This property, in turn, has implications for the sampling procedure required to choose the in-plane initial conditions for molecular-dynamics simulations. Further, for current operating conditions of the NIST Penning trap, our study suggests that the two-dimensional crystals produced in this trap undergo in-plane potential-energy fluctuations of the order of 10mK. Our study therefore motivates the need for designing improved techniques to cool the in-plane degrees of freedom.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A Virtual Space Vector Model Predictive Control for a Seven-Level Hybrid Multilevel Converter

This paper proposes a virtual space vector (VSV) model predictive control (MPC) for a three-phase seven-level (7L) hybrid multilevel converter (HMC), where each phase consists of an active-neutral-point-clamped converter with a floating H-bridge. To achieve the best current tracking, which is the primary goal of the proposed algorithm, a novel geometrical positioning approach is proposed to select the optimal voltage vector among all the realspace vectors and VSVs. Then, all the possible switching sequences that belong to the optimal voltage vector are evaluated to realize the dc capacitor voltage balancing and common-mode voltage reduction. Through an external modulator, the optimal voltage vector can be synthesized by using either one-, three-, or seven-segment switching sequence. Compared with the conventional MPC, the proposed VSV-MPC can reduce not only the computational burden but also the current THD. Both simulation and experimental results obtained on silicon carbide based 7L-HMC prototype are presented to validate the feasibility and effectiveness of the proposed VSVMPC strategy.

42 ENGINEERING↗
DOE OSTI2023

Extraction of the strong coupling with HERA and EIC inclusive data

Sensitivity to the strong coupling $∝$ s ($M^{2}_{Z}$) is investigated using existing Deep Inelastic Scattering data from HERA in combination with projected future measurements from the Electron Ion Collider (EIC) in a next-to-next-to-leading order QCD analysis. A potentially world-leading level of precision is achievable when combining simulated inclusive neutral current EIC data with inclusive charged and neutral current measurements from HERA, with or without the addition of HERA inclusive jet and dijet data. The result can be obtained with substantially less than one year of projected EIC data at the lower end of the EIC centre-of-mass energy range. Some questions remain over the magnitude of uncertainties due to missing higher orders in the theoretical framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗
DOE OSTI2022

Validation of energetic particle transport models for time-dependent integrated simulations of burning plasmas (Final ST Report)

The major goal of this project is to deliver a suite of validated energetic particle (EP) modeling tools for time-dependent integrated tokamak simulations (with the code TRANSP) that can be used for modeling plasma discharges featuring a substantial EP content, such as burning plasmas. During FY20, issues were resolved with an energetic particle transport model called TGLF-EP, with improvements made to the TGLF-EP workflow, generation of input files, and interpretation of runs on the NERSC supercomputing cluster. The recipient also gave input on simulations with the code TRANSP to reproduce the reference JET discharge, and assessed the accuracy and uncertainties of the simulated B field, current evolution, thermal plasma profiles, stored energy, etc. Input was also given on interpretation of fast-ion instability calculations and recommendations for modifications to JET afterglow discharge for the upcoming DT campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Modeling Gray Zone Tactics Used Against Green Energy Supply Chains

This report documents a project demonstrating capabilities within Lawrence Livermore National Laboratory’s Cyber and Infrastructure Resilience program to place newly developed numeric simulations into the current, larger geopolitical context. The emergence of state-backed gray zone threats introduces an unprecedented challenge because they fall short of armed conflict and involve ambiguous tactics, requiring a fresh analytical approach to understanding their potential impact and countering them. In the context of our project, we analyze how China might use gray zone tactics to degrade the power of the United States, including our influence over global efforts to combat climate change.

25 ENERGY STORAGE↗
DOE OSTI2024

Improving NOvA's Sterile Neutrino Search with the Booster Neutrino Beam

The NOvA experiment’s most recent search for eV-scale sterile neutrinos is systematically limited in the region of parameter space where $\Delta m^2_{41} \gtrsim 1~\mathrm{eV}^2$. This region of parameter space is preferred by sterile neutrino interpretations of current experimental anomalies; improving sensitivity here is high-priority. When added directly into the fit, additional data samples which are subject to orthogonal systematic uncertainties act as in-situ constraints, breaking the degeneracy between systematic uncertainties and sterile-induced oscillations. The NOvA experiment consists of two functionally identical detectors, 14.6 mrad off-axis of the NuMI beam, with the Near Detector (Far Detector) 1 km (810 km) from the beam source. The Near Detector’s position on-site at Fermilab means that it is also able to observe neutrinos from a second neutrino beam, the BNB, 160 mrad off-axis. NOvA has been taking BNB data since 2015, but has not yet analysed these data. The BNB and NuMI are subject to different beam-related uncertainties, allowing us to leverage this sample as an in-situ constraint. This poster will present the current status, preliminary simulations, and potential additional uses of this unique experimental setup

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Combined TDLAS and chemiluminescence imaging in a flat flame burner operated with NH3/H2 blends

Ammonia is viewed as a viable hydrogen carrier due to favorable storage and transport characteristics. While it can be re-converted to hydrogen at point-of-use via thermal catalytic cracking, direct utilization in combustion systems can result in reduced costs and improved efficiency. A major barrier to this approach is the low flammability and potential for high nitrogen oxide emissions, driven by fuel-bound nitrogen and complex kinetic pathways. While a number of kinetic mechanisms currently exist for simulating ammonia combustion, a major need continues to be direct information about species profiles in easy-to-model systems capable of isolating chemical kinetics from multi-dimensional fluid dynamic effects. This paper reports on recent species measurements made in a flat flame burner using a combined tunable-diode-laser-absorption-spectroscopy (TDLAS) and chemiluminescence imaging approach. Three flame conditions were included representative of NH3/H2 blends, partially cracked NH3 (inc. N2), and 100% NH3 with enhanced air (30% oxygen). Two NIR distributed feedback (DFB) laser diodes were used to determine H2O concentration and temperature (via ratio thermometry) at various radial and axial positions, after which an inverse Abel transform was used to infer centerline values. Companion images were collected using a Princeton Instrument PI-MAX intensified camera equipped with a 105mm UV lens and multiple filter sets targeting OH*, NH*, and NH2* emission. Results were compared to companion Cantera burner-stabilized flame simulations using various kinetic mechanisms. A recent mechanism including excited species chemistry was also included, to investigate whether excited and ground state profiles exhibit significant differences.

ammonia combustion↗
DOE OSTI2025

Optimized V1G and V2G Electric Vehicle Fleet Management and Grid Transaction at Marine Corps Air Station Miramar in San Diego, CA

The overall technical goal of the project was to demonstrate an all-electric bi-directional non-tactical fleet at Marine Corps Air Station (MCAS) Miramar that was integrated and controlled with other distributed energy resources (DERs) (i.e., PV, stationary battery, and building loads) to provide resilience to critical electric loads in the event of grid outages, to minimize charging costs, and to provide economic energy resources to electricity markets. In this project, the specific, technical objectives were: 1. Demonstrate that bi-directional electric vehicles can provide critical complementary services to fixed storage batteries in microgrid applications while performing function as non-tactical vehicles. 2. Demonstrate participation of bi-directional (V2G) and unidirectional (V1G) PEVs for demand management and minimization of charging costs. 3. Demonstrate integration of multiple DERs for grid service participation. US Marine Corps Air Station (MCAS) Miramar in San Diego was the site of this electric vehicle-to-microgrid-utility grid test and demonstration project. Existing microgrid assets in this study included (1) a public works building; (2) a 30-kW rooftop photovoltaic (PV) system and (3) a separate 250 kW carport PV system. In this project, six bi-directional V2G vans were located at the MCAS Miramar’s showcase building-scale microgrid to develop and test technical capabilities that V2G can provide in microgrid applications (e.g., cost reduction and resiliency). These resources provided aggregated demand management and simulated participation in current retail DR programs. The vehicles used in this demonstration were selected because they provided functionality that MCAS Miramar needed, 15 passenger transport and facilities work cargo carrying capacity, and bi-directional charging capability that the research project required. All vehicles in this study were manufactured and distributed by VIA Motors, Inc. There were six vehicles total and each was VIA’s VTRUX eREV V2G model, a modified General Motors Chevrolet 2500 2WD van. Three of the vans were configured as passenger vans and the other three were configured as cargo vans. Each van had an on-board bi-direcrtional inverter/charger, Bel Power Solutions model 350INVCHGT150-120-240-8G nominally rated at +/-15 kW. The VIA van’s charging connector follows the J1772 charging protocol. The bi-directional EVSEs demonstrated in this study were manufactured by Coritech, Inc. Each VGI-80-AC charging station enabled enhanced V2G charging capability to a Clipper Creek CS-100 charging module. The enhanced capabilities included ethernet communication following the SEP2.0 protocol with a distributed energy resource function set and an operator screen displaying real-time SOC, voltage, and current. The VGI-80-AC charging stations are classified as level 2 with a maximum current output of 80 A or effectively 19 kW. The VIA van’s onboard charger limited the charging and discharging power to 15 kW in each direction. A control computer was installed in the EWOC and connected to an existing monitor. The V2G control communication network was a completely stand-alone closed system that did not have any connection to any other networks on the base. A cybersecure remote communication connection was created with a cellular modem, firewall hardware, and a virtual private network configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

High-Fidelity Building Emulator for Integrated Comfort and Energy Analysis using EnergyPlus and Radiance

The growing need for smart, energy-efficient, and occupant-centric buildings has created a demand for advanced control systems that can optimize building operations to balance energy savings, demand flexibility, and comfort. However, current building energy simulation tools, such as EnergyPlus, have limitations that hinder the development and evaluation of these complex control systems. To address this challenge, we introduce a high-fidelity building emulator that dynamically couples EnergyPlus with Radiance for enhanced daylight modeling. The introduced workflow allows researchers and practitioners to rapidly develop and evaluate innovative control solutions. An example study looking at a south-facing office zone revealed up to 67% deviation in predicted light levels, which can significantly impact building assessment.

Yu, Tammie↗
DOE OSTI2022

Towards Fast and Accurate Predictions of Radio Frequency Power Deposition and Current Profile via Data-driven Modeling

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($n_{e0}$, $T_{e0}$, $I_p$, $B_t$, $R_0$, $n_{||}$, $Z_{eff}$, $V_{loop}$, $P_{LHCD}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

HYDRODYNAMIC ANALYSIS OF MACROALGAE LOCAL MODEL USING COMPUTATIONAL FLUID DYNAMICS

In this article, a local scale, fully nonlinear coupled fluid-structural interaction (FSI) sugar kelp model has been developed using a computational fluid dynamics (CFD) method. In this model, to be consistent with available experimental data, the sugar kelp is approximated as elongated rectangles with smoothed isosceles triangles at the ends and a single kelp model with one end fixed in a channel with constant current model is developed. Several different current speeds are simulated, and the resulting drag forces and calculated drag coefficients are validated by comparison with experimental data from the literature. In a previous study, a global scale model was developed using a computational structural dynamics (CSD) method to simulate macroalgae farming system and guide the system configuration design. In the global scale model, the hydrodynamic forces are calculated using Morison’s equation and the kinematics and dynamics of the sugar kelp are simplified and the group of kelps attached to the long line is modeled as a slender structure with the same length and an effective diameter such that the volumes are consistent with the real physical system. This simplified model matches the weight and buoyancy but adjusting the hydrodynamic properties when the general hydrodynamic coefficients are employed. Therefore, optimal hydrodynamic coefficients used in global scale model were determined to obtain the hydrodynamic force more accurately. The validated local scale model is then be applied to determine the hydrodynamic coefficients of the simplified sugar kelp model for global dynamic analysis.

hydrodynamic modeling, macroalgae, CFD model↗
DOE OSTI2023

Energy-Efficient and Resilient Infrastructure: Simulation, Validation, and Installation

Advanced, high-performance computing at the National Renewable Energy Laboratory (NREL) has enabled access to vast data resources with cutting-edge software techniques to understand, design, plan for, and maintain energy-efficient and resilient infrastructure. We have focused on cities and airports, but the technology we have developed will easily translate to seaports, inland ports, military installations, or other complex and large-scale energy-intensive systems. We can digitally simulate and explore current and future scenarios to make datadriven decisions for optimizing advanced energy systems, transportation and building operations, infrastructure planning and expansion, and battery storage to guide short- and long-term investments, electrification strategies, and integration of new technologies.

Athena↗
DOE OSTI2026

Error quantification and comparison of binary neutron star gravitational waveforms from numerical relativity codes

Future gravitational wave detections of merging binary neutron star systems have the possibility to tightly constrain the equation of state of dense nuclear matter. In order to extract such constraints, gravitational waveform models need to be calibrated to accurate numerical relativity simulations of the late inspiral and merger. In this work, we take an essential step toward classifying the error and potential systematics in current generation numerical relativity simulations of merging binary neutron stars. To this end, we perform a direct comparison of two codes (fil, spec), which differ in many aspects, including the numerical methods and discretizations used and equations solved. We find that despite these different approaches, the codes are—within current numerical resolution bounds—fully consistent, and broadly comparable in cost for a given accuracy level. Our results indicate that the error in the waveforms is primarily dominated by the hydrodynamic evolution, consistent with earlier findings in the literature. Finally, we also discuss current limitations and cost estimates for numerical relativity simulations to reach the accuracies required in the era of next-generation gravitational detectors.

gravitational waves↗
DOE OSTI2021

Estimating outflow masses and velocities in merger simulations: Impact of r -process heating and neutrino cooling

Here, the determination of the mass, composition, and geometry of matter outflows in black hole-neutron star and neutron star-neutron star binaries is crucial to current efforts to model kilonovae and to understand the role of neutron star merger in r-process nucleosynthesis. In this manuscript, we review the simple criteria currently used in merger simulations to determine whether matter is unbound and what the asymptotic velocity of ejected material will be. We then show that properly accounting for both heating and cooling during r-process nucleosynthesis is important to accurately predict the mass and kinetic energy of the outflows. These processes are also likely to be crucial to predict the fall-back timescale of any bound ejecta. We derive a model for the asymptotic velocity of unbound matter and binding energy of bound matter that accounts for both of these effects and that can easily be implemented in merger simulations. We show, however, that the detailed velocity distribution and geometry of the outflows can currently only be captured by full three-dimensional fluid simulations of the outflows, as nonlocal effect ignored by the simple criteria used in merger simulations cannot be safely neglected when modeling these effects. Finally, we propose the introduction of simple source terms in the fluid equations to approximately account for heating/cooling from r-process nucleosynthesis in future seconds-long three-dimensional simulations of merger remnants, without the explicit inclusion of out-of-nuclear statistical equilibrium reactions in the simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Impact of ocean model resolution on understanding the delayed warming of the Southern Ocean

Abstract Currently available historical climate change simulations indicate a relatively delayed Southern Ocean warming, particularly poleward of the Antarctic Circumpolar Current (ACC) compared much of the rest of the globe. However, even this simulated delayed warming is inconsistent with observational estimates which show a cooling trend poleward of the ACC for the period 1979–2014. A fully coupled model run at two resolutions, i.e. ocean eddy parameterized and ocean eddy resolving, driven by historical and fixed CO2 concentration is used to investigate forced trends south of the ACC. We analyze the 1961–2005 Southern Ocean surface and upper ocean temperatures trends simulated by the model and observational estimates to understand the observed trends in the SO. At both resolutions, the models successfully reproduce the observed warming response for the northern flank of the ACC. The eddy resolving simulations, however, are able to reproduce the observed near Antarctic cooling in contrast to the eddy parameterized simulation which shows a warming trend. The cause of this inconsistency between the observations and the ocean eddy parameterized climate models is still a matter of debate, and we show here results that suggest resolved ocean meso-scale processes may be an integral part of capturing the observed trends in the Southern Ocean.

Environmental Sciences & Ecology↗
DOE OSTI2022

maestro

MÆSTRO stands for Multi-fidelity Adaptive Ensemble Stochastic Trust Region Optimization and it is a plug n play derivate fee stochastic optimization solver. The problem being considered in MÆSTRO involves fitting Monte Carlo simulations that describe complex phenomena to experiments. This is done by finding parameters of the resource intensive and noisy simulation that yield the least squares objective function value to the noisy experimental data. This problem is solved using a stochastic trust-region optimization algorithm where in each iteration, a local approximation of the simulation signal and of the simulation noise is constructed over data, which is obtained by running the simulation at strategically placed design points within the trust-region around the current iterate. Then the simulation components of the objective are replaced by their approximations and this analytical and closed-form optimization problem is solved to find the next iterate within the trust-region. Then the trust region is moved and the iterations continue until a satisfactory convergence criteria is met.

KRISHNAMOORTHY, MOHAN↗
DOE OSTI2024

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

54 ENVIRONMENTAL SCIENCES↗