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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Refinement of Vortex Generators in a Streamline-Traced, External-Compression Supersonic Inlet

Computational simulations of the flow within a streamline-traced, external-compression supersonic inlet for Mach 1.664 without and with vortex generators were performed to refine the characterization of the inlet performance as measured by the total pressure recovery and the radial and circumferential total pressure distortion indices at the engine face. The refinement of the simulations concerned two aspects: 1) refinement of the grid for the simulations to evaluate and reduce uncertainty, and 2) refinement of the modeling and design of the vortex generators. The vortex generators studied were rectangular vane-type vortex generators arranged in co-rotating arrays within the subsonic diffuser. The vortex generator geometric factors of interest included the height, circumferential spacing, and angle-of-incidence. The flow through the inlet was simulated numerically through the solution of the steady-state, Reynolds-averaged Navier-Stokes equations on multi-block, structured grids using the Wind-US flow solver. The vortex generators were simulated using either a vortex generator model or with grids generated about each vortex generator. Statistical methods were used to compute confidence intervals and grid convergence indices to establish the uncertainties of the analyses with respect to grid refinement. Design-of-experiments methods were applied to quantify the effects of the geometric factors of the vortex generators. The analyses of the computed results illustrate the complexities of quantifying the uncertainties of the inlet performance and the implications of the uncertainties for the design of a vortex generator array for the STEX inlet.

Slater, John W.↗
DOE OSTI2018

Evaluating the functionality and streamflow impacts of explicitly modelling forest-snow interactions and canopy gaps in a distributed hydrologic model

Many plot-scale observational studies found that snow cover dynamics in forest gaps are distinctly different from open and continuously forested areas, and canopy gaps have the potential to increase snow retention and alter the snow regime. However, our knowledge on forest-snow interactions in discontinuous forests with canopy gaps and their implications for forest and water management is far more limited. We address this knowledge gap through a major enhancement of a widely used distributed hydrologic model, DHSVM. We developed a canopy gap component as a unique vegetation type that is characterized on the subgrid scale, with physics-based representation of processes that allow independent evolution of snowpack in forest gaps. We validated the enhanced DHSVM for three experimental sites in close proximity consisting of a completely open site, a densely forested site, and a canopy gap site. Despite the scale mismatch between point measurements and grid-scale simulations, the modeled snow water equivalent (SWE) agreed reasonably well with the continuous observations collected during one full ablation season at all sites. Evaluation of snowpack energetics suggested that snow ablation at three sites was controlled primarily by net radiation that critically depends on the canopy cover and structure. While the rate of snowmelt remained relatively constant in the forest during the ablation season, the gap demonstrated marked temporal variability in ablation rate as a result of complex interplay between canopy structure of the adjacent forest and the angle of solar incidence. The comparison of this enhanced version to the original DHSVM highlighted the importance of explicitly accounting for forest impacts on the gap snowpack energy balance. Between the two versions, the greatest difference in snow ablation rates was found in smaller gaps. The enhanced model tends to predict notably later melt for small to medium canopy gaps (the ratio of gap radius to canopy height r/h = 1.3), and snow melt rates exhibit great sensitivity to changing gap size in medium-size gaps (0.5 = r/h = 1.3).

Forest-Snow Dynamics, Canopy Gap, Snow Hydrology, ↗
DOE OSTI2018

Polarization Filter for Microstrip Lumped-Element Kinetic Inductance Detectors

Polarization sensitivity is a major requirement in future cosmic microwave background studies. Even though lumped-element kinetic inductance detectors (LEKIDs) have already demonstrated a good performance in the millimeter range, the typical configuration based on linear meander inductors exhibits a cross polarization up to 30%. In this work, we propose a new configuration of LEKIDs coupled to a microstrip transmission line where the continuous ground plane has been replaced with parallel lines in order to be used as a polarizing grid. Microwave simulations and preliminary experiments show that the polarizer acts as an effective ground plane with no influence in the electromagnetic performance and that the cross polarization can be decreased to 3%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

A new high-order spectral difference method for simulating viscous flows on unstructured grids with mixed-element meshes

In the present study, a spectral difference (SD) method is developed for viscous flows on meshes with a mixture of triangular and quadrilateral elements. The standard SD method for triangular elements, which employs Lagrangian interpolating functions for fluxes, is not stable when the designed accuracy of spatial discretization is third order or higher. Unlike the standard SD method, the method examined here uses vector interpolating functions in the Raviart-Thomas (RT) spaces to construct continuous flux functions on reference elements. The spectral-difference Raviart-Thomas (SDRT) method was originally proposed by Balan et al. and implemented on triangular-element meshes for invisid flow only. Our present results demonstrated that the SDRT method is stable and high-order accurate in two dimensions (2D) for a number of test problems by using triangular-, quadrilateral-, and mixed-element meshes for both inviscid and viscous flows. A stability analysis is also performed for mixed elements. Furthermore, we found our current SDRT scheme is stable for fourth-order accurate spatial discretizations. However, a stable fifth-order SDRT scheme remains to be identified.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A parallel-in-time algorithm for variable step multistep methods

This paper presents a multigrid reduction in time (MGRIT) algorithm for achieving time parallelism using multistep backward difference formula (BDF) methods on variably-spaced temporal grids. This MGRIT approach transforms the linear multistep methods into single step methods applied to groups of time steps. Stability considerations are addressed through lowering of the order on coarse grids. The methods are presented for fixed and variable time step formulations. Furthermore, numerical results show moderate speedups for the heat equation as well as two IEEE power grid test problems characterized by nonlinear differential algebraic equations (DAE) of index 1. The performance of MGRIT with BDF is compared to MGRIT with Runge-Kutta for both the fixed and variable step methods of the same order.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of APCVD BSG and POCl 3 Codiffusion Process for Double-Side TOPCon Solar Cell Precursor Fabrication

This paper presents a commercially viable process for fabricating a high-quality double-side tunnel oxide passivating contact (DS-TOPCon) cell precursor using APCVD-deposited boron silicate glass and ex-situ POCl 3 diffusion in a single high-temperature step, eliminating the need for additional masking and diffusion processes. A two-tier temperature profile was developed, involving a pre-annealing at above 900°C in nitrogen (N2) ambient followed by POCl 3 diffusion at 840°C. We investigated the effect of varying pre-annealing temperatures, ranging from 875°C to 950°C, on the passivation quality and metal-Si contact properties of both n-TOPCon and p-TOPCon layers. The resultant DS-TOPCon cell precursor after silicon nitride (SiNX) passivation exhibited an excellent iV OC of close to 730 mV. In addition, a rapid asymmetric poly-Si thinning technique, developed in this work, enabled adjustment of the front n + poly-Si thickness while maintaining the rear p + poly-Si thickness. Two types of DS-TOPCon cell architectures can be fabricated: i) full-area thin (≈40nm) n-TOPCon layer on the front and ii) selective-area thick (≈200nm) n-TOPCon fingers underneath the metal grid. Device simulations suggest that full-area DS-TOPCon cell with 40 nm n + poly-Si and selective-area DS-TOPCon cell with 200 nm n + poly fingers on the front, fabricated from our current DS-TOPCon cell precursor, can achieve cell efficiencies of 22.1 and 23.5%, respectively. Detailed power loss analysis and device simulation reveal that further improvements in material and device parameters have potential to push the cell efficiencies of DS-TOPCon cell structure beyond 25%, making it a promising alternative to fabricate a high-efficiency next-generation solar cells at low cost.

14 SOLAR ENERGY↗
DOE OSTI2025

A Sequential Model Predictive and Deep Reinforcement Learning-Based Controller for Distribution System Outage Mitigation under Hurricane Events

This paper proposes a proactive outage mitigation framework for power distribution networks to withstand hurricane-induced disruptions. It leverages Model Predictive Control (MPC) to identify safe lines for proactive switching during hurricanes, minimizing the risk of cascading failures and voltage violations. The switching strategies optimized by MPC are sequentially integrated with a Deep Reinforcement Learning agent using the Advantage Actor-Critic algorithm, enabling dynamic line switching to maximize connected buses and minimize voltage violations in real time. Using a probabilistic hurricane model, the framework predicts line failures and adapts to varying conditions to enhance grid resilience. Simulations on the IEEE 123-bus system demonstrate its effectiveness in maintaining high connectivity and minimizing disruptions. Real-time testing with an RTDS confirms the practicality and reliability of the proposed approach.

Selim, Alaa [University of Connecticut]↗
DOE OSTI2020

Algorithms in Diffraction Profile Analysis

This chapter introduces several approaches to crystallographic structure refinement from diffraction profile data including the Rietveld method, global optimization methods, and Bayesian modeling. First, the Rietveld method and related nonlinear least squares approaches to analyzing diffraction profiles are briefly discussed. We then review the most common algorithms within global optimization as alternative methods of structure solution: grid search, simulated annealing, and genetic algorithms. As the Rietveld method is the standard tool for analyzing diffraction profile data, we catalogue its challenges and limitations. A Bayesian approach to diffraction profile analysis is presented as an alternative, along with several sampling algorithms including Gibbs sampling, variants of Metropolis sampling, Hamiltonian Monte Carlo, and approximate Bayesian computing. These sampling algorithms are applied to the fitting of a neutron diffraction profile from a National Institute of Standards and Technology silicon standard reference material, and their results are compared.

Singh, Susheela↗
DOE OSTI2019

A Cooperative Multi-Agent Deep Reinforcement Learning Framework for Real-Time Residential Load Scheduling

Internet-of-Things (IoT) enabled monitoring and control capabilities are enabling increasing numbers of household users with controllable loads to actively participate in smart grid energy management. Realizing an efficient real-time energy management system that takes advantage of these developments requires novel techniques for managing the increased complexity of the control action space in resolving multiple challenges such as the uncertainty in energy prices and renewable energy output along with the need to satisy physical grid constraints such as transformer capacity. Addressing these challenges, we develop a multi-household energy management framework for residential units connected to the same transformer and containing DERs such as PV, ESS and controllable loads. The goal of our framework is to schedule controllable household appliances and ESS such that the cost of procuring electricity from the utility over a horizon is minimized while physical grid constraints are satisfied at each scheduling step. Traditional energy management frameworks either perform global optimization to satisfy grid constraints but suffer from high computational complexity (for example Integer Program, Mixed Integer Programming frameworks and centralized reinforcement learning) or perform decentralized real-time energy management without satisfying global grid constraints (for example multi-agent reinforcement learning with no cooperation). In contrast, we propose a cooperative multiagent reinforcement learning (MARL) framework that i) operates in real-time, and ii) performs explicit collaboration to satisfy global grid constraints. The novelty in our framework is two fold. Firstly, our framework trains multiple independent learners (IL) for each household in parallel using historical data and performs real-time inferencing of control actions using the most recent system state. Secondly, our framework contains a low complexity knapsack based cooperation agent which combines the outputs of ILs to minimize cost while satisfying grid constraints. Simulation results show that our cooperative MARL approach achieves significant cost improvement over centralized reinforcement learning and day-ahead planning baselines. Moreover, our approach strictly satisfies physical constraints with no apriori knowledge of system dynamics while the baseline approaches have occasional violations. We also measure the training and inference time by ranging the number of households from 1 to 25. Results show that our cooperative MARL approach scales best among various approaches.

14 SOLAR ENERGY↗
DOE OSTI2022

Applied Mathematics Challenge: Simulation of Power Electronics in Future Power Grid

There has been a buzz around increased grid modernization for more than a decade. Grid modernization includes, but is not limited to, upgrades to the grid to enhance reliability, resilience, security, and access to clean energy sources. One significant change that has been happening in this context is the increased penetration of computing power and controlled devices like power electronics in the power grid. As this change happens, the operation and characteristics of the power grid are set to undergo a significant change. The power grid moves from an older electric machine dominated grid that used analog electronics for controls to a power electronics dominated grid that uses digital computing for controls. As this transition happens, there are significant problems of applied mathematics that will need to be resolved in power electronics and power grid. The problems include the ability to simulate in different timescales, while also leveraging the significantly improved computing capabilities available today. In this paper, the challenges related to simulation of power electronics are discussed and the challenge problems laid down that applied mathematics may help resolve in future.

Debnath, Suman↗
DOE OSTI2018

OpenRES

The OpenRES is an open-source library of intermittent energy resources developed in the JModelica.org platform, and provides capability for performing long-term power grid dynamic simulation with renewable ancillary services (e.g., load ramp, frequency and voltage regulation).

Qin, Yining↗
DOE OSTI2021

Assimilating Scanning Radar Data into Cloud-Scale Models

The aim of this project is to improve the initial conditions of large eddy simulations (LES) of deep convective storms by assimilating scanning radar reflectivity and radial velocity data as well as local environmental observations. Accomplishing this goal often results in more accurate numerical forecasts of clouds and precipitation than if the background initial meteorological conditions are solely relied upon for convection initiation and intensification, as is typical of most operational and research models. Conducted in a storm-scale LES framework, these more accurate numerical analyses can serve as tool to better understand and parameterize land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development of convection. Our work examined the sensitivity of the numerical representation of cloud-scale wind and microphysical structures to: i) the assimilation of radiosonde observations characterizing the near-storm inflow, ii) the assimilation of radar data of varying resolution for model simulations with grid spacings of 1-km and 0.25-km, and iii) to the assimilation of three-dimensional plan-projection-indicator (PPI) and range-height indicator (RHI) radar volumes. Few studies have explored deep convective storm analyses and forecasts produced by assimilation of radar observations to cloud-scale LES. Furthermore, to our knowledge, no storm-scale DA study has explored the value of assimilating high-resolution sector RHI volumes to determine if they can improve the retrieval of the vertical kinematic and microphysical storm structure aloft over the use of traditionally utilized PPI scans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Efficiently resolving the terrestrial-aquatic interface in E3SM with sub-grid methods to improve coastal simulations (Final Technical Report)

The broad goal of this project was to extend the capabilities of MPAS‐Ocean, the ocean component of DOE’s Energy Exascale Earth System Model (E3SM), to better resolve the hydrodynamics of the terrestrial‐aquatic interface without significantly increasing computing resource requirements. Specifically, our goal was to better resolve the small-scale features across the terrestrial‐aquatic interface which includes the low lying coastal floodplain and a dendritic network of hydraulically efficient channels connected to estuarine systems and back bays, barrier islands and high energy inlets. The processes driving flow across the terrestrial‐aquatic interface include upland and floodplain hydrology, tides, wind and atmospheric pressure (including coastally generated storm surge across continental shelves and within estuaries and bays), wind waves and water temperature and salinity gradients.

58 GEOSCIENCES↗
DOE OSTI2025

Hybrid Basis and Multi-Center Grid Method for Strong-Field Processes

We present a time-dependent framework that combines a hybrid basis, consisting of Gaussian-type orbitals (GTOs) and finite-element discrete-variable representation (FEDVR) functions, with a multicenter grid to simulate strong-field and attosecond dynamics in atoms and molecules. The method incorporates the construction of the orthonormal hybrid basis, the evaluation of electronic integrals, a unitary time-propagation scheme, and the extraction of optical and photoelectron observables. Its accuracy and robustness are benchmarked on one-electron systems such as atomic hydrogen and the dihydrogen cation (H$^+_2$) through comparisons with essentially-exact reference results for bound-state energies, high-harmonic generation spectra, photoionization cross sections, and photoelectron momentum distributions. This work establishes the groundwork for its integration with quantum-chemistry methods, which is already operational but will be detailed in future work, thereby enabling ab initio simulations of correlated polyatomic systems in intense ultrafast laser fields.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Modeling Sea Ice Effects for Wave Energy Resource Assessments

Wave-generated power has potential as a valuable coastal resource, but the wave climate needs to be mapped for feasibility before wave energy converters are installed. Numerical models are used for wave resource assessments to quantify the amount of available power and its seasonality. Alaska is the U.S. state with the longest coastline and has extensive wave resources, but it is affected by seasonal sea ice that dampens the wave energy and the full extent of this dampening is unknown. To accurately characterize the wave resource in regions that experience seasonal sea ice, coastal wave models must account for these effects. The aim of this study is to determine how the dampening effects of sea ice change wave energy resource assessments in the nearshore. Here, we show that by combining high-resolution sea ice imagery with a sea ice/wave dampening parameterization in an unstructured grid, the Simulating Waves Nearshore (SWAN) model improves wave height predictions and demonstrates the extent to which wave power decreases when sea ice is present. The sea ice parametrization decreases the bias and root mean square errors of wave height comparisons with two wave buoys and predicts a decrease in the wave power of up to 100 kW/m in areas around Prince William Sound, Alaska. The magnitude of the improvement of the model/buoy comparison depends on the coefficients used to parameterize the wave–ice interaction.

16 TIDAL AND WAVE POWER↗