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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Understanding the trade-offs of national municipal solid waste estimation methods for circular economy policy

Policies embracing circular economy concepts have taken hold in national legislation around the world. As the number of governments and organizations adopting circular economy policies increases, so does the need for accurate and timely measurement of material resource flows. Since many countries do not have access to centrally reported municipal solid waste (MSW) data, estimation and modeling are critical in evaluating circular economy policy effectiveness. The purpose of this paper is to examine three modeling approaches estimating national MSW data in the United States, including industry-based material flow analysis, waste-extended input-output modeling, and aggregated regional waste reporting. We establish five criteria to guide the analysis through the context of policy monitoring (data quality, flow totality, update frequency, sensitivity to disruption, and product granularity) and use these criteria to analyze and score each model. We then use a literature search to identify five, internationally-implemented options for circular economy policy and determine the data and modeling components that are most helpful in evaluating policy effectiveness. Finally, we provide a crosswalk of the model scores and policy needs to inform the suitability of model selection by policy type. We found that data quality and update frequency are identified as critical components for evaluating circular economy policies within the models evaluated, and can both be fulfilled by aggregated regional waste reporting. Flow totality, sensitivity to disruption, and product granularity requirements vary by both model and policy types. While none of the evaluated models satisfy the combination of requirements for any of the five policies, industry-based material flow analysis offers flow totality for extended producer responsibility, landfill bans, and recycling rate target policies that typically require it. Here, the waste-extended input-output model can provide disruption sensitivity and product granularity as needed for policies like minimum recycled content and market restrictions. Policy developers in areas where strong centralized data collection is not an option should design policy action(s) with modeling tradeoffs in mind, including the potential hybridization of modeling approaches that may provide the most accurate national MSW estimates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Power Hardware-In-the-Loop Hydropower and Ultracapacitor Hybrid Testbed

Widespread deployment of a proof-of-concept technological solution for power system performance improvement can be accelerated through onsite demonstration and pilot projects. Digital real-time simulation with power hardware-in-the-loop in controlled lab environment is the precursor to de-risk the timely and efficient execution of field demonstration and pilot projects. However, the detailed process of development, characterization, and calibration of such high fidelity simulation testbed needs to be documented in a reproducible and publicly accessible manner to leverage across broader research and development communities. This paper presents such a laboratory testbed to study black start capability of a run-of-river hydro + ultracapacitor hybrid system. Beside the high-fidelity model of the islanded grid with hydropower unit, the testbed includes the energy storage, grid following inverter, and grid emulator hardware. Several aspects such as storage characterization, control and various parameter settings necessary to capture frequency transients during load restoration are described in a comprehensive manner, which led to an effective and timely execution of black start field demonstration with an actual hydropower plant.

13 HYDRO ENERGY↗
NASA NTRS2011

In Situ Observations and Sampling of Volcanic Emissions with Unmanned Aircraft: A NASA/UCR Case Study at Turrialba Volcano, Costa Rica

Burgeoning new technology in the design and development of robotic aircraft-unmanned aerial vehicles (UAVs)-presents unprecedented opportunities for the volcanology community to observe, measure, and sample eruption plumes and drifting volcanic clouds in situ. While manned aircraft can sample dilute parts of such emissions, demonstrated hazards to air breathing, and most particularly turbine, engines preclude penetration of the zones of highest ash concentrations. Such areas within plumes are often of highest interest with respect to boundary conditions of applicable mass-loading retrieval models, as well as Lagrangian, Eulerian, and hybrid transport models used by hazard responders to predict plume trajectories, particularly in the context of airborne hazards. Before the 2010 Ejyafyallajokull eruption in Iceland, ICAO zero-ash-tolerance rules were typically followed, particularly for relatively uncrowded Pacific Rim airspace, and over North and South America, where often diversion of aircraft around ash plumes and clouds was practical. The 2010 eruption in Iceland radically changed the paradigm, in that critical airspace over continental Europe and the United Kingdom were summarily shut by local civil aviation authorities and EURO CONTROL. A strong desire emerged for better real-time knowledge of ash cloud characteristics, particularly ash concentrations, and especially for validation of orbital multispectral imaging. UAV platforms appear to provide a viable adjunct, if not a primary source, of such in situ data for volcanic plumes and drifting volcanic clouds from explosive eruptions, with prompt and comprehensive application to aviation safety and to the basic science of volcanology. Current work is underway in Costa Rica at Turrialba volcano by the authors, with the goal of developing and testing new small, economical UAV platforms, with miniaturized instrument payloads, within a volcanic plume. We are underway with bi-monthly deployments of tethered SO2-sondes and are in the planning stages for the deployment of the SIERRA UAV to our site in March 2013. We will be conducting in situ observations simultaneously with ASTER orbital multispectral TIR data acquisitions, in order to compare in situ measurements with estimates of SO2 mass loading and dispersion derived from ASTER data. Though small UAVs are now being considered for use in active volcanic areas for in situ sampling of emissions (e.g., efforts by our group, and by our colleagues at the INGV in Italy and the Applied Science University in Dusseldorf, Germany, and others in the United Kingdom and Iceland), and also for remote sensing, much more needs to be done in the way of instrument development, and in developing small UAVs for both low altitude (tropospheric) and high altitude (stratospheric) applications. In particular, the development of all weather and day/night operational flight capabilities in close proximity to hazardous topography is crucial to a truly responsive volcano in situ measurement system. Finally, it is imperative that national civil aviation authorities recognize the unique benefits of such platforms. It is important that authorities understand that severely restricting or not deploying such tools in airspace over restless volcanoes or within eruption plumes, ostensibly because of the perceived (small) risk that such unmanned aircraft pose to manned air operations, itself poses a bigger transcendental risk to proximal populations and particularly to the aviation community, itself.

Volcanic Ash Advisory Centers (VAACs)↗
NASA NTRSDate not supplied

Predicting Maximum Thermal Response in a Li-ion Cell as a Thermal Runaway Predictor for a UAV Fight

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances to spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Moreover, a thermal runaway can propagate from a single "failed" cell to a whole battery pack, resulting in a more serious event. The commercial and automotive sectors need to mitigate thermal runaway events. An electric aircraft (or air taxi) has no alternative in the event of a failure, so preventing such events is paramount. Currently, battery prognostics algorithms only predict the state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle), but do not predict the maximum temperature during a flight or the likelihood of thermal runaway. Our current work focuses on adding the thermal model (based on Birkl's OCV model, in Ref. [1]) to the existing hybrid electrochemical model (single-particle model with lumped parameters as in Ref. [2]) to determine end-of-discharge, end-of-life, and maximum temperature during discharge.

Thermal runaway↗
NASA NTRS2024

Revolutionary Vertical Lift Technology Lift + Cruise Concept Vehicle Powertrain Dynamic Model

The Lift + Cruise (L+C) is one of several Revolutionary Vertical Lift Technology (RVLT) concept aircraft identified by NASA to investigate Urban Air Mobility (UAM) requirements. This six-passenger vehicle has a conventional tube-and-wing configuration, but with eight lift rotors (four on each wing) and a pusher rotor on the tail. This arrangement allows it to take off and land vertically as well as fly like an airplane. This report presents a novel dynamic model of the L+C powertrain built using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) and the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), both of which are NASA-developed toolboxes, and together are capable of modeling hybrid-electric powertrain architectures. This research effort models the complex behavior of the powertrain at the mechanical timescale (15 to 20 ms). The model consists of a turboshaft engine, generator, direct current bus, energy storage management system, gearboxes, and electric propulsor systems coupled together to create a dynamic model that mimics the operation of the multidomain powertrain. Two control algorithms, designed to equalize the power consumed to the power produced, are compared to assess their practicality: (1) independent proportional-integral (PI) control and (2) linear quadratic integral regulator (LQIR) control. Simulation results highlight the key features present in the model and demonstrate its operation under the two control schemes.

revolutionary vertical lift technology↗
DOE OSTI2025

Standard Library Grid-Forming Hybrid Control Inverter-Based Resource Model Specification (REGFM_C1)

This document describes a standard library grid-forming (GFM) hybrid control inverter-based resource (IBR) model. The GFM hybrid control approach implements both a typical GFM control and a typical grid-following (GFL) control inside one single inverter simultaneously, so that it can take advantage of both methods without comprising the benefits of a typical GFM. The initial version of model specification was jointly developed by Pacific Northwest National Laboratory (PNNL), Tesla Energy, and EPRI, and it was revised multiple times later to incorporate suggestions from WECC MVS members. Tesla Energy provided main control blocks to support the development of this model specification. This standard library model is developed to help the utility industry better understand the GFM technology. The model could be used to represent equipment for long-term planning studies where vendor-specific models are not available. As equipment matures and improves, standard library models will be updated to capture the new functionalities of GFMs. It is not intended that these models will always remain representative of all future GFM technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Joint Resource Modeling and Assessment for Hybrid Distributed Solar and Wind Systems

The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.

joint modeling↗
DOE OSTI2026

A hybrid CNN-LSTM surrogate model for hyper-resolution spatiotemporal flood forecasting in Norfolk, Virginia

Study region: Norfolk, Virginia, United States Study focus: Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features. New hydrologic insights for the region: The hybrid CNN-LSTM model was trained using the physics-based hydrodynamic model simulations obtained from the Two-dimensional Unsteady FLOW (TUFLOW) model for Norfolk, Virginia, and achieved high predictive accuracy across diverse flood-prone areas. The reduced computational time from four to six hours using TUFLOW to 3.2 min per event using CNN-LSTM enables rapid flood inundation mapping and early warning applications. The model effectively captured both spatial flood extents and their temporal evolution across different flooding scenarios, providing forecasts at a 2.5-m spatial resolution and 15-min temporal resolution and a one-hour-ahead prediction horizon. While challenges remain in terms of transferability to new regions and real-time data assimilation, this approach demonstrates strong potential for supporting operational flood risk management in coastal urban environments.

Coastal urban flooding↗
NASA NTRS2007

Modeling and Analysis of Facility Systems for A Hybrid Materials Test Program

Analytic modeling and analysis processes employed at NASA-SSC in rocket propulsion systems testing are discussed in this paper with application to test facility propellant supply system design, activation and test of a hybrid rocket motor provided. This paper discusses the analytic model employed, its utilization across project phases and reviews performance results.

Congiardo, Jared F.↗
DOE OSTI2020

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer paral- lelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation.

Wang, Tianqi↗
NASA NTRS1998

Application of Model Based Parameter Estimation for Fast Frequency Response Calculations of Input Characteristics of Cavity-Backed Aperture Antennas Using Hybrid FEM/MoM Technique

Model Based Parameter Estimation (MBPE) is presented in conjunction with the hybrid Finite Element Method (FEM)/Method of Moments (MoM) technique for fast computation of the input characteristics of cavity-backed aperture antennas over a frequency range. The hybrid FENI/MoM technique is used to form an integro-partial- differential equation to compute the electric field distribution of a cavity-backed aperture antenna. In MBPE, the electric field is expanded in a rational function of two polynomials. The coefficients of the rational function are obtained using the frequency derivatives of the integro-partial-differential equation formed by the hybrid FEM/ MoM technique. Using the rational function approximation, the electric field is obtained over a frequency range. Using the electric field at different frequencies, the input characteristics of the antenna are obtained over a wide frequency range. Numerical results for an open coaxial line, probe-fed coaxial cavity and cavity-backed microstrip patch antennas are presented. Good agreement between MBPE and the solutions over individual frequencies is observed.

Reddy C. J.↗
DOE OSTI2021

An analytic hybrid halo + perturbation theory model for small-scale correlators: baryons, halos, and galaxies

In this work, we update Halo Zeldovich Perturbation Theory (HZPT, [1]), an analytic model for the two-point statistics of dark matter, to describe halo and galaxy clustering, and galaxy-matter cross-correlation on nonlinear scales. The model correcting Zeldovich has an analytic Fourier transform, and therefore is valid in both configuration space and Fourier space. The model is accurate at the 2%-level or less for P_mm (k< 1 h/Mpc), P_hm (k< 1 h/Mpc), P_hh (k< 2 h/Mpc), P gm (k< 1 h/Mpc), P gg (k< 1 h/Mpc), ξ mm (r> 1 Mpc/h), ξ hm (r> 2 Mpc/h), ξ hh (r> 2 Mpc/h), ξ gm (r> 1 Mpc/h), ξ gg (r> 2 Mpc/h), for LRG-like mock galaxies. We show that the HZPT model for matter correlators can account for the effects of a wide range of baryonic feedback models and provide two extended dark matter models which are of 1% (3%) accuracy for k < 10 (8) h/Mpc. We explicitly model the non-perturbative features of halo exclusion for the halo-halo and galaxy-galaxy correlators, as well as the presence of satellites for galaxy-matter and galaxy-galaxy correlation functions. We perform density estimation using N-body simulations and a wide range of HOD galaxy mocks to obtain correlations of model parameters with the cosmological parameters Ω m and σ 8 . HZPT can provide a fast, interpretable, and analytic model for combined-probe analyses of redshift surveys using scales well into the non-linear regime.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

NUMERICAL INVESTIGATION OF FUEL PROPERTY EFFECTS ON MIXED-MODE COMBUSTION IN A SPARK-IGNITION ENGINE

In the present study, mixed-mode combustion of an E30 fuel in a direct-injection spark-ignition engine is numerically investigated at a fuel-lean operating condition using multidimensional computational fluid dynamics (CFD). A fuel surrogate matching Research Octane Number (RON) and Motor Octane Number (MON) of E30 is first developed using neural network based non-linear regression model. To enable efficient 3D engine simulations, a 164-species skeletal reaction mechanism incorporating NO, chemistry is reducedfrom a detailed chemical kinetic model. A hybrid approach that incorporates the G-equation model for tracking turbulentflame front, and the multi-zone well-stirred reactor model for predicting auto-ignition in the end gas, is employed to account for turbulent combustion interactions in the engine cylinder. Predicted in-cylinder pressure and heat release rate traces agree well with experimental measurements. The proposed modelling approach also captures moderated cyclic variability. Two different types of combustion cycles, corresponding to purely deflagrative and mixed-mode combustion, are observed. In contrast to the purely deflagrative cycles, mixed-mode combustion cycles feature early flame propagation followed by end-gas auto-ignition, leading to two distinctive peaks in heat release rate traces. The positive correlation between mixed-mode combustion cycles and early flame propagation is well captured by simulations. With the validated numerical setup, effects of NOx chemistry on mixed-mode combustion predictions are investigated. NOx chemistry is found to promote auto-ignition through residual gas recirculation, while the deflagrative flame propagation phase remains largely unaffected. Local sensitivity analysis is then performed to understand effects of physical and chemical properties of the fuel, i.e., heat of evaporation (HoV) and laminar flame speed (S-L). An increased HoV tends to suppress end-gas auto-ignition due to increased vaporization cooling, while the impact of HoV on flame propagation is insignificant. In contrast, an increased SL is found to significantly promote both flame propagation and auto-ignition. The promoting effect of SL on auto-ignition is not a direct chemical effect; it is rather caused by an advancement of the combustion phasing, which increases compression heating of the end gas.

detailed chemical kinetics↗
NASA NTRS1993

Estimation of surface heat and moisture fluxes over a prairie grassland. III - Design of a hybrid physical/remote sensing biosphere model

The design and formulation of an experimental biosphere model (Ex-BATS), based on the biosphere-atmosphere transfer scheme (BATS) of Dickinson (1983, 1984) and Dickinson et al. (1986) are described. The Ex-BATS was designed to incorporate both in situ measurements and satellite parameterizations of certain canopy variables which are slowly varying in the course of a growing season. The new model was validated using measurements obtained during the May 28 to August 21 First International Satellite and Surface Climatology Project Field Experiment, showing that the Ex-BATS model reproduced closely the diurnal behavior of the surface fluxes.

Smith, Eric A.↗