Search NASA⌕ Search

SEARCH · Search NASA

Results for “multi frequency”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multi-scenario Extreme Weather Simulator application to heat waves: Ko’olauloa community resilience hub

Heat waves are increasing in severity, duration, and frequency. The Multi-Scenario Extreme Weather Simulator (MEWS) models this using historical data, climate model outputs, and heat wave multipliers. In this study, MEWS is applied for planning of a community resilience hub in Hau'ula, Hawaii. The hub will have normal operations and resilience operations modes. Both these modes were modeled using EnergyPlus. The resilience operations mode includes cutting off air conditioning for many spaces to decrease power requirements during emergencies. Results were simulated for 300 future weather files generated by MEWS for 2020, 2040, 2060, and 2080. Shared socioeconomic pathways 2-4.5, 3-7.0 and 5-8.5 were used. The resilience operations mode results show two to six times increase of hours of exceedance beyond 32.2 °C from present conditions, depending on climate scenario and future year. The resulting decrease in thermal resilience enables an average decrease of energy use intensity of 26% with little sensitivity to climate change. The decreased thermal resilience predicted in the future is undesirable, but was not severe enough to require a more energy-intensive resilience mode. Instead, planning is needed to assure vulnerable individuals are given prioritized access to air-conditioned parts of the hub if worst-case heat waves occur.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2016

Development of Multi-Physics Dynamics Models for High-Frequency Large-Amplitude Structural Response Simulation

An analytic approach is demonstrated to reveal potential pyroshock-driven dynamic effects causing power losses in the Thermo-Electric (TE) module bars of the Mars Science Laboratory (MSL) Multi-Mission Radioisotope Thermoelectric Generator (MMRTG). This study utilizes high-fidelity finite element analysis with SIERRA/PRESTO codes to estimate wave propagation effects due to large-amplitude suddenly-applied pyro shock loads in the MMRTG. A high fidelity model of the TE module bar was created with approximately 30 million degrees-of-freedom (DOF). First, a quasi-static preload was applied on top of the TE module bar, then transient tri-axial acceleration inputs were simultaneously applied on the preloaded module. The applied input acceleration signals were measured during MMRTG shock qualification tests performed at the Jet Propulsion Laboratory. An explicit finite element solver in the SIERRA/PRESTO computational environment, along with a 3000 processor parallel super -computing framework at NASA AMES, was used for the simulation. The simulation results were investigated both qualitatively and quantitatively. The predicted shock wave propagation results provide detailed structural responses throughout the TE module bar, and key insights into the dynamic response (i.e., loads, displacements, accelerations) of critical internal spring/piston compression systems, TE materials, and internal component interfaces in the MMRTG TE module bar. They also provide confidence on the viability of this high-fidelity modeling scheme to accurately predict shock wave propagation patterns within complex structures. This analytic approach is envisioned for modeling shock sensitive hardware susceptible to intense shock environments positioned near shock separation devices in modern space vehicles and systems.

Shock Waves↗
DOE OSTI2024

A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves

The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.

42 ENGINEERING↗
DOE OSTI2022

A Comprehensive Approach towards Multi-Objective EMI Filter Design Optimization in High-Frequency SiC-Based Motor Drives

With rapid penetration of high-frequency, highdensity power electronics into the industry, controlling the system generated EMI becomes one of the major design challenges. Designing practical optimized EMI filters require simultaneous consideration of multiple aspects - including electromagnetic couplings, magnetic materials, practical component parasitics and their impacts at high-frequencies, and also the overall manufacturing costs. The multi-dimensional nature of the problem often results in overcompensated designs that hurt the system efficiency and power density. This paper focuses on a comprehensive design platform (developed in MATLAB) towards designing high-density, optimized, highly efficient EMI filters by analyzing the generated noise spectrum and evaluating a vast array of potential solutions before outputting an optimized filter solution. The proposed tool includes database-based component selection, optimal topology selection, multistage filter design, overall optimization for volume, mass, cost and total loss. The proposed filter has been thoroughly evaluated in a PSIM simulation environment emulating the hardware in CRD300DA12EXM3, a 300kW three-phase inverter from Wolfspeed with an RL load representing a motor drive application. Furthermore, a 20kHz switching frequency is chosen and impacts of the motor high-frequency characteristics on the generated EMI noise spectrum of the overall system have been analyzed.

42 ENGINEERING↗
NASA NTRSDate not supplied

Towards Simultaneous Multi-Parameter Measurements Using Burst-Mode Frequency-Scanning Planar Laser-Induced Fluorescence

While planar laser-induced fluorescence (PLIF) can provide valuable flow visualization, there are many challenges in extracting quantitative measurements from the fluorescence intensity. To overcome the multi-parameter dependence of the signal intensity, frequency scanning planar laser-induced fluorescence (FS-PLIF) provides the potential for 2D temperature, pressure, and velocity measurements by spectrally resolving the excitation spectrum of one or more fluorescence transitions to extract the Doppler shift, Doppler- and collisional-broadening of absorption lines, and the relative signal intensities of multiple transitions. This work aims to characterize a burst-mode FS-PLIF system consisting of a Spectral Energies, LLC burst-mode laser coupled with a Spectral Energies, LLC optical parametric oscillator using nitric oxide (NO) static pressure cell measurements. The effect of the OPO cavity on intensity fluctuations during frequency-scanning is investigated. A NO PLIF model is implemented to investigate the feasibility of property measurements using single- and two-NO-line scans.

burst-mode laser↗
DOE OSTI2021

Frequency Response and Congestion Management using Multi-Terminal HVDC Overlay

Continental level power system interconnections using a High Voltage direct current (HVDC) transmission lines have been considered to bring economic benefits such as interregional power exchange. This paper describes the modeling process and studies the technical benefits of having multiple HVDC lines, including a grid of HVDC lines (macrogrid) configuration, connecting the North American Eastern and Western electric power interconnections. The models developed provide steady state and stability analysis for multiple HVDC overlay topologies and provide technical benefit analysis in terms of frequency response and congestions management. The paper also provides a comparison of different HVDC topologies performance and their grid support in case of major disturbances

Tbaileh, Ahmad↗
NASA NTRS1989

The high-overtone p-mode spectrum of the rapidly oscillating Ap star HR 1217 (HD 24712) - Results of a frequency analysis of 324 hr of multi-site photometric observations obtained during a 46-d time-span in 1986

The present high-speed photometry for the rapidly oscillating Ap star HR 1217 indicates that the 6-min light variations are amplitude-modulated, in phase with the magnetic variations. Amplitude and magnetic maxima coincide, while the mean light minimum occurs at a perhaps significantly different time. It is established that HR 1217 is an oblique rotator with a centered dipole magnetic field, as well as an oblique pulsator whose pulsation and magnetic axes are aligned. There are six principal frequencies of pulsation; the secondary frequencies indicate that the principal ones are amplitude-modulated on a time-scale of months.

Kurtz, D. W.↗
DOE OSTI2021

Quantifying Dam-Induced Fluctuations in Streamflow Frequencies Across the Colorado River Basin

Periodic fluctuations in natural streamflow are a major driver of river ecosystem dynamics and water resource management. However, most U.S. rivers are impacted both by long-term hydroclimatic trends and dams that alter flow variability. Despite these impacts, it remains largely unexplored how dams affect the dominant frequencies of natural streamflow over a highly regulated river network. We investigated the entire Colorado River Basin (CRB) to understand how the annual (10–14 months) and multi-annual (24–60 months) frequencies in natural flow regimes have been progressively altered by dams. Given the significant alteration over the CRB, we captured changes in streamflow frequencies between naturalized and observed monthly flows via wavelet analysis. Based on the similarity of changes in streamflow frequencies (annual and multi-annual) over the last 30 years, sections of the riverine network were classified into four groups. The annual frequency was relatively well preserved downstream of Hoover Dam, while showing a systematic trend of alteration downstream of Glen Canyon Dam until Hoover Dam. Meanwhile, the multi-annual frequency component was highly altered for the entire Lower Colorado main stem (i.e., downstream of Glen Canyon). We also identified dams with significant impacts on streamflow frequency by comparing wavelet coherence estimates. This study advances the notion that dams fundamentally alter river flow regimes across multiple frequencies and with varying amplitudes over time and space, with alteration propagating – or being dampened – by both hydroclimatic fluctuations and water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-metallic conduction cooled superconducting radio-frequency cavity with high thermal stability

Superconducting radio-frequency cavities are commonly used in modern particle accelerators for applied and fundamental research. Such cavities are typically made of high-purity, bulk Nb and with cooling by a liquid helium bath at a temperature of ~2 K. The size, cost and complexity of operating a particle accelerator with a liquid helium refrigerator make the current cavity technology not favorable for use in industrial-type accelerators. We have developed a multi-metallic 1.495 GHz elliptical cavity conductively cooled by a cryocooler. The cavity has a ~2 µm thick layer of Nb 3 Sn on the inner surface, exposed to the rf field, deposited on a ~3 mm thick bulk Nb shell and a bulk Cu shell, of thickness $\geqslant\! 5$ mm deposited on the outer surface by electroplating. A bolt-on Cu plate 1.27 cm thick was used to thermally connect the cavity equator to the second stage of a Gifford-McMahon cryocooler with a nominal capacity of 2 W at 4.2 K. The cavity was tested initially in liquid helium at 4.3 K and reached a peak surface magnetic field of ~36 mT with a quality factor of 2 × 10 9 . The cavity cooled by the cryocooler achieved a peak surface magnetic field of ~29 mT, equivalent to an accelerating gradient of 6.5 MV m –1 . The conduction-cooled cavity could be operated in continuous-wave with as high as 5 W dissipation in the cavity for 1 h without any thermal breakdown, because of the Cu outer layer with high thermal conductivity. This result represents a paradigm shift in the technology of superconducting accelerator cavities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2019

Interference Mitigation Using Cyclic Autocorrelation and Multi-Objective Optimization

Radio frequency interference on space-to-ground communications links can degrade performance and disrupt the transfer of critical data. These interference events become increasingly likely as more users enter the spectrum, due in part to shared spectrum allocations and scheduling conflicts. If this interference could be detected and mitigated by an automated system, then link performance and reliability in these scenarios could be improved. This report describes the implementation and evaluation of an automated interference mitigation system that provides this functionality. The system uses Cyclic Autocorrelation (CAC) signal processing techniques to monitor the spectrum and detect interfering signals, and it applies a multi-objective optimization approach to mitigate interference by changing link parameters to continuously optimize the link. The implementation was evaluated to characterize its signal detection capabilities for various link qualities and to compare its link management performance to Adaptive Coding and Modulation (ACM) and Constant Coding and Modulation (CCM) when in the presence of randomized interference. In the latter evaluation, the interference mitigation system achieved the highest average throughput in each tested scenario. With these results, the proposed solution provides the groundwork for further automated link management capabilities and continued investigation into interference mitigation approaches.

Interference mitigation↗
DOE OSTI2024

Frequency Support From Electric Vehicles for Advancing Renewable Energy Integration

The integration of renewable energy resources (RERs) in the modern power grid is increasing rapidly because of aggressive decarbonization goals, lower costs, and increased government investment. However, higher penetrations of inverter-based generation can lead to frequency stability issues because of reduced system inertia. This paper develops a framework for quantifying the contribution of electric vehicles (EVs) toward providing frequency support to the grid and thus increasing the penetration limit of renewable energy resources (RERs). EVs are considered to provide both inertial response and primary frequency response support to the grid. A stochastic approach incorporating the uncertainties associated with the behavior of EVs is developed to derive the discharge limit of EV aggregators. A multi-machine system frequency response (MM-SFR) model is developed, which incorporates the dynamic virtual inertia and droop coefficients of EV aggregators derived from the EV control modules. Frequency security constraints are developed from this MM-SFR model, which, along with the converter voltage security and low voltage ride-through constraints, are integrated within a nonlinear optimization framework to determine the RER integration limit. Here, the efficacy of the proposed approach is validated using the RTS-GMLC test system.

25 ENERGY STORAGE↗
DOE OSTI2021

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they still struggle in cases where the target functions to be approximated exhibit high-frequency or multi-scale features. Here in this work we investigate this limitation through the lens of Neural Tangent Kernel (NTK) theory and elucidate how PINNs are biased towards learning functions along the dominant eigen-directions of their limiting NTK. Using this observation, we construct novel architectures that employ spatio-temporal and multi-scale random Fourier features, and justify how such coordinate embedding layers can lead to robust and accurate PINN models. Numerical examples are presented for several challenging cases where conventional PINN models fail, including wave propagation and reaction–diffusion dynamics, illustrating how the proposed methods can be used to effectively tackle both forward and inverse problems involving partial differential equations with multi-scale behavior.

42 ENGINEERING↗
DOE OSTI2024

Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification

This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.

LSTM-MPC↗
NASA NTRS2001

Environmental Characteristics of Convective Systems During TRMM-LBA

In this paper, data collected from 51 days of continual upper atmospheric soundings and TOGA radar at ABRACOS Hill during the TRMM-LBA experiment are used to describe the mean thermodynamic and kinematic airmass properties of wet season convection over Rondonia, Brazil. Distinct multi-day easterly and westerly lower tropospheric wind regimes occurred during the campaign with contrasting airmass characteristics. Westerly wind periods featured modest CAPE (1000 J/kg), moist conditions (>90% RH) extending through 700 mb and shallow (900 mb) speed shear on the order of 10(exp -4)/s. This combination of characteristics promoted convective systems that featured a relatively large fraction of stratiform rainfall and weak convection nearly devoid of lightning. The environment is very similar to the general airmass conditions experienced during the Darwin, Australia monsoon convective regime. In contrast, easterly regime convective systems were more strongly electrified and featured larger convective rain rates and reduced stratiform rainfall fraction. These systems formed in an environment with significantly larger CAPE (1500 J/kg), drier lower and middle level humidities (< 80% RH) and a wind shear layer that was both stronger (10(exp -3)/s) and deeper (700 mb). The larger CAPE resulted from strong insolation under relatively cloud-free skies (owing to reduced column humidity) and was also weakly capped in the lowest 1-2 km, thus contributing to a more explosive growth of convection. The time series of low- and mid-level averaged humidity exhibited marked variability between westerly and easterly regimes and was characterized by low frequency (i.e., multi-day to weekly) oscillations. The synoptic scale origins of these moisture fluctuations are examined, which include the effects of variable low-level airmass trajectories and upper-level, westward migrating cyclonic vortices. The results reported herein provide an environmental context for ongoing dual Doppler analyses and numerical modeling case studies of individual TRMM-LBA convective systems.

Halverson, Jeffrey B.↗