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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Snowmelt on the Greenland Ice Sheet as Derived From Passive Microwave Satellite Data

The melt extent of the snow on the Greenland ice sheet is of considerable importance to the ice sheet's mass and energy balance, as well as Arctic and global climates. By comparing passive microwave satellite data to field observations, variations in melt extent have been detected by establishing melt thresholds in the cross-polarized gradient ratio (XPGR). The XPGR, defined as the normalized difference between the 19-GHz horizontal channel and the 37-GHz vertical channel of the Special Sensor Microwave/Imager (SSM/I), exploits the different effects of snow wetness on different frequencies and polarizations and establishes a distinct melt signal. Using this XPGR melt signal, seasonal and interannual variations in snowmelt extent of the ice sheet are studied. The melt is found to be most extensive on the western side of the ice sheet and peaks in late July. Moreover, there is a notable increasing trend in melt area between the years 1979 and 1991 of 4.4% per year, which came to an abrupt halt in 1992 after the eruption of Mt. Pinatubo. A similar trend is observed in the temperatures at six coastal stations. The relationship between the warming trend and increasing melt trend between 1979 and 1991 suggests that a 1 C temperature rise corresponds to an increase in melt area of 73000 sq km, which in general exceeds one standard deviation of the natural melt area variability.

Abdalati, Waleed↗
NASA NTRS1994

Pre-Departure Clearance (PDC): An Analysis of Aviation Safety Reporting System Reports Concerning PDC Related Errors

Airlines operating in the United States are required to operate under instrument flight rules (EFR). Typically, a clearance is issued via voice transmission from clearance delivery at the departing airport. In 1990, the Federal Aviation Administration (FAA) began deployment of the Pre-Departure Clearance (PDC) system at 30 U.S. airports. The PDC system utilizes aeronautical datalink and Aircraft Communication and Reporting System (ACARS) to transmit departure clearances directly to the pilot. An objective of the PDC system is to provide an immediate reduction in voice congestion over the clearance delivery frequency. Participating airports report that this objective has been met. However, preliminary analysis of 42 Aviation Safety Reporting System (ASRS) reports has revealed problems in PDC procedures and formatting which have caused errors in the proper execution of the clearance. It must be acknowledged that this technology, along with other advancements on the flightdeck, is adding more responsibility to the crew and increasing the opportunity for error. The present study uses these findings as a basis for further coding and analysis of an additional 82 reports obtained from an ASRS database search. These reports indicate that clearances are often amended or exceptions are added in order to accommodate local ATC facilities. However, the onboard ACARS is limited in its ability to emphasize or highlight these changes which has resulted in altitude and heading deviations along with increases in ATC workload. Furthermore, few participating airports require any type of PDC receipt confirmation. In fact, 35% of all ASRS reports dealing with PDC's include failure to acquire the PDC at all. Consequently, this study examines pilots' suggestions contained in ASRS reports in order to develop recommendations to airlines and ATC facilities to help reduce the amount of incidents that occur.

Montalyo, Michael L.↗
NASA NTRS2011

The Primordial Inflation Explorer (PIXIE)

The Primordial Inflation Explorer is an Explorer-class mission to measure the gravity-wave signature of primordial inflation through its distinctive imprint on the linear polarization of the cosmic microwave background. PIXIE uses an innovative optical design to achieve background-limited sensitivity in 400 spectral channels spanning 2.5 decades in frequency from 30 GHz to 6 THz (1 cm to 50 micron wavelength). Multi-moded non-imaging optics feed a polarizing Fourier Transform Spectrometer to produce a set of interference fringes, proportional to the difference spectrum between orthogonal linear polarizations from the two input beams. The differential design and multiple signal modulations spanning 11 orders of magnitude in time combine to reduce the instrumental signature and confusion from unpolarized sources to negligible levels. PIXIE will map the full sky in Stokes I, Q, and U parameters with angular resolution 2.6 deg and sensitivity 0.2 uK per 1 deg square pixel. The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r <10(exp -3) at 5 standard deviations. In addition, the rich PIXIE data will constrain physical processes ranging from Big Bang cosmology to the nature of the first stars to the physical conditions within the interstellar medium of the Galaxy. We describe the PIXIE instrument and mission architecture needed to detect the signature of an inflationary epoch in the early universe using only 4 semiconductor bolometers.

Kogut, Alan J.↗
NASA NTRS1959

Flutter Tests of Some Simple Models at a Mach Number of 7.2 in Helium Flow

Results of hypersonic flutter tests on some simple models are presented. The models had rectangular plan forms of panel aspect ratio 1.0, no sweepback, and bending-to-torsion frequency ratios of about 1/3. Two airfoil sections were included in the tests; double wedges of 5-, 10-, and 15-percent thickness and flat plates with straight, parallel sides and beveled leading and trailing edges. The models were supported by a cantilevered shaft. The double-wedge wings were tested in helium at a Mach number of 7.2. An effect of airfoil thickness on flutter speed was found, thicker wings requiring more stiffness to avoid flutter. A few tests in air at a Mach number of 6.9 showed the same thickness effect and also indicated that tests in helium would predict conservative flutter boundaries in air. The data in air and helium seemed to be correlated by piston-theory calculations. Piston-theory calculations agreed well with experiment for the thinner models but began to deviate as the thickness parameter MT approached and exceeded 1.0. A few tests on flat-plate models with various elastic-axis locations were made. Piston-theory calculations would not satisfactorily predict the flutter of these models, probably because of their blunt leading edges.

Morgan, Homer G.↗
DOE OSTI2024

Dynamic Wind Loading on CSP Collectors Caused by Turbulent Wind Fluctuations: Insights from a 2-Year Field Campaign

Concentrating Solar Power (CSP) is a promising solar technology for electricity generation with thermal energy storage and with the additional benefit of industrial heat production. Wind loading on CSP collector structures, such as parabolic troughs or heliostats, is one of the primary drivers of their structural design costs. In particular, dynamic wind loading is a major source of uncertainty in the collector design process, which heavily relies on wind tunnel testing. In the field, the turbulent nature of the incoming wind creates fluctuating loads (support structure loads and resulting mirror deflections) on the collectors, with impacts on fatigue lifetime and optical performance. As is well known, wind tunnel tests cannot entirely reproduce the complex turbulent wind conditions typically observed at full-scale plants. To shed light on this topic, NREL initiated a field campaign at the operational Nevada Solar One (NSO) powerplant that uses parabolic troughs as solar collectors. The aim of the project is a detailed characterization of prevailing wind and turbulence conditions and resulting operational loads on parabolic troughs. We use the published 2-year dataset of high-resolution combined wind and structural loads measurements [1] to characterize the dynamic structural wind response. For quantifying dynamic wind loading, we apply the concept of admittance functions, which are spectral transfer functions that couple the turbulent wind to resulting structural loads (aerodynamic admittance), and to the structural response (mechanical admittance). In practice, aerodynamic admittance describes which turbulent eddy sizes are effective in creating structural loads. The mechanical admittance describes in which frequency ranges these loads are reinforced or dampened by the structure. While these functions are an established concept in civil engineering, their recent application to a single full-scale heliostat [2] proved their broader applicability to CSP collectors. Here, we present a characterization of admittance functions for full-scale parabolic trough collectors and show how wind characteristics (mean wind speed and direction, turbulent kinetic energy, turbulent length scales), the sun-tracking trough angle, and row position alter the admittance functions. Further, we study to which extent the admittance functions are universal for a specific trough geometry and how our findings compare to reported heliostat results. References [1] https://data.openei.org/submissions/5938. [2] Blume, K., Roger, M., and Pitz-Paal, R. 2023b. "Simplified analytical model to describe wind loads and wind-induced tracking deviations of heliostats." Solar Energy, 256, 96-109. https://doi.org/10.1016/j.solener.2023.03.055.

admittance functions↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 A to a minimum safe operation of 300 A, or 10%. Because the current – voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical wind profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1 Hz frequency. For more details on the statistical analysis process, see the presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .

08 HYDROGEN↗
NASA NTRS1998

Tropospheric Wind Profile Measurements with a Direct Detection Doppler Lidar

Research has established the importance of global tropospheric wind measurements for large scale improvements in numerical weather prediction. In addition, global wind measurements provide data that are fundamental to the understanding and prediction of global climate change. These tasks are closely linked with the goals of the NASA Earth Science Enterprise and Global Climate Change programs. NASA Goddard has been actively involved in the development of direct detection Doppler lidar methods and technologies to meet the wind observing needs of the atmospheric science community. In this paper we describe a recently developed prototype wind lidar system using a direct detection Doppler technique for measuring wind profiles from the surface through the troposphere. This system uses a pulsed ND:YAG laser operating at 1064 nm as the transmitter. The laser pulse is directed to the atmosphere using a 40 cm diameter scan mirror. The portion of the laser energy backscattered from aerosols and molecules is collected by a 40 cm diameter telescope and coupled via fiber optics into the Doppler receiver. Single photon counting APD's are used to detect the atmospheric backscattered signal. The principle element of the receiver is a dual bandpass tunable Fabry Perot etalon which analyzes the Doppler shift of the incoming laser signal using the double edge technique. The double edge technique uses two high resolution optical filters having bandpasses offset relative to one another such that the 'edge' of the first filter's transmission function crosses that of the second at the half power point. The outgoing laser frequency is located approximately at the crossover point. Due to the opposite going slopes of the edges, a Doppler shift in the atmospheric backscattered laser frequency produces a positive change in signal for one filter and a negative change in the second filter. Taking the ratio of the two edge channel signals yields a result which is directly proportional to the component of the wind along the line-of-sight of the laser. Measuring the radial wind in several directions provides sufficient information to determine the true wind speed and direction. The lidar has operated from our laboratory at Goddard since June, 1997. Wind profiles have been obtained to altitudes of 12 km with a vertical resolution of 330 in. Vector wind data are obtained by rotating the scan mirror to measure line-of-sight wind profiles for at least two azimuth angles at an elevation angle of 45 degrees. The precision of the data as determined from the standard deviation of multiple independent lidar profiles is in the range of 1 to 3 m/sec up to 10 km. Good agreement is obtained when the lidar data are compared with the upper air rawinsonde soundings taken at Dulles airport. Examples of the wind lidar data will be presented along with a description of the instrument and future developments.

Gentry, Bruce M.↗
DOE OSTI2021

Convection-permitting hindcasting of diurnal variation of Mei-yu rainfall over East China with a global variable-resolution model

Two sets of 48-hr hindcast experiments for Mei-yu season using non-hydrostatic global variable-resolution model (MPAS-Atmosphere) with regional refinement at 4km resolution (V4km) and uniform 60km resolution (U60km) are first-time conducted to attempt at simulating the Mei-yu rainfall in 2015 over East China. The analysis focuses on the seasonal average of diurnal variation of precipitation amount (PA), intensity (PI), and frequency (PF). Both simulations reasonably reproduce the spatial distribution of PA. V4km is more skillful in simulating the spatial distributions and magnitudes of PF and PI. The diurnal cycle of Mei-yu rainfall shows a major early morning peak and a minor afternoon peak, contributed by precipitation during two sub-periods with distinct synoptic circulations. With strong Mei-yu in the first sub-period, the diurnal variation of PA is controlled by nocturnal southwesterly jet. V4km overestimates the morning peak mainly due to its bias in simulating boundary layer inertial oscillation. Although differences in PA between the simulations are small, U60km overestimates PF and underestimates PI. With weak Mei-yu in the second sub-period, the diurnal variation of PA is controlled by both synoptic circulation and local convection. At both resolutions, deviations in the large-scale circulation modulated by a few typhoons lead to positive biases in the morning peak of PA. After removing the typhoon impacts, V4km captures the observed diurnal cycle of PA well, while U60km significantly underestimates PA and PI particularly in the afternoon. Future studies focusing on advancing modeling of southwesterly jet and typhoons may further improve convection permitting simulation of Mei-yu rainfall.

Xu, Mingyue↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
NASA NTRS2015

Results from the Wilkinson Microwave Anisotropy Probe

The Wilkinson Microwave Anisotropy Probe (WMAP) mapped the distribution of temperature and polarization over the entire sky in five microwave frequency bands. These full-sky maps were used to obtain measurements of temperature and polarization anisotropy of the cosmic microwave background with the unprecedented accuracy and precision. The analysis of two-point correlation functions of temperature and polarization data gives determinations of the fundamental cosmological parameters such as the age and composition of the universe, as well as the key parameters describing the physics of inflation, which is further constrained by three-point correlation functions. WMAP observations alone reduced the flat Λ cold dark matter (Lambda Cold Dark Matter) cosmological model (six) parameter volume by a factor of > 68, 000 compared with pre-WMAP measurements. The WMAP observations (sometimes in combination with other astrophysical probes) convincingly show the existence of non-baryonic dark matter, the cosmic neutrino background, flatness of spatial geometry of the universe, a deviation from a scale-invariant spectrum of initial scalar fluctuations, and that the current universe is undergoing an accelerated expansion. The WMAP observations provide the strongest ever support for inflation; namely, the structures we see in the universe originate from quantum fluctuations generated during inflation.

The Wilkinson Microwave Anisotropy Probe (WMAP)↗
DOE OSTI2023

Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

The superconducting radio-frequency cavities are a crucial component of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. When a cavity faults, beam delivery to experimental end users is disrupted. Prediction of cavity faults prior to onset is essential to reduce operation and maintenance costs. In this work, a parallel long short-term memory (LSTM)-convolution neural network (CNN)-based deep learning (DL) model is proposed to predict impending faults using pre-fault signals. Further, we introduce an uncertainty quantification approach using Monte Carlo dropout with the LSTM-CNN model to ascertain confidence in the prediction. The model was tested using multivariate time series signals from stable cavity operations and before faults. Initial results show that on the test dataset, the model can identify impending faults before their onset with an average 10-fold cross validation accuracy of 97.39% and a standard deviation of 0.12% using a 100-ms time window. It is also observed that the model performs better as the prediction time moves closer to the fault onset. For additional context, we compare the performance of the model with three machine-learning-based (ML) fault prediction models. Our proposed parallel LSTM-CNN-based DL method shows better performance than the ML-based methods.

Rahman, Md Monibor↗
NASA NTRS2006

Physics-based Tests to Identify the Accuracy of Solar Wind Ion Measurements: A Case Study with the Wind Faraday Cups

We present techniques for comparing measurements of velocity, temperature, and density with constraints imposed by the plasma physics of magnetized bi-Maxwellian ions. Deviations from these physics-based constraints are interpreted as arising from measurement errors. Two million ion spectra from the Solar Wind Experiment Faraday Cup instruments on the Wind spacecraft are used as a case study. The accuracy of velocity measurements is determined by the fact that differential flow between hydrogen and helium should be aligned with the ambient magnetic field. Modeling the breakdown of field alignment suggests velocity uncertainties are less than 0.16% in magnitude and 3deg in direction. Temperature uncertainty is found by examining the distribution of observed temperature anisotropies in high-beta solar wind intervals where the firehose, mirror, and cyclotron microinstabilities should drive the distribution to isotropy. The presence of a finite anisotropy at high beta suggests overall temperature uncertainties of 8%. Hydrogen and helium number densities are compared with the electron density inferred from observations of the local electron plasma frequency as a function of solar wind speed and year. We find that after accounting for the contribution of minor ions, the results are consistent with a systematic offset between the two instruments of 34%. The temperature and density methods are sensitive to non-Maxwellian features such as heat flux and proton beams and as a result are more suited to slow solar wind where these features are rare. These procedures are of general use in identifying the accuracy of observations from any solar wind ion instrument.

Kasper, J. C.↗
NASA NTRS1998

Modelling and Estimation of Dynamic Ocean Topography Within Global Geopotential Solutions

In this poster, we will present a report on the ongoing investigation "Improved Gravitational and Dynamic Height Models Through the use of Oceanographic Data." we have used a two year mean (1993-1994) of the Dynamic ocean Topography (DOT) field implied by the Semtner and Chervin POCM_4B model, and developed normal equations in surface spherical harmonics to degree and order 30. These normal equations, were combined with normal equations derived from TOPEX and ERS-1 altimeter data over the same time period. Combination solutions (based on satellite tracking data, altimeter data, surface gravity data and OCM data) were were developed Test solutions were obtained estimating the DOT field to 2Ox2O and 3ox3O. These solutions were tested with independent DOT values computed over 38 WOCE hydrographic sections, which contained a total of 3072 stations and represented 216000 km of travelled lines. The weighted standard deviation of the differences between the DOT obtained from the hydrographic data and and the field estimated from the joint combination solutions was computed for each of the test models, the weighted standard deviation for the baseline combination solution excluding the POCM4_B data was 9.7 cm for a DOT solution to 30x30, where introducing the POCM4_B data into the combination model reduced the standard deviation to 9.2 cm, indicating the the introduction of oceanographic information benefits the solution for the dynamic ocean topography. We will discuss the weighting schemes applied and the method of solution. Another aspect of our investigation involves testing alternate parametric representations of the dynamic height field. We looked at the alternative representations in terms of the Proudman functions (PF), and compared these to the use of spherical harmonics (SH) to represent the dynamic ocean topography, using once again the 1993 and 1994 output of the POCM4_B OCM as the reference model. A significant advantage of PF's over SH's is that the former require no "fill-in" values over areas where the DOT is undefined. We show that the PF and SH results using equal number of parameters agree quite well in overall content. In addition, PFs appear better suited for representation of high frequency signals close to the signal boundary. The development of the PF solutions for the DOT will be described and the statistics of the comparisons will be presented.

Lemoine, Frank G.↗
NASA NTRS2016

Results of Outdoor to Indoor Propagation Measurements from 5-32GHz

The demand for wireless services has increased exponentially in the last few years and shows no signs of slowing in the near future. In order for the next generation wireless to provide seamless access, whether the user is indoors or out, a thorough understanding and validation of models describing the impact of building entry loss (BEL) is required. This information is currently lacking and presents a challenge for most system designers. For this reason empirical data is needed to assess the impact of BEL at frequencies that are being explored for future mobile broadband applications This paper present the results of measurements of outdoor-to-indoor propagation from 5-32 GHz in three different buildings. The first is a newer building that is similar in construction to modern residential home. The second is an older commercial office building. The last building is a very new commercial office building built using modern green building techniques. These three buildings allow for the measurement of propagation losses through both modern and older materials; such as glass windows and exterior block and siding. Initial results found that at particular spatial locations the BEL could be less than 1dB or more than 70dB with free space losses discounted (this is likely influenced by multipath). Additionally, it was observed that the PDF distributions of a majority of the measurements trended toward log-normal with means and standard deviations ranging from 8-38dB and 6-14dB, respectively.

building entry loss↗
NASA NTRSDate not supplied

Payload Fairing Acoustic Trade Study: Fill Effect Modeling

A review of acoustic fill effect was conducted using vibroacoustic models of the NASA Space Launch System Block 1B 8.4-meter Payload Fairing. Fill effect is the term used to describe how sound pressure levels (SPLs) inside of a launch vehicle’s payload cavity change when a payload is present, compared to that of an unfilled cavity. Several vibroacoustic models with various payload shapes were developed and the deviation in output from an unfilled cavity model were compared to an analytical fill effect calculation. The analytical expression for the fill effect correction factor, known as the fill factor, is published in NASA-STD-7001B. The development and validation of the NASA fill factor is discussed to provide context for this study and define important variables including the volume fill percentage and the fairing-payload separation gap. The fill factor is reviewed from two perspectives; a global perspective which defines the volume ratio with respect to the entire fairing cavity, and a local perspective which defines the volume ratio with respect to a zone of interest dependent on the payload’s height. The global perspective was considered because it allows a decoupling of the volume ratio and fairing-payload separation gap variables. Both the global and local fill factor methods have difficulty capturing low frequency SPL because of a breakdown in the underlying assumptions of a diffuse acoustic field. Spatial variation in the pressure field at low frequency showed that axially oriented standing waves may be of concern for sensitive payload components; radial variation in SPL was found to be less significant. Further, a novel secondary derivation of the theoretical fill factor is posed for future work in which the absorption characteristics of the payload and fairing surfaces are considered. This work is supported by the NASA Glenn Research Center and the Space Launch System Payload Fairing Working Group.

Spacecraft environments↗
NASA NTRS2004

Qualitative Features Extraction from Sensor Data using Short-time Fourier Transform

The information gathered from sensors is used to determine the health of a sensor. Once a normal mode of operation is established any deviation from the normal behavior indicates a change. This change may be due to a malfunction of the sensor(s) or the system (or process). The step-up and step-down features, as well as sensor disturbances are assumed to be exponential. An RC network is used to model the main process, which is defined by a step-up (charging), drift, and step-down (discharging). The sensor disturbances and spike are added while the system is in drift. The system runs for a period of at least three time-constants of the main process every time a process feature occurs (e.g. step change). The Short-Time Fourier Transform of the Signal is taken using the Hamming window. Three window widths are used. The DC value is removed from the windowed data prior to taking the FFT. The resulting three dimensional spectral plots provide good time frequency resolution. The results indicate distinct shapes corresponding to each process.

Amini, Abolfazl M.↗
NASA NTRS2010

Accurate Measurements of the Dielectric Constant of Seawater at L Band

This report describes measurements of the dielectric constant of seawater at a frequency of 1.413 GHz that is at the center of the L-Sand radiometric protected frequency spectrum. Aquarius will be sensing the sea surface salinity from space in this band. The objective of the project is to refine the model function for the dielectric constant as a function of salinity and temperature so that remote sensing measurements can be made with the accuracy needed to meet the measurement goals (0.2 psu) of the Aquarius mission. The measurements were made, using a microwave cavity operated in the transmission configuration. The cavity's temperature was accurately regulated to 0.02 C by immersing it in a temperature controlled bath of distilled water and ethanol glycol. Seawater had been purchased from Ocean Scientific International Limited (OS1L) at salinities of 30, 35 and 38 psu. Measurements of these seawater samples were then made over a range of temperatures, from l0 C to 35 C in 5 C intervals. Repeated measurements were made at each temperature and salinity, Mean values and standard deviations were then computed. Total error budgets indicated that the real and imaginary parts of the dielectric constant had a relative accuracy of about l%.

Lang, Roger H.↗
NASA NTRS2003

Response Surface Methods For Spatially-Resolved Optical Measurement Techniques

Response surface methods (or methodology), RSM, have been applied to improve data quality for two vastly different spatially-resolved optical measurement techniques. In the first application, modern design of experiments (MDOE) methods, including RSM, are employed to map the temperature field in a direct-connect supersonic combustion test facility at NASA Langley Research Center. The laser-based measurement technique known as coherent anti-Stokes Raman spectroscopy (CARS) is used to measure temperature at various locations in the combustor. RSM is then used to develop temperature maps of the flow. Even though the temperature fluctuations at a single point in the flowfield have a standard deviation on the order of 300 K, RSM provides analytic fits to the data having 95% confidence interval half width uncertainties in the fit as low as +/- 30 K. Methods of optimizing future CARS experiments are explored. The second application of RSM is to quantify the shape of a 5-meter diameter, ultra-lightweight, inflatable space antenna at NASA Langley Research Center. Photogrammetry is used to simultaneously measure the shape of the antenna at approximately 500 discrete spatial locations. RSM allows an analytic model to be developed that describes the shape of the majority of the antenna with an uncertainty of 0.4 mm, with 95% confidence. This model would allow a quantitative comparison between the actual shape of the antenna and the original design shape. Accurately determining this shape also allows confident interpolation between the measured points. Such a model could, for example, be used for ray tracing of radio-frequency waves up to 95 GHz. to predict the performance of the antenna.

Danehy, P. M.↗