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Robert Downs

Publications and source records attributed to Robert Downs.

11 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

The Chemistry and Mineralogy (CheMin) X-Ray Diffractometer on the MSL Curiosity Rover: A Decade of Mineralogy From Gale Crater, Mars

For more than a decade, the CheMin X-ray diffraction instrument on the Mars Science Laboratory rover Curiosity has been returning definitive and quantitative mineralogical and mineral-chemistry data from ~3.5-billion-year-old (Ga) sediments in Gale crater, Mars. To date, 40 drilled rock samples and 3 scooped soil samples have been analyzed during the rover’s 30+ km transit. These samples document the mineralogy of over 800 meters of flat-lying fluvial, lacustrine and aeolian sedimentary rocks that comprise the lower strata of the central mound of Gale crater (Aeolis Mons; informally known as Mt. Sharp) and the surrounding plains (Aeolis Palus, informally known as the Bradbury Rise). The principal mineralogy of the sedimentary rocks is basaltic, with evidence of early and late-stage diagenetic overprinting. The rocks in many cases preserve much of their primary mineralogy and sedimentary features, suggesting that they were never strongly heated or deformed. Using aeolian soil composition as a proxy for the composition of the deposited and lithified sediment, it appears that in many cases diagenetic changes observed are principally isochemical. Exceptions to this trend include secondary nodules, calcium sulfate veining, and rare Si-rich alteration halos. A surprising and yet poorly understood observation is that nearly all the ~3.5 Ga sedimentary rocks analyzed to date contain 15-70 wt.% of X-ray amorphous material. Over-all, this >800-meter section of sedimentary rock explored in lower Mt. Sharp documents a perennial shallow lake environment grading upward into alternating lacustrine/fluvial and aeolian environments, many of which would have been habitable to microbial life.

Mars↗
NASA NTRS2020

Quiet Supersonic Flights 2018 (QSF18) Test: Galveston, Texas Risk Reduction for Future Community Testing with a Low-Boom Flight Demonstration Vehicle

The Quiet Supersonic Flights 2018 (QSF18) Program was designed to develop tools and methods for demonstration of overland supersonic flight with an acceptable sonic boom, and collect a large dataset of responses from a representative sample of the population. Phase 1 provided the basis for a low amplitude sonic boom testing in six different climate regions that will enable international regulatory agencies to draft a noise-based standard for certifying civilian supersonic overland flight. Phase 2 successfully executed a large scale test in Galveston, Texas, developed well documented data sets, calculated dose response relationships, yielded lessons, and identified future risk reduction activities.

Galveston↗
NASA NTRS2020

Quiet Supersonic Flights 2018 (QSF18) Test: Galveston, Texas Risk Reduction for Future Community Testing with a Low-Boom Flight Demonstration Vehicle

The Quiet Supersonic Flights 2018 (QSF18) Program was designed to develop tools and methods for demonstration of overland supersonic flight with an acceptable sonic boom, and collect a large dataset of responses from a representative sample of the population. Phase 1 provided the basis for a low amplitude sonic boom testing in six different climate regions that will enable international regulatory agencies to draft a noise-based standard for certifying civilian supersonic overland flight. Phase 2 successfully executed a large scale test in Galveston, Texas, developed well documented data sets, calculated dose response relationships, yielded lessons, and identified future risk reduction activities.

Galveston↗
NASA NTRSDate not supplied

Turbulence Effects on Shaped Booms: Propagation Simulations Using KZKFourier

Upcoming X-59 aircraft flight tests as part of NASA’s Quesst Mission are expected to occur in a range of atmospheric conditions. Sensitivity of ground waveform acoustic metrics to turbulent perturbations during propagation complicates the determination of noise levels. A series of simulations through turbulence was executed using the KZKFourier model of Stout et al. to develop a database of results for expanding the functionality of NASA tools for estimating turbulence effects on shaped-boom ground waveforms. For 45 cases covering a seven-factor design space, multiple realizations of turbulence were generated to characterize statistical turbulence effects on levels of six acoustic metrics. An individual simulation in the study produced over one thousand waveforms across a virtual microphone array. Different measures were used to evaluate refinement of results with increasing number of simulations. Data suggest that mean effects and variability were most strongly influenced by propagation distance and velocity fluctuation intensity. Although local increases in acoustic metrics were common, the overall average result in all cases was a reduction in metric levels. Sensitivity of acoustic metrics varied, with mean reductions of up to 2 dB in Perceived Level occurring across the 45 cases.

turbulence↗
NASA NTRSDate not supplied

Turbulence Effects on Shaped Booms: Finite Impulse Response Filter Development

Numerical simulations of propagation through turbulent atmospheres can quantify effects on ground waveforms, but such simulations are computationally expensive. To enable quick turnaround analyses as required by NASA’s Quesst Mission, updating the N-wave filtering approach developed by researchers at The Pennsylvania State University to include shaped booms is proposed as an alternative method for estimating turbulence effects on acoustic metrics more quickly. Beginning with a nearfield pressure cylinder modeled after the on-design X-59 configuration, a database of propagation results at 45 turbulence conditions was compiled using nonlinear turbulence propagation modeling code (KZKFourier) and used as input to a process for generating finite impulse response (FIR) filters. Ground waveforms distorted by turbulence were selected to represent mean and mean±standard deviation levels for six metrics, and corresponding FIR filters were generated through a matrix deconvolution process. In order to evaluate how well the FIR filters perform, additional KZKFourier verification cases were devised with different input conditions, and results used as a benchmark. Convolution of shaped boom waveforms modeled using nonturbulent propagation simulations with the new FIR filters showed better agreement on average with KZKFourier statistical results than the N-wave-based FIR filters.

turbulence↗
NASA NTRSDate not supplied

4pPA1 - Turbulence Effects on Shaped Booms: Central Composite Design of Modeled Atmospheric Turbulence Parameters for Sonic Boom Propagation

Propagation of sonic booms through turbulence reduces mean sonic boom perception metric levels and also causes considerable variability. NASA’s PCBoom suite of sonic boom acoustic propagation modules includes an approximate method for accounting for the effects of turbulence on traditional N-wave sonic booms. The current implementation is ineffective for shaped sonic booms or low-booms, and it also has limited values for turbulence and ambient input parameters. NASA’s future X-59 low-boom community noise surveys require an accurate estimate of the effects of turbulence in regions across the USA, so the module must be improved. This work presents the methods of selecting which ambient and turbulence parameters should be included in an improved PCBoom turbulence module. Turbulence and ambient data were collected from two atmospheric model databases, the Climate Forecast System Version 2 and European Centre for Medium-Range Weather Forecast Reanalysis Version 5 (ERA5), hourly from 7 AM to 7 PM local time for 10 years at 19 locations across the USA. A fully-factorial propagation analysis using these parameters would be exceedingly computationally expensive. Instead, a central composite design was chosen resulting in 45 combinations of ambient and turbulence parameters. These 45 cases effectively sample the space balancing computational burden.

turbulence↗
NASA NTRSDate not supplied

The Open Data Repository - an Open Science Platform for Long-Tail Research Data

Introduction: ‘Long-tail’ research is performed by individual PIs and small research teams, producing what are often highly diverse, but relatively small da-tasets that span a variety of traditional scientific disci-plines. ‘Long-tail’ research is a fundamental part of realizing NASA’s goals in planetary sciences and a key input feeding into mission life cycles. The ‘long-tail’ has traditionally lacked the resources (available to larg-er groups and missions) to overcome barriers inhibit-ing the adoption of open science practices, including lack of acknowledgment, time, money, guidance, ex-pertise, and trust in available platforms1. Here we show how the Open Data Repository’s (ODR) data publish-ing platform could help lower some of these barriers and accelerate the adoption of open-science practices in planetary science.

‘Long-tail’ research↗
NASA NTRSDate not supplied

Data Quality Challenges for Analysis Ready Data (ARD)

Data quality plays a critical role in research and applications. The Earth Science Information Partners (ESIP) Information Quality Cluster (IQC) defines four aspects of information quality: Science, Product, Stewardship, and Services. The ESIP IQC has become internationally recognized as an authoritative and responsive resource of information and guidance to data producers and distributors on how to implement data quality standards and best practices for their science data systems, datasets, and data/metadata dissemination services. In recent years, cloud computing environments have provided scale-up capabilities such as data archives and services, enabling interdisciplinary science and applications. More value-added products are expected from data service providers, including Analysis Ready Data (ARD). ARD refers to data that has been preprocessed into a form that allows immediate analysis by the end user, processed to a minimum set of requirements and provides interoperability over time and across multiple datasets. Once a dataset has been developed from its original form to produce ARD, what quality characteristics should the derived dataset or ARD possess? Also, is it safe to assume that the quality of the ARD is consistent with the quality of the source data, or are there special attributes to an ARD that would warrant a secondary, independent quality assessment? What provenance (also called “data lineage”) information needs to be included in ARD? It is important to answer these questions, especially given the ease of use of ARD, and the consequent temptation by users to trust ARD without understanding the limitations or possible variations in quality compared to the source data. In this presentation, we will discuss data quality challenges for ARD products and services and introduce IQC for participation.

data quality↗