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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 1,261 records · Page 70
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NASA NTRS2022

How Well do Multisatellite Products Capture the Space-Time Dynamics of Precipitation? Part II: Building an Error Model Through Spectral System Identification

Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatio-temporal scale. We propose a spectral error model which accounts for the neighboring space-time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency-wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space-time linear filtering term. The random errors are represented as a non-stationary additive noise. The spectral error model is applied to the IMERG multi satellite precipitation product and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge-radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass. While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from totally different sources shall not be expected to automatically have statistically independent errors.

Precipitation↗
NASA NTRSDate not supplied

Where and How Well Do Cabin Crew Sleep During Long-Haul Flights?

INTRODUCTION: Sleep loss and circadian disruption pose a significant risk in aviation. Many studies have shown that inflight rest locations influence alertness and performance among pilots, but few studies have evaluated cabin crew. The aim of this research was to assess sleep outcomes among cabin crewmembers sleeping in a bunk during both outbound and inbound compared to alternating sleep in a bunk or a jump seat during one long-haul route. METHODS: Twenty-nine (5 male) cabin crewmembers (Mage = 30.61, SD = 2.91) flew the same long-haul route (outbound and inbound) with a flight duration of 10:41 (± 0:14) hours. Participants were randomly assigned to fly on an aircraft with a bunk in both directions or to fly an aircraft with a bunk in one direction and with a high comfort jump seat (HCJS) in the other direction for their sleep opportunity. They wore an Actiwatch throughout the entire study and completed a sleep diary at bedtime and upon waking for each sleep opportunity. Seventy-seven percent of the flights had a bunk and 23% had a HCJS. A series of mixed-effects models were performed to assess the differences in several sleep parameters when crewmembers slept in the bunk during both directions of the flight (bunk-only) compared to sleep obtained in the HCJS during one direction and bunk in the other (bunk+HCJS). RESULTS: Fifty-seven flights were included in the analyses. There were significant differences between sleep time (b = 22.56, SE = 9.07, p = .02, Hedges’ g = -0.65) and sleep efficiency (b = 16.75, SE = 4.49, p = .001, Hedges’ g = -1.07) obtained in bunk-only vs. bunk+HCJS. Crewmembers obtained more sleep and had better sleep efficiency when they slept only in the bunk compared to bunk+HCJS. DISCUSSION: Our results showed that cabin crewmembers slept longer and had better sleep efficiency when they used the bunk. Further research is needed to understand how subjective sleepiness and subsequent performance are influenced by sleep opportunity in a bunk-only compared to bunk+HCJS.

long-haul↗
NASA NTRS2024

Converter/Compressor Building Solid Waste Management Unit (SWMU) #89 Monitoring Well 21 Area Interim Measure Work Plan

This Work Plan details the approach and design for the Interim Measure (IM) to remediate groundwater within the MW21 Area where VOC concentrations exceed Florida Department of Environmental Protection Natural Attenuation Default Concentrations. The IM includes in-situ bioremediation via injection of emulsified vegetable oil (EVO) and emulsified zero-valent iron (EZVI). The in-situ bioremediation technology consists of injecting an electron donor substrate into the subsurface to promote microbial breakdown of VOCs. The EVO injections for this IM are designed based on the use of Provectus ERD-CH4, which is a vegetable oil/carbon substrate mixture. The EZVI injections for this IM are designed based on the use of Provectus EZVI-CH4. The IM treatment layout consists of nine injection locations of EVO at approximately 10-foot radius of influence (ROI) and three injections locations of EZVI at approximately 6-foot ROI. The injection method will be direct push using a DPT rig. The overall injection depths range from 7 to 17 ft bls. The 7 ft bls top depth is designed to be 2 ft below the water table to prevent substrates from reaching the top of the water table. Injections are planned to be implemented using 2 ft intervals and will take place using a bottom-up approach to 2 ft below the water table. This work plan further describes the IM design, injection calculations, performance monitoring plan, and provides an injection layout. Revision 1 of this Work Plan includes quarterly performance sampling during Year 1 in accordance with F.A.C. 62-780. These changes are reflected in Section 7 and Table 2 of the Work Plan.

Mark J Jonnet↗