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At least 289 records · Page 16

Logistics Reduction Technologies for Exploration Missions

Human exploration missions under study are limited by the launch mass capacity of existing and planned launch vehicles. The logistical mass of crew items is typically considered separate from the vehicle structure, habitat outfitting, and life support systems. Although mass is typically the focus of exploration missions, due to its strong impact on launch vehicle and habitable volume for the crew, logistics volume also needs to be considered. NASA's Advanced Exploration Systems (AES) Logistics Reduction and Repurposing (LRR) Project is developing six logistics technologies guided by a systems engineering cradle-to-grave approach to enable after-use crew items to augment vehicle systems. Specifically, AES LRR is investigating the direct reduction of clothing mass, the repurposing of logistical packaging, the use of autonomous logistics management technologies, the processing of spent crew items to benefit radiation shielding and water recovery, and the conversion of trash to propulsion gases. Reduction of mass has a corresponding and significant impact to logistical volume. The reduction of logistical volume can reduce the overall pressurized vehicle mass directly, or indirectly benefit the mission by allowing for an increase in habitable volume during the mission. The systematic implementation of these types of technologies will increase launch mass efficiency by enabling items to be used for secondary purposes and improve the habitability of the vehicle as mission durations increase. Early studies have shown that the use of advanced logistics technologies can save approximately 20 m(sup 3) of volume during transit alone for a six-person Mars conjunction class mission.

Broyan, James L., Jr.↗

Propulsion Noise Reduction Research in the NASA Advanced Air Transport Technology Project

The Aircraft Noise Reduction (ANR) sub-project is focused on the generation, development, and testing of component noise reduction technologies progressing toward the NASA far term noise goals while providing associated near and mid-term benefits. The ANR sub-project has efforts in airframe noise reduction, propulsion (including fan and core) noise reduction, acoustic liner technology, and propulsion airframe aeroacoustics for candidate conventional and unconventional aircraft configurations. The current suite of propulsion specific noise research areas is reviewed along with emerging facility and measurement capabilities. In the longer term, the changes in engine and aircraft configuration will influence the suite of technologies necessary to reduce noise in next generation systems.

Van Zante, Dale↗

Update on Risk Reduction Activities for a Liquid Advanced Booster for NASA's Space Launch System

The stated goals of NASA's Research Announcement for the Space Launch System (SLS) Advanced Booster Engineering Demonstration and/or Risk Reduction (ABEDRR) are to reduce risks leading to an affordable Advanced Booster that meets the evolved capabilities of SLS and enable competition by mitigating targeted Advanced Booster risks to enhance SLS affordability. Dynetics, Inc. and Aerojet Rocketdyne (AR) formed a team to offer a wide-ranging set of risk reduction activities and full-scale, system-level demonstrations that support NASA's ABEDRR goals. During the ABEDRR effort, the Dynetics Team has modified flight-proven Apollo-Saturn F-1 engine components and subsystems to improve affordability and reliability (e.g., reduce parts counts, touch labor, or use lower cost manufacturing processes and materials). The team has built hardware to validate production costs and completed tests to demonstrate it can meet performance requirements. State-of-the-art manufacturing and processing techniques have been applied to the heritage F-1, resulting in a low recurring cost engine while retaining the benefits of Apollo-era experience. NASA test facilities have been used to perform low-cost risk-reduction engine testing. In early 2014, NASA and the Dynetics Team agreed to move additional large liquid oxygen/kerosene engine work under Dynetics' ABEDRR contract. Also led by AR, the objectives of this work are to demonstrate combustion stability and measure performance of a 500,000 lbf class Oxidizer-Rich Staged Combustion (ORSC) cycle main injector. A trade study was completed to investigate the feasibility, cost effectiveness, and technical maturity of a domestically-produced engine that could potentially both replace the RD-180 on Atlas V and satisfy NASA SLS payload-to-orbit requirements via an advanced booster application. Engine physical dimensions and performance parameters resulting from this study provide the system level requirements for the ORSC risk reduction test article. The test article is scheduled to complete fabrication and assembly soon and continue testing through late 2019. Dynetics has also designed, developed, and built innovative tank and structure assemblies using friction stir welding to leverage recent NASA investments in manufacturing tools, facilities, and processes, significantly reducing development and recurring costs. The full-scale cryotank assembly was used to verify the structural design and prove affordable processes. Dynetics performed hydrostatic and cryothermal proof tests on the assembly to verify the assembly meets performance requirements..

Crocker, Andrew M.↗

Limits on Achievable Intensity Reduction with an Optical Occulter

Deep shadowing of a normally incident plane wave by an opaque circular disk is partially negated by the formation of a region of strong intensity surrounding the axis passing normally through the disk center. This local intensity enhancement, historically referred to as the Poisson Spot (also known as the Spot of Arago), has been the principal source of difficulties in applications where a significant reduction of the incident intensity is essential. In particular, the NASA Terrestrial Planet Finder's (TPF) mission requires suppression of direct starlight by at least 10 orders of magnitude over the entire visible spectral range. One technique that has been proposed for blocking the direct starlight is to use a rotationally symmetric disk with petallike segments along its boundary. We find that, even though such configurations could, indeed, theoretically provide the desired intensity reduction, they would require unreasonably small radii of curvature at the petals' tips (in the range of micrometers). When the radii of curvature are increased to 3 mm, the intensity reduction drops to a modest 5 to 6 orders of magnitude. Given that for the NASA's TPF mission the proposed occulter radius would be on the order of 25 m, even the 3 mm radius of curvature would be too small for any practical implementation. Further increases of the radius of curvature result in progressively poorer intensity suppression. As an alternative solution we propose an apodized circular disk. We show that with an optimized apodization function, intensity reductions of at least 10 orders of magnitude can be achieved over the entire visible spectral range. Numerical results are presented for parameters appropriate to the NASA TPF mission.

Wasylkiwskyj, Wasyl↗

Far Term Noise Reduction Roadmap for the Mid-Fuselage Nacelle Subsonic Transport

A noise reduction technology roadmap study is presented to determine the feasibility for the Mid-Fuselage Nacelle (MFN) aircraft concept to achieve the noise goal set by NASA for the Far Term time frame, beyond 2035. The study starts with updating the noise prediction of the existing MFN configuration that had been modeled for the time frame between 2025 and 2035. The updated prediction for the Mid Term time frame is 34.3 dB cumulative effective perceived noise level (EPNL) below the Stage 4 regulation. A suite of technologies that are deemed feasible to mature for practical implementation in the Far Term and whose potentials for noise reduction have been illustrated is selected for analysis. For each technology, component noise reduction is modeled either by available experimental data or by physics-based modeling with aircraft system level methods. The noise reduction is then applied to the corresponding noise component predicted by advanced aircraft system noise prediction tools, and the total aircraft noise is predicted as the incoherent summation of the components. It is shown that the Far Term MFN aircraft has the potential to achieve a cumulative noise level of 40.2 EPNL dB below Stage 4. The key technologies to achieve this low aircraft noise level are assessed by the impact of each technology on the aircraft system noise. This roadmap shows the potential of this revolutionary, yet still tube-and-wing, MFN concept to reach the NASA Far Term noise goal.

Guo, Yueping↗

Far Term Noise Reduction Technology Roadmap for a Large Twin-Aisle Tube-And-Wing Subsonic Transport

Interest in unconventional aircraft architectures has steadily increased over the past several decades. However, each of these concepts has several technical challenges to overcome before maturing to the point of commercial acceptance. In the interim, it is important to identify any technologies that will enhance the noise reduction of conventional tube-and-wing aircraft. A technology roadmap with an assumed acoustic technology level of a 2035 entry into service is established for a large twin-aisle, tube-and-wing architecture to identify which technologies provide the most noise reduction. The noise reduction potential of the architecture relative to NASA noise goals is also assessed. The current roadmap estimates only a 30 EPNdB cumulative margin to Stage 4 for this configuration of a tube-and-wing aircraft with engines under the wing. This falls short of reaching even the 2025 Mid Term NASA goal (32 EPNdB) in the Far Term time frame. Specifically, the lack of additional technologies to reduce the aft fan noise and the corresponding installation effects is the key limitation of the noise reduction potential of the aircraft. Under the same acoustic technology assumptions, unconventional architectures are shown to offer an 8–10 EPNdB benefit from favorable relative placement of the engine when integrated to the airframe.

June, Jason C.↗

Variable Mixing Nozzle Design with Slotted Vortex Generators for Jet Noise Reduction

A new variable geometry turbofan nozzle concept is presented with the dual goals of airport noise reduction and high propulsive efficiency at cruise, while employing only a single moving part. The nozzle utilizes a number of curved vanes to function as vortex generators (VGs) during takeoff and initial climb. Streamwise vortices provide an increase in mixing between the jet and external stream, with a resulting decrease in high frequency noise generation and a noise source distribution which is more amenable to airframe-based shielding. Unlike conventional VGs, the vanes are positioned without transverse incidence, and a pressure difference across the surface of each vane is created via an adjacent slot. During high altitude flight, when nozzle efficiency concerns outweigh any preference for jet noise reduction, all slots may be closed through rotation of a slotted ring inside the nozzle. RANS-based numerical analysis is performed to calculate propulsive metrics, determine noise source characteristics and understand various design parameter sensitivities. A new formulation is developed to approximately correct computed noise intensities for any throttle adjustments required to maintain takeoff thrust, and a rough estimate of shielding effectiveness is proposed by means of integration over a spatially distributed noise source. A total of 19 different nozzle geometries are considered in the present study. Results indicate significant noise benefits at takeoff, including a 2 kHz fly-over noise reduction of roughly 2-3 dB, while allowing for less than 15% of the net thrust reduction at supersonic cruise calculated for a fixed-penetration chevron nozzle.

Jet Noise↗

Reduction of Spectral Radiance Reflectance During the Annular Solar Eclipse of 21 June 2020 from DSCOVR/EPIC

We have analyzed three EPIC images during the annular solar eclipse on June 21, 2020 when centers of the eclipse were in the Arabian Peninsula (mostly desert), the Himalayas (mostly barren land), and China (mostly cloudy over vegetation) and compared with two images for 2017 American solar eclipse over Casper, WY and Columbia, MO (vegetated surface for both). We found that the global average reductions of spectral reflectance for the three images during 2020 solar eclipse are quite different while the reductions of spectral reflectance for the two images for 2017 solar eclipse are similar. Radiative transfer model simulations suggest that different surface spectral albedo and cloud fraction attribute to the different reduction of spectral reflectance for three images during 2020 eclipse while similar spectral albedo of vegetated surface around Casper and Columbia are the main cause for the similar spectral reflectance reduction for the two images during 2017 solar eclipse.

solar eclipse↗

Significant Climate Benefits from Near-Term Climate Forcer Mitigation in Spite of Aerosol Reductions

Near-term climate forcers (NTCFs), including aerosols and chemically reactive gases such as tropospheric ozone and methane, offer a potential way to mitigate climate change and improve air quality--so called "win-win" mitigation policies. Prior studies support improved air quality under NTCF mitigation, but with conflicting climate impacts that range from a significant reduction in the rate of global warming to only a modest impact. Here, we use state-of-the-art chemistry-climate model simulations conducted as part of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) to quantify the 21st-century impact of NTCF reductions, using a realistic future emission scenario with a consistent air quality policy. Non-methane NTCF (NMNTCF; aerosols and ozone precursors) mitigation improves air quality, but leads to significant increases in global mean precipitation of 1.3% by mid-century and 1.4% by end-of-the-century, and corresponding surface warming of 0.23 and 0.21 K. NTCF (all-NTCF; including methane) mitigation further improves air quality, with larger reductions of up to 45% for ozone pollution, while offsetting half of the wetting by mid-century (0.7% increase) and all the wetting by end-of-the-century (non-significant 0.1% increase) and leading to surface cooling of -0.15 K by mid-century and -0.50 K by end-of-the-century. This suggests that methane mitigation offsets warming induced from reductions in NMNTCFs, while also leading to net improvements in air quality.

Near-term climate forcers (NTCFs)↗

Identification and Reduction of Interactional Noise of a Quadcopter in Hover and Forward Flight Conditions

Advanced Air Mobility is a vision for a safe, accessible, and sustainable aviation system to transport people and cargo between places not served by traditional aviation. With this emerging transportation industry, there is motivation to characterize the noise of vehicles to determine their potential impacts on the community. An experimental testing campaign was conducted on a representative model of a small unmanned aircraft system in the NASA Langley Low Speed Aeroacoustic Wind Tunnel as a continuation of a previous testing campaign. The goals of the current test are to identify sources of interactional noise as well as to test custom-designed rotors and noise reduction devices. The tested noise reduction methods involve increasing the vertical distances between the rotors and the vehicle airframe as well as between the forward and aft rotor disk planes. These methods are intended to reduce rotor-airframe interaction noise in hover and fore-aft rotor wake ingestion noise in forward flight. A phased microphone array is also utilized to identify the locations of prominent noise generation for the different vehicle configurations in forward flight. Elevation of the rotors from the vehicle airframe yielded nearly 8 dBA overall noise reduction in forward flight, while yielding up to 4 dB reduction in overall tonal levels for one of the rotors in hover.

Nikolas S Zawodny↗

NASA Satellite Measurements Show Global-scale Reductions in Free Tropospheric Ozone in 2020 and Again in 2021 During COVID-19

NASA satellite measurements show that ozone reductions throughout the Northern Hemisphere (NH) free troposphere reported for spring-summer 2020 during the Corona VIrus Disease 2019 (COVID-19) pandemic have occurred again in spring-summer 2021. The satellite measurements show that tropospheric column ozone (TCO) (mostly representative of the free troposphere) for 20oN-60oN during spring-summer for both 2020 and 2021 averaged ~3 Dobson Units (DU) (or ~7-8%) below normal. These ozone reductions in 2020 and 2021 were the lowest in the 2005- 2021 record. We also include satellite measurements of tropospheric NO2that exhibit reductions of ~10-20% in the NH in early spring-to-summer 2020 and 2021, suggesting that reduced pollution was the main cause for the low anomalies in NH TCO in 2020 and 2021. Reductions of TCO ~2DU (7 %) are also measured in the Southern Hemisphere in austral summer but are 26not associated with reduced NO2.

Tropospheric Ozone↗

Decoupling the Effects of Anthropogenic Emission Reductions from the Meteorology and Natural Emissions in TROPOMI NO 2 Retrievals During the 2020 COVID-19 Lockdowns

Satellite measurements during the COVID-19 lockdowns that began in 2020 revealed unprecedented reductions in NO 2 tropospheric vertical column densities (VCD). These reductions have largely been attributed to reduced anthropogenic emissions associated with abrupt decreases in road traffic and other power consuming business activities. Although decreased emissions tended to be the main contributor to the observed NO 2 VCD reduction, meteorological variability also played a role. Whereas the observed VCD changes were predominantly negative in places where public health policies were strictly enforced, meteorology had both positive and negative effects over short time intervals. Here, we present results from a global study of the NO 2 reductions aimed at disentangling the meteorological and natural emission variability from the anthropogenic emissions over the world’s most populated megacities. For this study, NASA’s TROPOMI NO 2 algorithm was used in conjunction with the Global Modeling Initiative (GMI) chemical transport model to separate the contributions due emissions and meteorology. A priori NO 2 profiles were generated from two GMI simulations performed for 2020 at a resolution of 0.25° longitude x 0.25° latitude. The first simulation used updated, COVID-impacted NOX emissions based on Forster et al. (2020), while the second simulation used the 2019 emissions with the 2020 meteorology, referred to here as 2020BAU. The 2020BAU data set allowed for the decoupling of the emission component from the meteorology. When compared to the same period in 2019, NO 2 column amounts during the lockdowns were reduced in 35 out of 36 cities. While reduced emissions contributed most to the observed total change in NO 2 during the lockdowns, the effects of meteorology were significant, ranging between 40% (Chennai) and 15% (Beijing). In China, an increase in NO 2 levels due to meteorology were observed in five out of the seven cities considered in the study. Use of different a-priori NO 2 profiles from the two simulations in our TROPOMI retrievals allowed for a determination of retrieval errors that ranged from -1.7% to -11.0%. We used the quality assurance flag > 0.75 to select the highest quality scenes in the study period. Using the GMI, we estimated the sampling biases to be in the range -11% to 10% of the total change for the cities in our study.

Brad Fisher↗

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1%in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.

Machine Learning↗

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Machine Learning↗

Uncertainty Reduction With Multi-Model Monte Carlo for Crystal Plasticity Simulations of Additively Manufactured Metals

In this work, multi-model Monte Carlo estimators are developed to reduce uncertainty in quantities of interest (QoIs) extracted from crystal plasticity simulations of additively manufactured (AM) metals. A significant concern in AM parts is uncertainty in mechanical properties caused in part by complex microstructures that arise from the AM process. Quantifying uncertainty in microstructure-sensitive behavior using experiments alone is costly, especially when mechanical allowables must be established. Quantitative relationships among microstructure, micromechanical metrics like slip accumulation, crack initiation, and failure are also difficult to capture with limited experiments. Crystal plasticity material models instead enable computational prediction of micromechanical stress and strain fields given a discretized microstructure. However, high-fidelity finely discretized crystal plasticity simulations are computationally expensive, while lower-fidelity models are less accurate and generally biased, making uncertainty quantification and reduction computationally difficult as well. Multi-model Monte Carlo methods leverage correlations between high- and low-fidelity models to produce unbiased estimators for QoIs with reduced uncertainty relative to standard Monte Carlo. Crystal plasticity QoIs considered in this work include yield strength and the mean and extreme values of micromechanical fields that are relevant to crack initiation. Multi-model Monte Carlo estimators are developed for each individual QoI and several groups of QoIs. The results of this work establish relationships among model correlations, sample allocation, and uncertainty reduction for different combinations of QoIs and demonstrate a trend of less uncertainty reduction as QoIs become more sensitive to local microstructure. Limitations from using pilot samples to estimate model covariances and train low-fidelity models are also addressed. The uncertainty reduction achieved by multi-model Monte Carlo is an important step toward using computational mechanics models to predict microstructure-sensitive crack initiation and failure in AM parts.

uncertainty quantification↗

Synergizing Fe 2 O 3 Nanoparticles on Single Atom Fe‐N‐C for Nitrate Reduction to Ammonia at Industrial Current Densities

The electrochemical reduction of nitrates (NO 3 − ) enables a pathway for the carbon neutral synthesis of ammonia (NH 3 ), via the nitrate reduction reaction (NO 3 RR), which has been demonstrated at high selectivity. However, to make NH 3 synthesis cost-competitive with current technologies, high NH 3 partial current densities (j NH3 ) must be achieved to reduce the levelized cost of NH 3 . Here, the high NO 3 RR activity of Fe-based materials is leveraged to synthesize a novel active particle-active support system with Fe 2 O 3 nanoparticles supported on atomically dispersed Fe–N–C. The optimized 3×Fe 2 O 3 /Fe–N–C catalyst demonstrates an ultrahigh NO 3 RR activity, reaching a maximum j NH3 of 1.95 A cm −2 at a Faradaic efficiency (FE) for NH 3 of 100% and an NH 3 yield rate over 9 mmol hr −1 cm −2 . Operando XANES and post-mortem XPS reveal the importance of a pre-reduction activation step, reducing the surface Fe 2 O 3 (Fe 3+ ) to highly active Fe 0 sites, which are maintained during electrolysis. Durability studies demonstrate the robustness of both the Fe 2 O 3 particles and Fe–N x sites at highly cathodic potentials, maintaining a current of −1.3 A cm −2 over 24 hours. This work exhibits an effective and durable active particle-active support system enhancing the performance of the NO 3 RR, enabling industrially relevant current densities and near 100% selectivity.

active support↗

Surface water nitrogen and sediment potential nitrate reduction rates, nutrient stocks, and stable isotopes from nine wetlands at the Tanglewood Biological Station, Alabama

This dataset supports a broader study investigating wetland hydrologic and biogeochemical responses to inundation disturbances. Bimonthly surface water and sediment sampling events were conducted at nine wetland sites situated within the Tanglewood Biological Station in Alabama from April 2023 to February 2024. The contents included in the data package include surface water nitrogen (nitrogen oxides and ammonium) and sediment potential nitrate reduction rates (measured as potential denitrification and dissimilatory nitrate reduction to ammonium processing), nutrient stocks (total carbon, total nitrogen, and organic matter), and stable isotopes (carbon and nitrogen). Water level data related to each wetland location can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/2530253 (Kirker et al., 2024) and related water geochemistry data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3001967 (Forbes et al., 2025). In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata and international generic sample numbers (IGSNs); (4) readme; (5) the field protocol; and (6) a subfolder with sample data. The sample data subfolder contains (1) sediment potential denitrification rate, (2) sediment potential dissimilatory nitrate reduction to ammonium (DNRA) rate, (3) sediment total carbon and nitrogen content, (4) sediment stable isotopes (delta nitrogen-15 and delta carbon-13), (5) sediment percent organic matter, (6) surface water nitrous oxides, (7) surface water ammonium, and (8) methods codes. All files are .csv or .pdf.

Ammonium↗

Selection of solvents for integrated CO 2 absorption and electrochemical reduction systems

Abstract Solvent‐based electrochemical CO 2 reduction (CO 2 R) enables the production of chemicals or fuels using CO 2 from a preceding absorption process. Employing previously tested CO 2 capture solvents does not ensure their suitability for either CO 2 R or integrated CO 2 absorption‐reduction. We propose solvent selection criteria that include the CO 2 solubility, kinetic constant, ionic conductivity, concentration of the bicarbonate, carbamate, and solvent cation in the CO 2 ‐loaded solution, and sustainability indicators. They are implemented for solvent selection (a) from novel, aqueous mixtures of N ‐methylcyclohexylamine (MCA) with piperazine (PZ), 2‐amino‐2‐methyl‐1‐propanol (AMP), potassium hydroxide (KOH), and potassium chloride (KCl) and (b) from aqueous monoethanolamine (MEA), AMP, KOH, MCA, and PZ solutions. Versions of a modified Kent‐Eisenberg model for strong bases, carbamate, and non‐carbamate‐forming amine solutions are developed and parameterized through experimental equilibrium measurements. CO 2 R experimental results are presented for solutions of KOH and MCA + KOH, as these indicate desired trade‐offs for CO 2 absorption and reduction.

Amines↗