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At least 1,135 records · Page 63

All sources NASA NTRS DOE OSTI

NASA NTRS2018

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↗

NASA NTRS2019

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.↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRS2021

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)↗

NASA NTRS2022

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 NTRS2022

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

DOE OSTI2014

Modular cathode assemblies and methods of using the same for electrochemical reduction

Modular cathode assemblies are useable in electrolytic reduction systems and include a basket through which fluid electrolyte may pass and exchange charge with a material to be reduced in the basket. The basket can be divided into upper and lower sections to provide entry for the material. Example embodiment cathode assemblies may have any shape to permit modular placement at any position in reduction systems. Modular cathode assemblies include a cathode plate in the basket, to which unique and opposite electrical power may be supplied. Example embodiment modular cathode assemblies may have standardized electrical connectors. Modular cathode assemblies may be supported by a top plate of an electrolytic reduction system. Electrolytic oxide reduction systems are operated by positioning modular cathode and anode assemblies at desired positions, placing a material in the basket, and charging the modular assemblies to reduce the metal oxide.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Sustained Low Temperature NOx Reduction

Increasing regulatory, environmental, and customer pressure in recent years led to substantial improvements in the fuel efficiency of diesel engines, including the remarkable breakthroughs demonstrated through the Super Truck program supported by the U.S. Department of Energy (DOE). On the other hand, these improvements have translated into a reduction of exhaust gas temperatures, thus further complicating the task of controlling NOx emissions, especially in low power duty cycles. The need for improved NOx conversion over these low temperature duty cycles is also observed as requirements tighten with in-use emissions testing. Sustained NOx reduction at low temperatures, especially in the 150-200oC range, shares some similarities with the more commonly discussed cold-start challenge, however poses a number of additional and distinct technical problems. In this project we set a bold target of achieving and maintaining a 90% NOx conversion at the SCR catalyst inlet temperature of 150oC. The project is intended to push the boundaries of the existing technologies, while staying within the realm of realistic future practical implementation. In order to meet the resulting challenges at the levels of catalyst fundamentals, system components, and system integration, Cummins has partnered with the DOE, Johnson Matthey, and Pacific Northwest National Lab and initiated the Sustained Low-Temperature NOx Reduction program at the beginning of 2015. Through this collaboration, we are exploring catalyst formulations and catalyst architectures with enhanced catalytic activity at 150°C; opportunities to approach the desirable ratio of NO and NO2 in the SCR feed gas; options for robust low-temperature reductant delivery; and the requirements for overall system integration. The program is expected to deliver an on-engine demonstration of the technical solution and an assessment of its commercial potential. In the SAE meeting, we will share the initial performance data on engine to highlight the path to achieve 90% NOx conversion at the SCR inlet temperature of 150oC.

Zha, Yuhui↗

DOE OSTI2018

Modular cathode assemblies and methods of using the same for electrochemical reduction

25 ENERGY STORAGE↗

DOE OSTI2013

Implementation of hybrid variance reduction methods in a multi group Monte Carlo code for deep shielding problems

In this paper, the work that has been done to implement variance reduction techniques in a three dimensional, multi group Monte Carlo code - Tortilla, that works within the frame work of the commercial deterministic code - Attila, is presented. This project is aimed to develop an integrated Hybrid code that seamlessly takes advantage of the deterministic and Monte Carlo methods for deep shielding radiation detection problems. Tortilla takes advantage of Attila's features for generating the geometric mesh, cross section library and source definitions. Tortilla can also read importance functions (like adjoint scalar flux) generated from deterministic calculations performed in Attila and use them to employ variance reduction schemes in the Monte Carlo simulation. The variance reduction techniques that are implemented in Tortilla are based on the CADIS (Consistent Adjoint Driven Importance Sampling) method and the LIFT (Local Importance Function Transform) method. These methods make use of the results from an adjoint deterministic calculation to bias the particle transport using techniques like source biasing, survival biasing, transport biasing and weight windows. The results obtained so far and the challenges faced in implementing the variance reduction techniques are reported here. (authors)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Dimensionality Reduction Algorithms for Big Nuclear Data Streams with Application to SCALE Cross Sections

A central problem in performing accurate nuclear reactor simulations is the ability to capture the changes in the few group microscopic cross sections as functions of system state, e.g. moderator density, fuel temperature, or neutron spectrum, and time and/or burnup of the fuel. For most reactor simulation scenarios, this results in huge data stream, which must be interpolated as needed by downstream calculations (e.g., core-wide simulation). This is typically accomplished by using sophisticated interpolating functions to capture the few-group variations over the wide range of conditions expected during operation. To address handling this big data, this paper explores the use of dimensionality reduction (DR) techniques to identify the true dimensionality of the data prior to its use in the calculations. This could significantly reduce the computational cost required for data processing/manipulation, thus reducing a significant portion of the total calculations time. By way of an example, in the current version of the ORIGEN-ARP module, the assembly-averaged microscopic cross sections at different conditions, e.g., fuel enrichments, irradiation history and water density, which are calculated using the TRITON sequence and interpolated on-demand by the ARP module to model assembly depletion for a wide range of assembly types (e.g. Westinghouse 17x17 and GE 9x9) over a wide range of conditions. This approach typically results in ∼1 GB per assembly type, which although not insurmountable, can be impractical for distribution. and it is the premise of this paper that most of this data is heavily correlated and hence can take great benefit of condensation via DR. To demonstrate DR, the proposed algorithm is applied to the nuclear data streams generated by SCALE 6.1. Using the depletion sequence (TRITON), microscopic cross sections are calculated for a mixed oxide (MOX) fuel assembly design at a representative range of fuel enrichments and fuel burnup. In conventional core simulators, multi dimensional interpolation of these cross sections is used to provides neutronics feedback due to local conditions without returning to expensive TRITON calculations for every change. This paper shows that dimensionality reduction can help compress the size of the cross section data without sacrificing the accuracy of their reconstruction at all conditions of interest. This work explores the application of dimensionality reduction algorithms to nuclear data streams typically employed in support of conventional full-core reactor calculations which rely on interpolation of pre-calculated microscopic cross sections. In particular, analysis of the few group MOX cross sections revealed that a large degree of dependencies exist between the few-group cross sections, which can be revealed via range-finding algorithms. Specifically, correlations between different reaction types for the same isotope have been identified, and correlations between different enrichments and correlations across different isotopes have been identified. This paper demonstrates that the reduction errors can be quantified, and they are uncorrelated with the range of variations for the various cross sections. In future work, we will focus on identifying the smallest rank that takes into account all types of correlations across the range of conditions used to model cross section variations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

Recovery from loss of reductant feed to a U/Pu separation contactor, magnox reprocessing plant, Sellafield - 14217

Following a revealed sustained loss of reductant feed to the Primary Separation 4 (PS4) U/Pu separation contactor in the Magnox Reprocessing Plant in 2014 the solvent extraction cycles were safely shutdown. However, initial mass balance calculations indicated that over four times the typical mass of Pu could be present in PS4. Due to the loss of reductant feed, much of this Pu was expected to be in the solvent phase of Tributyl Phosphate and Odourless Kerosene (TBP/OK) where it would be difficult to observe with the neutron monitors located under PS4. Samples were taken of the solvent in PS4 which confirmed the presence of Pu in solvent; normally almost all of the Pu in PS4 is in the aqueous phase. The volume ratio of solvent to aqueous in the PS4 contactor is high. This meant that simply re-dosing PS4 with reductant could not be supported as a recovery option due to the potential to realise high Pu concentrations in the aqueous phase. This paper details the recovery options which were considered and the ultimate decision to recover PS4 using Gadolinium (Gd) poisoning and Gd-dosed reductant feeds. The recovery included: - The need to consider the impact of Gd poisoning on monitoring of the recovery with the PS4 neutron monitors; - Close support from criticality safety personnel on the plant at all times during the recovery; - Production of operator aids and short fit for purpose safety documentation to assist with the recovery. The recovery of PS4 proceeded safely and smoothly. There were no losses of Pu to raffinate streams despite the prolonged shutdown. The plant was then safely rundown to flush out residual Pu and Gd. (author)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

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↗