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At least 1,243 records · Page 69
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NASA NTRS2021

The Case for a 50+ Year Radioisotope Power System

The Johns Hopkins University Applied Physics Laboratory (JHU/APL) is leading the NASA funded Interstellar Probe study to explore the “Very Local” interstellar medium. To perform this exploration the mission will be required to last at least 50 years in regions of space where solar power is no longer practical. Additionally, several new studies for the National Academies’ Planetary Science and Astrobiology Decadal Survey are planning missions lasting 20-35 years. The Decadal Survey is used to build consensus on priority of national science goals. These proposed missions are inconsistent with the NASA’s current Radioisotope Power Systems (RPS) life requirement of 14 years (flight). Paramount to these proposed long-duration missions are questions about the longevity of such a mission. Evidence exists that space-borne Radioisotope Power Systems can indeed last a long time. LES-9, Voyager I, and Voyager II are over 40 years old, LES-8, Pioneer 10, and Pioneer 11 lasted 28, 30, and 22 years, respectively, and New Horizons is still active 15 years after launch. This paper explores the need for RPS designs that are intended to last much longer than the current requirement of 14 years (17 years after fueling) and explores the historical record for actual vs design lifetimes to show the feasibility of building long lasting RPS. We also exercise a current RTG performance model of the General-Purpose Heat Source RTG using the JPL Lifetime Performance Prediction (LPP) tool to make top-level inferences about power output at end-of-mission, and discusses how reliability engineering and testing methods can be brought to bear to increase confidence in delivering sufficient power at end-of-mission.

Phan, Brian↗
NASA NTRS2022

Environmental Testing of Contamination Sensitive Instruments and Optics in Non-Cleanliness Controlled Facilities

The James Webb Space Telescope (JWST) Optical Telescope Element + Integrated Science Instrument Module (OTIS) went through environmental testing at the Goddard Space Flight Center. The vibration and acoustic tests occurred outside of cleanrooms. These areas are typically around ISO Class 8, far out of specifications for JWST. Through collaboration between the OTIS Mechanical and Contamination Engineering teams, a portable clean enclosure was designed, built, and verified to prevent contamination of the sensitive OTIS assembly. This manuscript describes the process of designing and building this enclosure, including materials selection and High Efficiency Particle Air (HEPA) filtration. This enclosure also provided environmental control with a portable air conditioning unit to control the relative humidity level enabling personnel to work within it. The manuscript emphasizes working in a systematic order to maintain cleanliness and integrity. “Clean as you go” during construction was a top priority to ensure that this critical Ground Support Equipment (GSE) was acceptable. The process of installing the walls and then sealing their internal and external surfaces with approved films was critical to maintaining OTIS integrity. This proved important because OTIS was in the enclosure for months longer than originally planned. This manuscript concludes by reviewing the process used to verify that the OTIS clean enclosure could achieve acceptable contamination level fallout accumulation for use during the vibration and acoustic testing and then verifying that the predicted performance was achieved after testing was complete.

Contamination Control, James Webb Space Telescope,↗
NASA NTRS2023

Fine particulate concentrations over East Asia derived from aerosols measured by the Advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.

Pm2.5↗
NASA NTRS2021

Mars 2020 Perseverance Entry Descent and Landing Simulation Statistical Analysis and Operations

On February 18, 2021, the Perseverance rover and Ingenuity helicopter demonstra- tion landed at Jezero Crater. The Entry, Descent, and Landing (EDL) architecture, largely the same used to land Curiosity at Gale Crater on August 6, 2012, required high-fidelity flight dynamics simulation with two independent tools to verify per- formance. The process for creating the EDL simulation using the Dynamics Sim- ulator for Entry, Descent and Surface landing (DSENDS) tool will be discussed, along with its use for for independent verification of the EDL statistical analy- sis results and reference trajectory simulation. Analysis and usage details both in development and cruise, along with post-landing assessment of the prediction performance of the simulation, will also be discussed.

"O'Farrell, Clara"↗
NASA NTRS2021

The Effect of Transfer Line Length and Heat Rejection Temperature Distribution on the Thales Linear Pulse Tube (LPT) Cryocoolers

Four Thales LPT9310 and four Thales LPT9510 commercial off-the-shelf (COTS) cryocoolers were parametrically performance tested. The LPT9310 coolers had transfer lines of various lengths while the LPT9510 coolers all had the same transfer line geometry. The LPT9510 test results revealed unit-tounit thermodynamic performance variation, quantified the effect of compressor temperature on thermodynamic performance and quantified the heat flow distribution between the compressor and expander as a function of their temperature distribution. The LPT9310 test results revealed the optimal drive frequency as a function of transfer line length, quantified the effect of heat rejection temperature distribution on thermodynamic performance and enabled the development and verification of a thermodynamic performance prediction tool that accounted for cold tip temperature, heat lift, heat rejection temperature and transfer line length.

Rodriguez, J.I.↗
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗
NASA NTRSDate not supplied

Validation of Universal Cryogenic Flow Boiling Correlations in Thermal Desktop for Liquid Methane and Liquid Nitrogen

Future in-space cryogenic architectures will depend on the ability to accurately model various stages of cryogenic propellant transfer and storage. Currently there is a need for more accurate, direct cryogenic data-anchored models for various boiling phenomena. This paper presents the implementation and validation of recently developed universal cryogenic flow boiling correlations for saturated flow boiling heat transfer coefficients (HTC) and critical heat flux (CHF) into the Thermal Desktop (SINDA/FLUINT) modeling software. The correlations are validated against steady state cryogenic flow boiling historical heated tube cases from liquid nitrogen and liquid methane experiments covering a wide range of inlet conditions, mass flux, and heat flux. Overall, Thermal Desktop with the new universal cryogenic flow boiling correlations demonstrates significant improvement in predictive performance for both wall temperature and CHF location compared to the Thermal Desktop model with built-in correlations when both are compared to the data.

Mariano Mercado↗
NASA NTRSDate not supplied

NavCube3-mini Lunar GNSS Receiver

This paper describes development and testing of NASA Goddard Space Flight Center’s new NavCube3-mini (NC3m) spaceborne, weak-signal GNSS receiver, which targets all Earth orbit regimes with special focus on lunar applications. NC3m derives from the ground-breaking Magnetospheric Multiscale (MMS) mission Navigator GPS receiver. The MMS-Navigator (launched 2015) holds a Guinness World Record for highest altitude GPS fix and is currently in a highly elliptic orbit with a 29 Earth radii apogee, nearly half lunar distance. NC3m has reduced size, weight, and power compared to the MMS-Navigator, making it suitable for smallsat applications, and adds multi-frequency and multi-GNSS capabilities, among other improvements. A NC3m engineering test unit was subjected to and successfully completed a comprehensive Technology Readiness Level 6 (system/subsystem model or prototype demonstration in a relevant environment) testing campaign in the second half of 2022. This involved high-fidelity simulations in LEO, GEO, and lunar regimes and full environmental testing, including vibration, thermal vacuum, and electromagnetic compatibility. An overview of the NC3m receiver, the test setup, and a sample of results is presented. The results include predicted performance at high-altitude and in the lunar regime.

NavCube3↗
NASA NTRSDate not supplied

Large-Scale Magnetic Microcalorimeter: Modeling and Multiplexing

We will present performance predictions for a large-scale magnetic microcalorimeter detector array intended for a potential future X-ray astrophysics mission. The focal plane comprises three distinct array designs, necessitating over 100,000 pixels to meet the scientific objectives. Our presentation will describe the results of numerical modeling aimed at estimating the energy resolution and detector responses for each array. Additionally, we will discuss the application of microwave multiplexing technology for the comprehensive readout of the entire sensor array.

x-ray↗
NASA NTRSDate not supplied

Coaxial Rotor CFD Validation and ML Surrogate Model Generation

A fixed-pitch speed-controlled coaxial rotor system was tested in the NASA Langley Transonic Dynamics Tunnel in September 2022. The rotors have a diameter of 1.35 meters and an inter-rotor spacing of 25% of the diameter. Though this test was focused on the NASA New Frontiers Dragonfly mission, the resulting dataset is relevant to a wide array of applications of multirotor vehicles, especially those using fixed-pitch variable-speed rotors. Most notably beyond Dragonfly, perhaps, is the application of coaxial rotor pair configurations for eVTOL and Urban Air Mobility (UAM) aircraft. This work provides a thorough CFD validation study quantifying coaxial rotor performance estimation with accuracy on order 5-10% using an efficient hybrid BEMT-URANS flow solver over a wide range of operating conditions. This accuracy was achieved using novel approaches for the construction of both the C81 airfoil performance lookup tables and the BEMT rotor model. These novel approaches are combined with advanced scripting to further accelerate the commercial off-the-shelf CFD solver on GPU accelerated machines. Finally, the development of machine learning surrogate models is presented to produce highly efficient and accurate rotor performance predictions for fixed-pitch variable-speed multirotor aircraft over the complete flight regime including scout, cruise, climb, descent, as well as limiting cases of vortex-ring and windmill-brake states.

Coaxial↗
NASA NTRSDate not supplied

Impacts of Non-Simultaneous Global Photospheric Magnetic Measurements on Coronal and Solar Wind Modeling

The intensity and arrival time of coronal mass ejections (CMEs) can be significantly influenced by the background solar wind encountered as they propagate outward from the Sun into the interplanetary medium. In addition, solar energetic particles (SEPs) race ahead of CME shock fronts and flare regions along magnetic field lines largely determined by the background solar wind. Predicting the solar wind accurately is therefore critical for improving forecasts of CMEs, SEPs, and high-speed streams. Modeling of the corona and solar wind is challenging in general, as it is highly dependent on global photospheric magnetic field maps, which serve as the boundary conditions to all coronal models that drive solar wind models. Unfortunately, less than half of the Sun’s photospheric magnetic field is reliably measured from any given vantage point and thus it is common for the maps to have highly dated and unreliable measurements in them. While Solar Obiter (SolO) now provides for the first time the opportunity to have simultaneous measurements of nearly the entire surface magnetic field of Sun (e.g., when SolO/PHI measurements are combined with those from SDO/HMI), the required alignment to accomplish this occurs only occasionally. Further, coronal models are extremely sensitive to the strengths of the polar magnetic fields of the Sun, which remain poorly observed. Recently, efforts to mitigate this problem include using flux transport models such as the Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model, which evolves the field forward in time using well known transport processes occurring on the Sun. However, it cannot account for the emergence of new magnetic flux without direct observations. The ESA VIGIL mission will eventually provide continuous observations from the L5, and the proposed SunCHASER mission will likewise do this at the L4 vantage point should it be funded. Ultimately, what is needed is a constellation of spacecraft distributed around the Sun with magnetographs that continuously measure the global surface magnetic field. In this talk, we discuss how the lack of simultaneous global measurements of the photospheric magnetic field adversely impacts the predictive performance of coronal and solar wind models.

C. Nick Arge↗
NASA NTRSDate not supplied

Applications and Development Work for Digital Image Correlation Using High Speed Cameras

NASA, Army, and industry partners are working to advance optical measurement techniques used in highly dynamic rocket engine and missile test environments. This allows for direct component data collection during test and flight environments, significantly reduced analysis time, and associated cost savings. Digital Image Correlation (DIC) uses calibrated stereo cameras to collect three-dimensional (3-D) displacements, strains, and acceleration data by referencing a stochastic speckle pattern applied to the test object. These optical measurement techniques provide significantly improved data resulting in better performance predictions, model validation, and information about components during hot-fire tests and test flights.

Rachel Paden↗
NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1

Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off-the-Shelf fan, shroud, motor, and Electronic Speed Controller, plus a custom-designed inlet bellmouth, and four experimental inlet duct acoustic liners. Fan speed and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large single-aisle aircraft. Results indicated that inlet duct acoustic liners reduced tone and broadband noise as compared to a hardwall inlet duct. Predicted performance of the honeycomb liner compared well with measurements from an array of far field microphones. Inlet acoustic liners are just one method for mitigating noise for electric ducted fans for aircraft propulsion systems. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller are presented in a separate report.

Aircraft propulsion and power↗
NASA NTRS2024

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This first release of the database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series calculations were run on the NASA High-End Compute Capability (HECC) supercomputer and the corresponding airfoil performance coefficients are embedded in the Appendix of this document for public distribution. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop OVERFLOW-quality airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. Downstream surrogate models enable OVERFLOW- quality airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle-of-attack within the bounds of the database.

Database↗
NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1

Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off-the-Shelf fan, shroud, motor, and Electronic Speed Controller, plus a custom-designed inlet bellmouth, and four experimental inlet duct acoustic liners. Fan speed and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large single-aisle aircraft. Results indicated that inlet duct acoustic liners reduced tone and broadband noise as compared to a hardwall inlet duct. Predicted performance of the honeycomb liner compared well with measurements from an array of far field microphones. Inlet acoustic liners are just one method for mitigating noise for electric ducted fans for aircraft propulsion systems. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller are presented in a separate report.

Aircraft propulsion and power↗
NASA NTRS1980

Summary of advanced methods for predicting high speed propeller performance

Three advanced analyses for predicting aircraft propeller performance at high subsonic speeds are described. Two of these analyses use a lifting line representation for the propeller blades and vortex filaments for the blade wakes but differ in the details of the solution. The third analysis is a finite difference solution of the unsteady, three dimensional Euler equations for the flow between adjacent blades. Analysis results are compared to data for a high speed propeller having eight swept blades integrally designed with the spinner and nacelle.

Bober, L. A.↗