Rosman I dynamic analysis.
Rosman I reflector antenna for collecting data from earth orbiting satellites, discussing dynamic analysis of structural response to natural frequencies
Butler, T. G.↗
SEARCH · Engineering Papers
Results for “dynamic data collection”
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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The TIROS-N high resolution infrared radiation sounder
The high-resolution infrared radiation sounder (HIRS/2) was developed and flown on the Television and Infrared Observation Satellite, N Series (TIROS-N) as one means of obtaining atmospheric vertical profile information. The HIRS/2 receives visible and infrared spectrum radiation through a single telescope and selects 20 narrow spectral channels by means of a rotating filter wheel. A passive radiant cooler provides an operating temperature of 106.7K for the HgCdTe and InSb detectors while the visible detector operates at instrument frame temperature. Low noise amplifiers and digital processing provide 13 bit data for spacecraft data multiplexing and transmission. The qualities of system performance that determine sounding capability are the dynamic range of data collection, the noise equivalent radiance of the system, the registration of the air columns sampled in each channel, and the ability to upgrade the calibration of the instrument to maintain the performance standard throughout life. The basic performance of the instrument in test is described. Early orbital information from the TIROS-N launched on October 13, 1978 are given and some observations on system quality are made.
Koenig, E. W.↗
Performance of the HIRS/2 instrument on TIROS-N
The High Resolution Infrared Radiation Sounder (HIRS/2) was developed and flown on the TIROS-N satellite as one means of obtaining atmospheric vertical profile information. The HIRS/2 receives visible and infrared spectrum radiation through a single telescope and selects 20 narrow radiation channels by means of a rotating filter wheel. A passive radiant cooler provides an operating temperature of 106.7 K for the HgCdTe and InSb detectors while the visible detector operates at instrument frame temperature. Low noise amplifiers and digital processing provide 13 bit data for spacecraft data multiplexing and transmission. The qualities of system performance that determine sounding capability are the dynamic range of data collection, the noise equivalent radiance of the system, the registration of the air columns sampled in each channel and the ability to upgrade the calibration of the instrument to maintain the performance standard throughout life. The basic features, operating characteristics and performance of the instrument in test are described. Early orbital information from the TIROS-N launched on October 13, 1978 is given and some observations on system quality are made.
Koenig, E. W.↗
Photo Doppler Velocimetry and Gamma/Neutron Yield Measurements of Godiva-IV Critical Assembly
The purpose of this experiment was to collect data for a dynamic benchmark of the Godiva-IV burst assembly. Data were taken using two different measurement systems. The gamma & neutron flux was measured using a calibrated, high-dynamic-range scintillator detector, the MHD-240. The measurement will be used to determine the total fission yield and alpha throughout the burst and shutdown sequence of Godiva-IV. The Photon Doppler Velocimetry (PDV) measurement detected the surface motion of the burst assembly as a function of time. The combined data will be used in a multi-physics simulation of the Godiva-IV burst assembly.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Interpretable Deep Learning for the Earth System with Fractal Nets
Focal Area 3: Explainable AI Our confidence in the projections made by Earth System Models (ESMs) depends on understanding them to be, in some important respects, faithful representations of the Earth system. Here we present an “explainable Artificial Intelligence (AI)” method that allows us to uncover the dynamical structure of the observed and modeled Earth system, discover hidden links across wide spatiotemporal scales, target model development efforts at poorly-represented dynamics, and optimize observed or modeled data collection to maximize predictive information. Science Challenge: Dynamical system science for the Earth system poses unique challenges given the large degree of internal climate variability. Thus, tools that help us understand how ESMs succeed and fail at representing these dynamics are crucial, particularly in relation to the observed system. Furthermore, the computational and memory constraints on ESM data output motivate in situ analysis of ESM dynamics, including automatic detection of dynamical shifts. Also, of key importance are procedures that leverage ESMs to optimize observational campaigns for improving process representation, reducing structural uncertainty and improving model skill.
54 ENVIRONMENTAL SCIENCES↗
Adaptive Dynamic Digital Twin for Test Scenario Generation
Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.
Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗
Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy
X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.
Bayesian optimization↗
Understanding collective human movement dynamics during large-scale events using big geosocial data analytics
Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.
54 ENVIRONMENTAL SCIENCES↗
Development of Unsteady Pressure-Sensitive Paint Application on NASA Space Launch System
The key measurement to acquire for understanding unsteady flow is surface pressure. Unsteady Pressure-Sensitive Paint (uPSP) is an emerging optical technique used in wind tunnel testing to measure fluctuating surface pressures. Recently, tests were conducted on NASA’s Space Launch System in NASA Ames Research Center’s Unitary Plan Wind Tunnel to determine the aeroacoustics environment and assist in developing the buffet forcing functions. Unsteady PSP data was collected during this test campaign. Steady state PSP data, infrared thermography, shadowgraph, accelerometer data, and dynamic pressure transducer data were also collected. In all 50 TB of data were collected during the three days of testing. During these three days of testing, a repeating transonic and supersonic alpha sweep condition was acquired. This paper presents these two wind tunnel conditions and examines how the temperature influences the PSP data. In the first large demonstration of uPSP in 2015 on an NESC-, AETC-sponsored wind tunnel test, lifetime PSP results highlighted the influence the model temperature had on the PSP data. A best practice of heat soaking the model before acquiring calibration images was followed during the test campaign presented in this paper. An infrared thermography camera and thermocouples were installed in the model to collect more details of the model surface temperature. Data processing schemes for uPSP are still in development but will be briefly presented here as well.
Roozeboom, Nettie H.↗
Magnetospheric Multiscale Mission Attitude Dynamics: Observations from Flight Data
Extensive flight data is being collected throughout the MMS mission that includes quantities that are of interest for attitude dynamics studies such as spin rate, spin axis orientation nutation rate, etc. One example of such data is the long-term evolution of the spin rates of the four spacecraft. Spikes in these rates are observed that are separated by the MMS orbital period (just under 24 hr) and occur around perigee due to gravity-gradient torque. Periodic discontinuities in spin rate are caused by the controller resetting the spin rate approximately to the nominal 3.1 RPM value at the time of each maneuver. In between, a slow decay in spin rate can be seen to occur. The paper will discuss various disturbance torque mechanisms that could potentially be responsible for this behavior: these include magnetic hysteresis, eddy currents, solar radiation pressure, and a possible interaction between gravity-gradient and wire boom flexibility effects. One additional disturbance mechanism is produced by the Active Spacecraft Potential Control (ASPOC) devices: these emit positive indium ions to keep the MMS spacecraft electrically neutral, so as not to corrupt the electric field observations that are made by some of the on-board instruments. The spin rate decays that could be produced by these various mechanisms will be quantified in the paper, and their signatures described. Comparing these with the observations from flight data then allow the most likely candidate to be determined.
DYNAMICS↗
Plume and Shock Interaction Effects on Sonic Boom in the 1-foot by 1-foot Supersonic Wind Tunnel
The desire to reduce or eliminate the operational restrictions of supersonic aircraft over populated areas has led to extensive research at NASA. Restrictions are due to the disturbance of the sonic boom, caused by the coalescence of shock waves formed by the aircraft. A study has been performed focused on reducing the magnitude of the sonic boom N-wave generated by airplane components with a focus on shock waves caused by the exhaust nozzle plume. Testing was completed in the 1-foot by 1-foot supersonic wind tunnel to study the effects of an exhaust nozzle plume and shock wave interaction. The plume and shock interaction study was developed to collect data for computational fluid dynamics (CFD) validation of a nozzle plume passing through the shock generated from the wing or tail of a supersonic vehicle. The wing or tail was simulated with a wedgeshaped shock generator. This test entry was the first of two phases to collect schlieren images and off-body static pressure profiles. Three wedge configurations were tested consisting of strut-mounted wedges of 2.5- degrees and 5-degrees. Three propulsion configurations were tested simulating the propulsion pod and aft deck from a low boom vehicle concept, which also provided a trailing edge shock and plume interaction. Findings include how the interaction of the jet plume caused a thickening of the shock generated by the wedge (or aft deck) and demonstrate how the shock location moved with increasing nozzle pressure ratio.
Shock wave interaction↗
Multi-channel Scaler Cards Improve Data Collection
Scientists interested in exploring the intricacies and dynamics of Earth's climate and ecosystems continually need smaller, lighter instrumentation that can be placed onboard various sensing platforms, such as Unmanned Aerial Vehicles (UAVs). Responding to a need for improved data collection for remote atmospheric measurement systems, ASRC Aerospace Corporation, of Greenbelt, Maryland, developed a series of low-power, highly integrated, multichannel scaler (MCS) cards. The cards were designed to meet the needs of NASA's ground-based and airborne Light Detection and Ranging (LIDAR) photoncounting programs. They can rapidly collect thousands of data points during a continuous scan of the atmosphere.
Source record↗
Development of Biocompatibility Procedures for Assessment of Plant Growth in Ground Test Hardware for the EMCS
The European Modular Cultivation System (EMCS) is an European Space Agency-developed facility designed to support plant research in microgravity on the IS NASA is responsible for providing US specific hardware to use within the EMCS. In preparation for flight, research will be developed and tested at Ames Research Center in the EMCS ground test hardware, the Experiment Reference Module (ERM) In order to determine the acceptability of the ERM for such purposes, biocompatibility tests will be performed to determine that the hardware functions as intended and successfully supports the' growth of plants. In this report, we describe the development of procedures and the collection of baseline data against which to compare ERM function, e.g. biocompatibility testing. A simple and robust system was developed to grow whole Arabidopsis thaliana plants within the confined volumes characteristic of spaceflight hardware. Our system for growing plants eliminated the necessity of a water/nutrient delivery system and allowed for quantifiable assessment of individual plants, as well as entire population dynamics. To insure uniform germination, seeds were started in small straw segments and transplanted into modified scintillation vials. Seedlings were selected prior to transplantation to decrease genetic variability. Plants were grown for a total of 24 days in standard laboratory plant growth chambers under controlled conditions. Sequential digital still images were taken on a daily basis. Analysis of these images allowed for the quantification of even minute environmental effect, on growth dynamics whole plants. The data collected provide reliable growth curves against which to compare plants grown in the ERM.
Bowman, R. N.↗
Dataset for "A primer on forest structure measurement with lidar for ecologists"
This repository includes data and code accompanying the case study included in the manuscript "A primer on forest structure measurement with lidar for ecologists" (submitted to Ecosphere). We compiled lidar datasets from multiple platforms in a common area to: 1. Demonstrate how differences in sensor characteristics influence density and resolution of lidar data. 2. Provide open-source, co-located datasets for users to further inspect differences in lidar data. 3. Provide example code to perform basic lidar analysis. This case study is meant to allow readers to get hands-on experience with real-world data from different platforms. This case study is not meant to be a rigorous comparison of derived ecological metrics among all sensors; such comparisons can be found throughout other publications referenced throughout the main manuscript. Code includes basic functions in R commonly used to visualize and manipulate lidar data accessible with a normal laptop computer; more sophisticated algorithms for advanced users are also referenced throughout the main manuscript. Terrestrial laser scanning (TLS), mobile laser scanning (MLS), UAS laser scanning (ULS), airborne laser scanning (ALS), and spaceborne laser scanning (SLS) data were collected within the Smithsonian Environmental Research Center (SERC) forest dynamics plot in Maryland, USA. TLS, MLS, and ALS data were collected within 1 month of the 2021 growing season; ULS data were collected in November 2020 (“leaf-off” data) and July 2022 (“leaf-on” data).
54 ENVIRONMENTAL SCIENCES↗
Forced synchronization of autonomous dynamical Boolean networks
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset
Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Libpanda: A High Performance Library for Vehicle Data Collection
Cyber-Physical Systems (CPS) generally involve time-critical components due to physical dynamics, therefore necessitating high-performance subsystems. This is also true in data collection scenarios to infer physical phenomena. This paper covers Libpanda as an example of a component that has been designed to address performance issues in CPS implementations. Libpanda is a C++ library that interfaces software with a Comma.ai Panda device. Pandas are used for installation in modern vehicles to read the vehicle CAN bus, providing rich sensor data and limited vehicle control through message injection. The motivation to design lib-panda stems from the lack of performance in Python-based code that runs on inexpensive hardware like a Raspberry Pi. In such situations, Python code would result in utilizing 92% CPU while also dropping around 40% of the CAN packet due to bottlenecks. Without using different tools, inconsistent data collection means a loss of time-based vehicle state interpretation. Libpanda addresses these issues through implementation in a different language and implementation of different design paradigms involving asynchronous calls and multithreading. The Panda also features a GPS module that allows multiple instances to synchronize clocks for large-scale data collection scenarios. Libpanda has been designed with time-synchronization in mind to aid in the measurement of inter-vehicle dynamics. The performance improvements of libpanda have resulted in it becoming an important component in automotive dynamics research that requires a higher technical performance in large-scale experiments.
Bunting, Matthew↗
Powerpack Testing of the J2-X Oxidizer Turbopump
The J-2X PPA-2 (Powerpack 2) test series was conducted from February through December 2012 on the A1 test stand at NASA Stennis Space Center in a joint effort between Pratt and Whitney Rocketdyne (PWR) and NASA Marshall Space Flight Center (MSFC). The series consisted of 13 tests for a total hotfire duration close to 6500 seconds. Among the chief test objectives was characterization of the capabilities of the new J-2X turbopumps. This paper concentrates on the test results pertaining specifically to the Oxidizer Turbopump (OTP) operation in liquid oxygen (LOX). The two bladed inducer configuration that was tested had a 0.81% blade height tip clearance and incorporated the B-groove modification to the inducer tunnel outboard of the inducer leading edge. Data was collected on inducer suction performance, pump radial loading, and the pump dynamic environment in LOX. Comparisons were made to prior data collected on two geometrically similar subscale inducers tested in water at NASA MSFC (70% scale) and Concepts NREC (52% scale). In overview, the results of the powerpack testing were consistent with the radial load assessment from the water tests. The inducer performed differently in the other two categories, however. The inducer suction performance capability in LOX was notably improved. This was expected and is probably attributable to thermal suppression head (TSH) effects. While the dynamic environment was similar in most aspects to water test observations, the higher order cavitation (HOC) during the powerpack testing was much more benign.
Jambusaria, Mitul↗