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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2017

Lessons Learned from Inlet Integration Analysis of NASA's Low Boom Flight Demonstrator

In 2016, NASA's Aeronautics Research Mission Directorate announced the New Aviation Horizons Initiative with a goal of designing/building several X-Planes, including a Low Boom Flight Demonstrator (LBFD). That same year, NASA awarded a contract to Lockheed Martin (LM) to advance the LBFD concept through preliminary design. Several configurations of the LBFD aircraft were analyzed by both LM engineers and NASA researchers. This presentation focuses on some of the CFD simulations that were run by NASA Glenn researchers. NASA's FUN3D V13.1 code was used for all adjoint-based grid refinement studies and Spalart-Allmaras turbulence model was used during adaptation. It was found that adjoint-based grid adaptation did not accurately capture inlet performance for high speed top-aft-mounted propulsion.

Adjoint-based Grid Adaptatio↗
NASA NTRS2003

Flight Test of an Intelligent Flight-Control System

The F-15 Advanced Controls Technology for Integrated Vehicles (ACTIVE) airplane (see figure) was the test bed for a flight test of an intelligent flight control system (IFCS). This IFCS utilizes a neural network to determine critical stability and control derivatives for a control law, the real-time gains of which are computed by an algorithm that solves the Riccati equation. These derivatives are also used to identify the parameters of a dynamic model of the airplane. The model is used in a model-following portion of the control law, in order to provide specific vehicle handling characteristics. The flight test of the IFCS marks the initiation of the Intelligent Flight Control System Advanced Concept Program (IFCS ACP), which is a collaboration between NASA and Boeing Phantom Works. The goals of the IFCS ACP are to (1) develop the concept of a flight-control system that uses neural-network technology to identify aircraft characteristics to provide optimal aircraft performance, (2) develop a self-training neural network to update estimates of aircraft properties in flight, and (3) demonstrate the aforementioned concepts on the F-15 ACTIVE airplane in flight. The activities of the initial IFCS ACP were divided into three Phases, each devoted to the attainment of a different objective. The objective of Phase I was to develop a pre-trained neural network to store and recall the wind-tunnel-based stability and control derivatives of the vehicle. The objective of Phase II was to develop a neural network that can learn how to adjust the stability and control derivatives to account for failures or modeling deficiencies. The objective of Phase III was to develop a flight control system that uses the neural network outputs as a basis for controlling the aircraft. The flight test of the IFCS was performed in stages. In the first stage, the Phase I version of the pre-trained neural network was flown in a passive mode. The neural network software was running using flight data inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

Davidson, Ron↗
NASA NTRS1991

Neural Architectures for Control

The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

Peterson, James K.↗
NASA NTRS2017

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Subpixel↗
NASA NTRS2019

European Service Module - Structural Test Article (E-STA) Building Block Test Approach and Model Correlation Observations

The Orion European Service Module - Structural Test Article (E-STA) underwent sine vibration testing in 2016 using the Mechanical Vibration Facility (MVF) multi-axis shaker system at NASA Glenn Research Center’s (GRC) Plum Brook Station (PBS) Space Power Facility (SPF). The main objective was to verify the structural integrity of the European Service Module (ESM) under sine sweep dynamic qualification vibration testing. A secondary objective was to perform a fixed-base modal survey, while E-STA was still mounted to MVF, in order to achieve a test correlate the finite element model (FEM). To facilitate the E-STA system level correlation effort, a building block test approach was implemented. Modal tests were performed on two major subassemblies, the crew module/launch abort structure (CM/LAS) and the crew module adapter (CMA) mass simulators. These subassembly FEMs were individually correlated and then integrated into the E-STA FEM prior to the start of the E-STA sine vibration test. This paper summarizes the modal testing and model correlation efforts of both of these subassemblies and how the building block approach assisted in the overall correlation of the E-STA FEM. This paper will also cover modeling practices that should be avoided, recommended instrumentation positioning on complex structures, and the importance of the FEM geometrically matching CAD in sufficient detail in order to adequately replicate internal load paths. The goal of this paper is to inform the reader of the hard earned lessons learned and pitfalls to avoid when applying a building block test approach.

Winkel, James P.↗
NASA NTRS2001

Modal Testing of Seven Shuttle Cargo Elements for Space Station

From December 1996 to May 2001, the Modal and Control Dynamics Team at NASA's Marshall Space Flight Center (MSFC) conducted modal tests on seven large elements of the International Space Station. Each of these elements has been or will be launched as a Space Shuttle payload for transport to the International Space Station (ISS). Like other Shuttle payloads, modal testing of these elements was required for verification of the finite element models used in coupled loads analyses for launch and landing. The seven modal tests included three modules - Node, Laboratory, and Airlock, and four truss segments - P6, P3/P4, S1/P1, and P5. Each element was installed and tested in the Shuttle Payload Modal Test Bed at MSFC. This unique facility can accommodate any Shuttle cargo element for modal test qualification. Flexure assemblies were utilized at each Shuttle-to-payload interface to simulate a constrained boundary in the load carrying degrees of freedom. For each element, multiple-input, multiple-output burst random modal testing was the primary approach with controlled input sine sweeps for linearity assessments. The accelerometer channel counts ranged from 252 channels to 1251 channels. An overview of these tests, as well as some lessons learned, will be provided in this paper.

Kappus, Kathy O.↗
NASA NTRS1993

Cirrus cloud development in a mobile upper tropospheric trough: The November 26th FIRE cirrus case study

The period from 18 UTC 26 Nov. 1991 to roughly 23 UTC 26 Nov. 1991 is one of the study periods of the FIRE (First International Satellite Cloud Climatology Regional Experiment) 2 field campaign. The middle and upper tropospheric cloud data that was collected during this time allowed FIRE scientists to learn a great deal about the detailed structure, microphysics, and radiative characteristics of the mid latitude cirrus that occurred during that time. Modeling studies that range from the microphysical to the mesoscale are now underway attempting to piece the detailed knowledge of this cloud system into a coherent picture of the atmospheric processes important to cirrus cloud development and maintenance. An important component of the modeling work, either as an input parameter in the case of cloud-scale models, or as output in the case of meso and larger scale models, is the large scale forcing of the cloud system. By forcing we mean the synoptic scale vertical motions and moisture budget that initially send air parcels ascending and supply the water vapor to allow condensation during ascent. Defining this forcing from the synoptic scale to the cloud scale is one of the stated scientific objectives of the FIRE program. From the standpoint of model validation, it is also necessary that the vertical motions and large scale moisture budget of the case studies be derived from observations. It is considered important that the models used to simulate the observed cloud fields begin with the correct dynamics and that the dynamics be in the right place for the right reasons.

Mace, Gerald G.↗
NASA NTRSDate not supplied

Communication Demand in the National Airspace - A Federated Learning Approach

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc.). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRSDate not supplied

A Federated Learning Approach to Predicting Communication Demand in the National Airspace

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRS2015

Assessment of Slat Noise Predictions for 30P30N High-Lift Configuration From BANC-III Workshop

This paper presents a summary of the computational predictions and measurement data contributed to Category 7 of the 3rd AIAA Workshop on Benchmark Problems for Airframe Noise Computations (BANC-III), which was held in Atlanta, GA, on June 14-15, 2014. Category 7 represents the first slat-noise configuration to be investigated under the BANC series of workshops, namely, the 30P30N two-dimensional high-lift model (with a slat contour that was slightly modified to enable unsteady pressure measurements) at an angle of attack that is relevant to approach conditions. Originally developed for a CFD challenge workshop to assess computational fluid dynamics techniques for steady high-lift predictions, the 30P30N configurations has provided a valuable opportunity for the airframe noise community to collectively assess and advance the computational and experimental techniques for slat noise. The contributed solutions are compared with each other as well as with the initial measurements that became available just prior to the BANC-III Workshop. Specific features of a number of computational solutions on the finer grids compare reasonably well with the initial measurements from FSU and JAXA facilities and/or with each other. However, no single solution (or a subset of solutions) could be identified as clearly superior to the remaining solutions. Grid sensitivity studies presented by multiple BANC-III participants demonstrated a relatively consistent trend of reduced surface pressure fluctuations, higher levels of turbulent kinetic energy in the flow, and lower levels of both narrow band peaks and the broadband component of unsteady pressure spectra in the nearfield and farfield. The lessons learned from the BANC-III contributions have been used to identify improvements to the problem statement for future Category-7 investigations.

Choudhari, Meelan↗
NASA NTRS2018

NASA ACC High Energy Dynamic Impact Methodology and Outcomes

For High Energy Dynamic Impact (HEDI) events, testing to evaluate the structural response of primary aircraft structure for design and certification is both expensive and time consuming. This paper discusses current work seeking to assess, develop, and validate appropriate analytical models that accurately predict physical response, damage, and failure modes for large scale composite structures in HEDI events. Four state-of-the-art Progressive Damage Analysis (PDA) methods were employed for this phased project: LS-DYNA MAT162, LS-DYNA MAT261, Smoothed Particle Galerkin (SPG), and EMU Peridynamics. Multiple material systems were considered, namely T700/5208 textile-infusion triaxial braid, T800/AMD-825 textile-infusion triaxial braid, IM7/8552 uni-directional tape, and SPG 196-PW/8552 plain-weave fabric. Extensive ballistic impact testing was performed to support this activity and measured results were compared to predictive models for assessment using panel delamination, panel displacement, force at the load cells, and threshold velocity as measures. Ultimately, the work under this activity provided significant progress in advancing the state-of-the-art in the use of PDA for HEDI events. Each material model had favorable performance comparing to test in some parameters and needed improvement in others. With the lessons learned from this activity, significant progress was made in the ability to predict panel behavior for a more general case beyond the flat panel in a ballistic impact event. Subsequent Phase II of the NASA ACC HEDI effort will continue to build on the coupon testing, flat panel ballistic impact testing, and analysis performed to-date with application of the PDA methods for intended material selections to test articles with greater complexity of configuration, curvature, and scale. It is not the intention of this paper to present a full set of data, but rather to give an overview of the NASA HEDI effort and show a small representative subset of the test and analysis results.

Hunziker, Kenneth J.↗
NASA NTRS2002

SHADOZ (Southern Hemisphere ADditional Ozonesondes): What Have We Learned About Tropical Tropospheric Ozone from the First Three Years' (1998-2000) Data?

The first climatological overview of total, stratospheric and tropospheric ozone in the southern hemisphere tropical and subtropics is based on ozone sounding data from 10 sites comprising the Southern Hemisphere Additional OZonesondes (SHADOZ) network. The period covered is 1998-2000. Observations were made over: Ascension Island; Nairobi, Kenya; Irene, South Africa; RCunion Island; Watukosek, Java; Fiji; Tahiti; American Samoa; San Cristobal, Galapagos; Natal, Brazil. Campaign data were collected on a trans-Atlantic oceanographic cruise and during SAFARI-2000 in Zambia. The ozone data, with simultaneous temperature profiles to approx. 7 hPa and relative humidity to approx. 200 hPa, reside at: . SHADOZ ozone time-series and profiles give a perspective on tropical total, stratospheric and tropospheric ozone in 1998-2000. Prominent features are highly variable tropospheric ozone, a zonal wave-one pattern in total (and tropospheric) column ozone, and signatures of the Quasi-Biennial Oscillation (QBO) in stratospheric ozone. Total, stratospheric and tropospheric column ozone amounts peak between August and November and are lowest between March and May. Tropospheric ozone variability over the Indian and Pacific Ocean displays influences of the Indian Ocean Dipole, and convective mixing. Pollution transport from Africa, South American and the Maritime Continent is a seasonal feature. Tropospheric ozone seasonality over the Atlantic Basin shows effects of regional subsidence and recirculation as well as biomass burning. Dynamical and chemical influences appear to be of comparable magnitude though model studies are needed to quantify this.

Thompson, Anne M.↗
NASA NTRS2002

SHADOZ (Southern Hemisphere ADditional Ozonesondes}: What Have We Learned About Tropical Tropospheric Ozone from the First Three Years (1998-2000) Data

The first climatological overview of total, stratospheric and tropospheric ozone in the southern hemisphere tropical and subtropics is based on ozone sounding data from 10 sites comprising the Southern Hemisphere Additional OZonesondes (SHADOZ) network. The period covered is 1998-2000. Observations were made over: Ascension Island; Nairobi, Kenya; Irene, South Africa; Reunion Island; Watukosek, Java; Fiji; Tahiti; American Samoa; San Cristobal, Galapagos; Natal, Brazil. Campaign data were collected on an Trans-Atlantic oceanographic cruise and during SAFARI-2000 in Zambia. The ozone data, with simultaneous temperature profiles to approximately 7 hPa and relative humidity to approximately 200 hPa, reside at: . SHADOZ ozone time-series and profiles give a perspective on tropical total, stratospheric and tropospheric ozone in 1998-2000. Prominent features are highly variable tropospheric ozone, a zonal wave-one pattern in total (and tropospheric) column ozone, and signatures of the Quasi-Biennial Oscillation (QBO) in stratospheric ozone. Total, stratospheric and tropospheric column ozone amounts peak between August and November and are lowest between March and May. Tropospheric ozone variability over the Indian and Pacific Ocean displays influences of the Indian Ocean Dipole, and convective mixing. Pollution transport from Africa, South American and the Maritime Continent is a seasonal feature. Tropospheric ozone seasonality over the Atlantic Basin shows effects of regional subsidence and recirculation as well as biomass burning. Dynamical and chemical influences appear to be of comparable magnitude though model studies are needed to quantify this.

Thompson, Anne M.↗
NASA NTRS2018

Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.

Park, Thomas↗
NASA NTRS1995

Locomotion training of legged robots using hybrid machine learning techniques

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results.

Simon, William E.↗
NASA NTRS2001

Twenty Years of Radiation Measurements in Low-Earth Orbit - What Have We Learned Space Radiation Environment?

The advent of the Space Shuttle program has made possible space radiation environment measurements spanning a wide range of altitudes and orbital inclinations over multiple solar cycles. These measurements range from routine integral dose measurements with thermoluminescent dosimeters to particle energy spectra measurements made with a charged particle telescope. This paper will review the new understanding about the space radiation environment gained from this diverse data set. Major findings from these measurements include: estimations of the westward drift rate of the South Atlantic Anomaly (SAA) of 0.28-0.49/y; evidence for a northward component to the SAA drift of 0.08-0.12/y; observation of the formation and decay of the pseudo-stable additional radiation belt following the Mar 1991 SPE and geomagnetic storm with an estimated decay e-folding time of 9-10 months; observation of a local geomagnetic east-west trapped proton exposure anisotropy with an estimated magnitude of 1.6-3.3; demonstration that the trapped proton exposure in low-Earth orbit (LEO) can be reasonably modeled as a power law function of atmospheric density in the SAA region, with best correlations obtained when the exospheric temperature saturates at 938-975 K; the actual solar cycle modulation of trapped proton exposure in LEO is less than predicted by the AP8 model; and the testing and validation of GCR flux models, radiation transport codes, and dynamic geomagnetic cutoff models. Long-term, time-resolved proportional counter measurements made aboard the Mir during the same period provides further demonstration of the solar cycle modulation of the trapped protons at low altitudes - the observed modulation is also well described as power law function of atmospheric density. These data and findings have helped to improve the overall accuracy of pre-mission crew exposure projections using various semi-empirical space environment models, radiation transport codes, and spacecraft radiation shielding models. During the rise phase of solar cycle 22 (1987-1991), the RMS error between preflight exposure projections and measured crew exposure was 73%. For the rise phase of cycle 23 (1997-2001), the preflight exposure projection RMS error has decreased to 23%. The launch and assembly of the Space Station has begun a new era of long-term LEO space environment monitoring. The radiation environment at the Space Station will be monitored with three external charged particle telescopes oriented in the velocity vector, anti-velocity vector, and zenith directions. Data from the telescopes will provide charge, mass, energy, and arrival direction for incident particles with energy to mass ratios of 13- 450 MeV/amu and Z of 1-24. The external environment data will be complimented by measurements from a portable charged particle telescope and proportional counter located inside the vehicle.

Golightly, Michael J.↗
NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗
NASA NTRSDate not supplied

Ensemble Forecasting/Assimilation/Emissions Estimation with WRF-Chem/DART: Accomplishments, Lessons Learned, and Future Plans

Over the past ten years, we have been conducting research on regional ensemble atmospheric composition forecasting/data assimilation/emissions estimation with WRF-Chem/DART. WRF- Chem/DART integrates the Weather Research and Forecasting model (WRF) with online chemistry (WRF-Chem) into the Data Assimilation Research Testbed (DART). DART is an ensemble data assimilation system based on the ensemble adjustment Kalman filter (EAKF) with adaptive inflation, localization (physical and state space), and an optional non-Gaussian formulation of the EAKF. DART includes assimilation of meteorological and limited chemical observations. WRF-Chem/DART extends DART to include assimilation of: MOPITT CO; IASI CO and O3; MODIS AOD; OMI O3, NO2, and SO2; TROPOMI CO, O3, NO2, and SO2, TES CO, CO2 (research mode), O3, NH3, and CH4 (research mode); CrIS CO, O3, NH3, CH4 (research mode), and PAN; SCIAMACHY NO2; GOME2a NO2; MLS O3 and HNO3; and proxy TEMPO O3, and NO2 satellite retrievals as raw retrievals or as ‘compact phase space retrievals’ (CPSRs) for profile retrievals. WRF-Chem/DART also assimilates in situ atmospheric composition measurements and uses the ‘state augmentation method’ for emissions estimation. In our presentation, we will provide an overview of WRF-Chem/DART: • applications and results; • lessons learned from: (i) independent versus joint assimilation; (ii) total/partial column versus profile retrieval assimilation; (iii) joint in situ and retrieval assimilation; (iv) assimilation at grid resolutions ranging from 100 km to 4 km; (v) dynamic emissions estimation; and • future work related to wildfire emissions estimation and intercomparison of CMAQ (online)/JEDI and CMAQ (online and offline)/DART.

WRF-Chem/DART↗