Added Scope: Beta experiment
Beta flight test data analysis, LDV signal processing analysis and simulation, data processing code, and CESTIM, signal processing code are presented.
Source record↗
SEARCH · Engineering Papers
Results for “simulation data”
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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A new algorithm for attitude-independent magnetometer calibration
A new algorithm is developed for inflight magnetometer bias determination without knowledge of the attitude. This algorithm combines the fast convergence of a heuristic algorithm currently in use with the correct treatment of the statistics and without discarding data. The algorithm performance is examined using simulated data and compared with previous algorithms.
Alonso, Roberto↗
Orbit Determination System for Low Earth Orbit Satellites
The IAI/MBT Precise Orbit Determination system for Low Earth Orbit satellites is presented. The system is based on GPS pesudorange and carrier phase measurements and implements the Reduced Dynamics method. The GPS measurements model, the dynamic model, and the least squares orbit determination are discussed. Results are shown for data from the CHAMP satellite and for simulated data from the ROKAR GPS receiver. In both cases the one sigma 3D position and velocity accuracy is about 0.2 m and 0.5 mm/sec respectively.
Elisha, Yossi↗
Comparison of MSL RCS Jet Computations With Flow Visualization and Velocimetry
Numerical predictions of the Mars Science Laboratory (MSL) reaction control system (RCS) jets interacting with a Mach 10 hypersonic flow are compared to experimental nitric oxide (NO) planar laser-induced fluorescence (PLIF) data. The steady Reynolds Averaged Navier Stokes (RANS) equations using the Baldwin-Barth one-equation turbulence model were solved using the OVERFLOW code. The experimental PLIF data used for comparison consists of qualitative two-dimensional visualization images, qualitative reconstructed three-dimensional flow structures, and quantitative two-dimensional distributions of streamwise velocity. Through modeling of the PLIF signal equation, computational flow images (CFI) were produced and directly compared to the qualitative PLIF data. Post processing of the experimental and simulation data enabled the jet trajectory to be extracted for a more quantitative comparison. The two-dimensional velocity fields were reconstructed through interpolation of a series of single-component velocity profiles. Each distribution of single-component velocity was obtained using molecular tagging velocimetry (MTV). After validating the numerical model, the numerical solution was further examined to gain insight into hypersonic jet-in-crossflow interaction. Future NO-PLIF experiments are proposed based on this analysis.
Johansen, Craig T.↗
Rotary Balance Wind Tunnel Testing for the FASER Flight Research Aircraft
Flight dynamics research was conducted to collect and analyze rotary balance wind tunnel test data in order to improve the aerodynamic simulation and modeling of a low-cost small unmanned aircraft called FASER (Free-flying Aircraft for Sub-scale Experimental Research). The impetus for using FASER was to provide risk and cost reduction for flight testing of more expensive aircraft and assist in the improvement of wind tunnel and flight test techniques, and control laws. The FASER research aircraft has the benefit of allowing wind tunnel and flight tests to be conducted on the same model, improving correlation between wind tunnel, flight, and simulation data. Prior wind tunnel tests include a static force and moment test, including power effects, and a roll and yaw damping forced oscillation test. Rotary balance testing allows for the calculation of aircraft rotary derivatives and the prediction of steady-state spins. The rotary balance wind tunnel test was conducted in the NASA Langley Research Center (LaRC) 20-Foot Vertical Spin Tunnel (VST). Rotary balance testing includes runs for a set of given angular rotation rates at a range of angles of attack and sideslip angles in order to fully characterize the aircraft rotary dynamics. Tests were performed at angles of attack from 0 to 50 degrees, sideslip angles of -5 to 10 degrees, and non-dimensional spin rates from -0.5 to 0.5. The effects of pro-spin elevator and rudder deflection and pro- and anti-spin elevator, rudder, and aileron deflection were examined. The data are presented to illustrate the functional dependence of the forces and moments on angle of attack, sideslip angle, and angular rate for the rotary contributions to the forces and moments. Further investigation is necessary to fully characterize the control effectors. The data were also used with a steady state spin prediction tool that did not predict an equilibrium spin mode.
Denham, Casey↗
Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation
In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LIDAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA’s Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNNbased algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the statedependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver
Rahmani, Amir↗
A new calibration method for charm jet identification validated with proton-proton collision events at $\sqrt{s}$ =13 TeV
Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb$^{-1}$ at √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A Summary of Validation Studies for the Integrated TIGER Series Performed on ACORN Plus-up 218468/99
The Integrated TIGER Series (ITS) transport code is a valuable tool for photon-electron transport. A seven-problem validation suite exists to make sure that the ITS transport code works as intended. It is important to ensure that data from benchmark problems is correctly compared to simulated data. Additionally, the validation suite did not previously make use of a consistent quantitative metric for comparing experimental and simulated datasets. To this end, the goal of this long-term project was to expand the validation suite both in problem type and in the quality of the error assessment. To accomplish that, the seven validation problems in the suite were examined for potential drawbacks. When a drawback was identified, the problems were ranked based on severity of the drawback and approachability of a solution. We determined that meaningful improvements could be made to the validation suite by improving the analysis for the Lockwood Albedo problem and by introducing the Ross dataset as an eighth problem to the suite. The Lockwood error analysis has been completed and will be integrated in the future. The Ross data is unfinished, but significant progress has been made towards analysis.
97 MATHEMATICS AND COMPUTING↗
Aerosol Retrieval and Atmospheric Correction Algorithms for EPIC
EPIC is a multi-spectral imager onboard planned Deep Space Climate ObserVatoRy (DSCOVR) designed for observations of the full illuminated disk of the Earth with high temporal and coarse spatial resolution (10 km) from Lagrangian L1 point. During the course of the day, EPIC will view the same Earth surface area in the full range of solar and view zenith angles at equator with fixed scattering angle near the backscattering direction. This talk will describe a new aerosol retrieval/atmospheric correction algorithm developed for EPIC and tested with EPIC Simulator data. This algorithm uses the time series approach and consists of two stages: the first stage is designed to periodically re-initialize the surface spectral bidirectional reflectance (BRF) on stable low AOD days. Such days can be selected based on the same measured reflectance between the morning and afternoon reciprocal view geometries of EPIC. On the second stage, the algorithm will monitor the diurnal cycle of aerosol optical depth and fine mode fraction based on the known spectral surface BRF. Testing of the developed algorithm with simulated EPIC data over continental USA showed a good accuracy of AOD retrievals (10-20%) except over very bright surfaces.
Wang, Yujie↗
An Optimal Estimation Aerosol Retrieval Algorithm for Lidar-only and Combined Lidar Polarimeter Remote Sensors
Global measurements of aerosol vertical profile, composition, concentration, and size distribution are very important due to aerosol impacts on air quality, climate, clouds, and ocean ecosystems. We have developed a lidar-only and combined lidar and polarimeter algorithm to retrieve vertically-resolved profiles of aerosol microphysical properties. The new retrieval system is modular in design. It consists of three modules, a vertically-resolved aerosol profile retrieval module for lidar data, an aerosol and cloud/ocean retrieval module for polarimeter data, and a combined retrieval module for both lidar and polarimeter measurements. In addition to performing optimal estimation retrievals on various data sources, we have designed the system so that it can be used to carry out aerosol retrieval performance trade studies for various lidar and polarimeter configurations. For example, the retrieval system can take inputs from attenuated backscatter lidar at two wavelengths (e.g. CALIPSO like instrument), aerosol backscattering measured at two wavelengths and and aerosol extinction measured at one wavelength (HSRL-1), or aerosol backscattering measured at three wavelengths and aerosol extinction measured at two wavelengths (HSRL-1) (HSRL-2). We have applied the lidar-only algorithm to both simulated data and various field campaign data (DISCOVER-AQ, CHARMS, TCAP, and ORACLES). For polarimeter-only modules, we have done the same for SABOR, TCAP, NAAMES, and ORACLES data. We have applied the combined retrieval algorithm to ORACLES data. The simulated retrieval studies show that the combined lidar+polarimeter retrieval provides much higher information content relative to their individual counterparts for retrieving effective radius, particle concentrations, and absorption properties.
Xu Liu↗
In Situ Inference for Earth System Predictability
Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.
54 ENVIRONMENTAL SCIENCES↗
Quantifying Errors in 3D CME Parameters Derived from Synthetic Data Using White-Light Reconstruction Techniques
Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4° in latitude, 22° in longitude, 14° in tilt, and 10° in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model. We found the following CME parameter error bars as a starting point for quantifying the minimum error in CME parameters from white-light reconstructions: Δθ (latitude)=6° +2° -3° , Δϕ (longitude)=11° +18° -6° , Δγ (tilt)=25° +8° -7° , Δx (half-angle)=10° +12° -6° , Δh (height)=0.6 +1.2 -0.4 R ⨀ , and Δκ (ratio)=0.1 +0.03 -0.02 .
Coronal mass ejections↗
Quantifying Errors in 3D CME Parameters Derived From Synthetic Data Using White-Light Reconstruction Techniques
Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4° in latitude, 22° in longitude, 14° in tilt, and 10° in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model. We found the following CME parameter error bars as a starting point for quantifying the minimum error in CME parameters from white-light reconstructions: Δθ (latitude)=6° +2° -3° , Δϕ (longitude)=11° +18° -6° , Δγ (tilt)=25° +8° -7° , Δx (half-angle)=10° +12° -6° , Δh (height)=0.6 +1.2 -0.4 R ⨀ , and Δκ (ratio)=0.1 +0.03 -0.02 .
Coronal mass ejections↗
The Effect of Surface Heating Heterogeneity on Boundary Layer Height and Its Dependence on Background Wind Speed
The planetary boundary layer height (PBLH) is a fundamental variable of the planetary boundary layer (PBL). The effect of surface heterogeneity is challenging in the PBL parameterization for numerical weather and climate models. Here we use large eddy simulation data to investigate the impact of surface heating heterogeneity on the PBLH spatial variations and mean values over a domain of typical mesoscale model grid size. It is found that this impact depends on background wind condition. Variance decomposition and Fourier spectra both reveal that for the fully developed sheared convective PBL, surface heterogeneity contributes little to the spatial variation of PBLH, except for the case with heterogeneity scale of 14.4 km. This suggests that surface heterogeneity with length scales less than ~10 km, which is typical for mesoscale modeling, does not induce significant subgrid spatial variation of PBLH. The domain-averaged PBLH is found to generally increase as surface heterogeneity scale decreases, until reaching the scale on the order of PBLH. The findings can be used to inform the parameterization development of PBL processes over heterogeneous surface.
54 ENVIRONMENTAL SCIENCES↗
Geometric GNNs for charged particle tracking at GlueX
Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.
batched GNN pipeline↗
Gaussian process hydrodynamics
Abstract We present a Gaussian process (GP) approach, called Gaussian process hydrodynamics (GPH) for approximating the solution to the Euler and Navier-Stokes (NS) equations. Similar to smoothed particle hydrodynamics (SPH), GPH is a Lagrangian particle-based approach that involves the tracking of a finite number of particles transported by a flow. However, these particles do not represent mollified particles of matter but carry discrete/partial information about the continuous flow. Closure is achieved by placing a divergence-free GP prior ξ on the velocity field and conditioning it on the vorticity at the particle locations. Known physics (e.g., the Richardson cascade and velocity increment power laws) is incorporated into the GP prior by using physics-informed additive kernels. This is equivalent to expressing ξ as a sum of independent GPs ξ l , which we call modes, acting at different scales (each mode ξ l self-activates to represent the formation of eddies at the corresponding scales). This approach enables a quantitative analysis of the Richardson cascade through the analysis of the activation of these modes, and enables us to analyze coarse-grain turbulence statistically rather than deterministically. Because GPH is formulated by using the vorticity equations, it does not require solving a pressure equation. By enforcing incompressibility and fluid-structure boundary conditions through the selection of a kernel, GPH requires significantly fewer particles than SPH. Because GPH has a natural probabilistic interpretation, the numerical results come with uncertainty estimates, enabling their incorporation into an uncertainty quantification (UQ) pipeline and adding/removing particles (quanta of information) in an adapted manner. The proposed approach is suitable for analysis because it inherits the complexity of state-of-the-art solvers for dense kernel matrices and results in a natural definition of turbulence as information loss. Numerical experiments support the importance of selecting physics-informed kernels and illustrate the major impact of such kernels on the accuracy and stability. Because the proposed approach uses a Bayesian interpretation, it naturally enables data assimilation and predictions and estimations by mixing simulation data and experimental data.
Mathematics↗
Observing correlations between dark matter accretion and galaxy growth: II. testing the impact of galaxy mass, star formation indicator, and neighbour colours
A crucial question in galaxy formation is what role new accretion has in star formation. Theoretical models have predicted a wide range of correlation strengths between halo accretion and galaxy star formation. Previously, we presented a technique to observationally constrain this correlation strength for isolated Milky Way mass galaxies at z ~ 0.12, based on the correlation between halo accretion and the density profile of neighbouring galaxies. By applying this technique to both observational data from the Sloan Digital Sky Survey and simulation data from the UNIVERSEMACHINE , where we can test different correlation strengths, we ruled out positive correlations between dark matter accretion and recent star formation activity. In this work, we expand our analysis by (1) applying our technique separately to red and blue neighbouring galaxies, which trace different infall populations, (2) correlating dark matter accretion rates with D n 4000 measurements as a longer-term quiescence indicator than instantaneous star-formation rates, and (3) analysing higher-mass isolated central galaxies with 10 11.0 < M * /M ⊙ < 10 11.5 out to z ~ 0.18. Finally, in all cases, our results are consistent with non-positive correlation strengths with ≳ 85 percent confidence, which is most consistent with models where processes such as gas recycling dominate star formation in massive z = 0 galaxies.
79 ASTRONOMY AND ASTROPHYSICS↗
Facilitating Staging-based Unstructured Mesh Processing to Support Hybrid In-Situ Workflows
In-situ and in-transit processing alleviate the gap between the computing and I/O capabilities by scheduling data analytics close to the data source. Hybrid in-situ processing splits data analytics into two stages: the data processing that runs in-situ aims to extract regions of interest, which are then transferred to staging services for further in-transit analytics. To facilitate this type of hybrid in-situ processing, the data staging service needs to support complex intermediate data representations generated/consumed by the in-situ tasks. Unstructured (or irregular) mesh is one such derived data representation that is typically used and bridges simulation data and analytics. However, how staging services efficiently support unstructured mesh transfer and processing remains to be explored. This paper investigates design options for transferring and processing unstructured mesh data using staging services. Using polygonal mesh data as an example, we show that hybrid in-situ workflows with staging-based unstructured mesh processing can effectively support hybrid in-situ workflows, and can significantly decrease data movement overheads.
data-driven↗