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NASA NTRS2018

Intra-EVA Space-to-Ground Interactions when Conducting Scientific Fieldwork Under Simulated Mars Mission Constraints

The Biologic Analog Science Associated with Lava Terrains (BASALT) project is a four-year program dedicated to iteratively designing, implementing, and evaluating concepts of operations (ConOps) and supporting capabilities to enable and enhance scientific exploration for future human Mars missions. The BASALT project has incorporated three field deployments during which real (non-simulated) biological and geochemical field science have been conducted at two high-fidelity Mars analog locations under simulated Mars mission conditions, including communication delays and data transmission limitations. BASALT's primary Science objective has been to extract basaltic samples for the purpose of investigating how microbial communities and habitability correlate with the physical and geochemical characteristics of chemically altered basalt environments. Field sites include the active East Rift Zone on the Big Island of Hawai'i, reminiscent of early Mars when basaltic volcanism and interaction with water were widespread, and the dormant eastern Snake River Plain in Idaho, similar to present-day Mars where basaltic volcanism is rare and most evidence for volcano-driven hydrothermal activity is relict. BASALT's primary Science Operations objective has been to investigate exploration ConOps and capabilities that facilitate scientific return during human-robotic exploration under Mars mission constraints. Each field deployment has consisted of ten extravehicular activities (EVAs) on the volcanic flows in which crews of two extravehicular and two intravehicular crewmembers conducted the field science while communicating across time delay and under bandwidth constraints with an Earth-based Mission Support Center (MSC) comprised of expert scientists and operators. Communication latencies of 5 and 15 min one-way light time and low (0.512 Mb/s uplink, 1.54 Mb/s downlink) and high (5.0 Mb/s uplink, 10.0 Mb/s downlink) bandwidth conditions were evaluated. EVA crewmembers communicated with the MSC via voice and text messaging. They also provided scientific instrument data, still imagery, video streams from chest-mounted cameras, GPS location tracking information. The MSC monitored and reviewed incoming data from the field across delay and provided recommendations for pre-sampling and sampling tasks based on their collective expertise. The scientists used dynamic priority ranking lists, referred to as dynamic leaderboards, to track and rank candidate samples relative to one another and against the science objectives for the current EVA and the overall mission. Updates to the dynamic leaderboards throughout the EVA were relayed regularly to the IV crewmembers. The use of these leaderboards enabled the crew to track the dynamic nature of the MSC recommendations and helped minimize crew idle time (defined as time spent waiting for input from Earth during which no other productive tasks are being performed). EVA timelines were strategically designed to enable continuous (delayed) feedback from an Earth-based Science Team while simultaneously minimizing crew idle time. Such timelines are operationally advantageous, reducing transport costs by eliminating the need for crews to return to the same locations on multiple EVAs while still providing opportunities for recommendations from science experts on Earth, and scientifically advantageous by minimizing the potential for cross-contamination across sites. This paper will highlight the space-to-ground interaction results from the three BASALT field deployments, including planned versus actual EVA timeline data, ground assimilation times (defined as the amount of time available to the MSC to provide input to the crew), and idle time. Furthermore, we describe how these results vary under the different communication latency and bandwidth conditions. Together, these data will provide a basis for guiding and prioritizing capability development for future human exploration missions.

Beaton, Kara H.↗

NASA NTRS2017

Comprehensive Analysis Modeling of Small-Scale UAS Rotors

Multicopter unmanned aircraft systems (UAS), or drones, have continued their explosive growth in recent years. With this growth comes demand for increased performance as the limits of existing technologies are reached. In order to better design multicopter UAS aircraft, better performance prediction tools are needed. This paper presents the results of a study aimed at using the rotorcraft comprehensive analysis code CAMRAD II to model a multicopter UAS rotor in hover. Parametric studies were performed to determine the level of fidelity needed in the analysis code inputs to achieve results that match test data. Overall, the results show that CAMRAD II is well suited to model small-scale UAS rotors in hover. This paper presents the results of the parametric studies as well as recommendations for the application of comprehensive analysis codes to multicopter UAS rotors.

Analysis↗

NASA NTRS2018

An Efficient Inverse Finite Element Method for Shape and Stress Sensing of Laminated Composite and Sandwich Plates and Shells

The inverse Finite Element Method (iFEM) is a revolutionary methodology for real-time reconstruction of full-field structural displacements and stresses in structures that are instrumented with strain sensors. This inverse problem is commonly referred to as shape and stress sensing, which is well-recognized as an enabling technology for structural health monitoring systems. In this study, an improved iFEM formulation is proposed for shape and stress sensing of laminated composite and sandwich plates and shells. The formulation includes the kinematics of a shear deformation plate theory known as Refined Zigzag Theory (RZT) as its baseline. The present iFEM formulation is based upon the minimization of a weighted-least-squares functional that uses the complete set of section strains of RZT. The improved iFEM methodology is applicable for shape and stress sensing of thin and moderately thick plate and shell structures involving a relatively small number of strain gauges. The main advantage of the current formulation is that highly accurate through-the-thickness distributions of displacements, strains, and stresses are attainable using an element based on simple C0-continuous displacement interpolation functions. A three-node inverseshell element, named i3-RZT, is developed. Two example problems are examined in detail: (1) a simply supported rectangular laminated composite plate and (2) a wedge structure with a hole near one of the clamped ends. For both problems, the experimental strain data are generated numerically by the direct finite element analysis using high-fidelity discretizations. These strains are then regarded as the experimental strains obtained from surface mounted strain gauges or embedded fiber Bragg grating (FBG) sensors. The numerical results demonstrate the superior capability and potential applicability of the i3- RZT/iFEM methodology for performing accurate shape and stress sensing of complex composite structures.

Kefal, Adnan↗

NASA NTRS2018

A Conservative, Scalable, Space-Time Blade Element Rotor Model for Multi-Rotor Vehicles

The development of a parallel blade-element rotor model and its implementation into an adaptive Cartesian method is described. The unsteady version of the rotor model applies a body force to all cells contained in the swept space-time volume at each timestep and special care is taken to maintain axisymmetry on the Cartesian grid. Mesh convergence of rotor thrust and torque is obtained with around 10000 cells in the disk for the steady model. Parallelization is accomplished using OpenMP and the rotor force computation is distributed across all available nodes. Simulations of an isolated XV-15 rotor in hover show good correlation with experimental data and predictions of multi-rotor thrust variation closely match previous high fidelity simulations. The final paper will also include results from the unsteady rotor model and parallel scaling tests.

Conservative↗

NASA NTRS2019

Progress Towards Modeling the Ablation Response of NuSil-Coated PICA

The Mars Science Laboratory (MSL) Entry, Descent and Landing Instrumentation (MEDLI) collected in-flight data largely used by the ablation community to verify and validate physics-based models for the response of the Phenolic Impregnated Carbon Ablator (PICA) material [1-4]. MEDLI data were recently used to guide the development of NASA’s high-fidelity material response models for PICA, implemented in the Porous material Analysis Toolbox based on OpenFOAM (PATO) software [5-6]. A follow-up instrumentation suite, MEDLI2, is planned for the upcoming Mars 2020 mission [7] after the large scientific impact of MEDLI. Recent analyses performed as part of MEDLI2 development draw the attention to significant effects of a protective coating to the aerothermal response of PICA. NuSil, a silicone-based overcoat sprayed onto the MSL heatshield as contamination control, is currently neglected in PICA ablation models. To mitigate the spread of phenolic dust from PICA, NuSil was applied to the entire MSL heatshield, including the MEDLI plugs. NuSil is a space grade designation of the siloxane copolymer, primarily used to protect against atomic oxygen erosion in the Low Earth Orbit environment. Ground testing of PICA-NuSil (PICA-N) models all exhibited surface temperature jumps of the order of 200 K due to oxide scale formation and subsequent NuSil burn-off. It is therefore critical to include a model for the aerothermal response of the coating in ongoing code development and validation efforts.

Meurisse, Jeremie B.↗

NASA NTRSDate not supplied

Testing of a Multi-Stringer Post-Buckled Panel with Incremental Damage Detection

A multi-stringer panel was designed and tested to produce a failure mode of skin-stiffener delamination due to compression loading of a post-buckled skin to support the validation of high-fidelity progressive damage failure analysis modeling. The test procedure included multiple methods of data acquisition ranging from multiple strain gages, front and back digital image correlation, passive thermography, acoustic emission, and ultrasonic inspection to capture key events and damage progression prior to catastrophic failure. Detailed data showing the initial stiffness, onset of skin buckling, buckled mode shape, damage initiation load, damage propagation morphology, and peak load was captured. Two panel configurations were tested. The first had a Teflon insert placed in the interface between the skin and the stiffener flanges adjacent to the center skin bay. The second was subjected to impact damage to the skin-stiffener flange. In each case, continuously-loaded-to-failure tests were conducted first with passive thermography being used exclusively to determine key damage events. Then, incrementally loaded tests were completed where ultrasonic inspections were conducted between each increment. The amount of load applied between each increment was determined based on the in-situ damage detection provided by the passive thermography system. The results of the testing showed a complex delamination growth pattern which migrated between ply interfaces through matrix splits in the upper skin ply of the panel. The result of the testing produced very detailed data which will prove to be useful for validation of delamination growth and migration predictions from progressive damage failure analysis methods.

Jason Action↗

NASA NTRSDate not supplied

3D Material Response of the MSL Heatshield Using NuSil-Coated PICA

The Mars Science Laboratory (MSL) Entry, Descent and Landing Instrumentation (MEDLI) collected in-flight data largely used by the ablation community to verify and validate physics-based models for the response of the Phenolic Impregnated Carbon Ablator (PICA) material [1-4]. MEDLI data were recently used to guide the development of NASA’s high-fidelity material response models for PICA implemented in the Porous material Analysis Toolbox based on OpenFOAM (PATO) software [5-6]. A follow-up instrumentation suite, MEDLI2, was used for the Mars 2020 mission [7] after the large scientific impact of MEDLI. Recent analyses performed as part of MEDLI2 development drew the attention to significant effects of a protective coating to the aerothermal response of PICA. NuSil, a silicone-based overcoat sprayed onto the MSL heatshield, including the MEDLI plugs, as contamination control, is currently neglected in PICA ablation models. To mitigate the spread of phenolic dust from PICA, NuSil was applied to the entire MSL and Mars 2020 heatshields, including the MEDLI and MEDLI2 plugs. Ground testing of PICA-NuSil (PICA-N) models exhibited surface temperature jumps due to oxide scale formation and subsequent NuSil burn- off. It is therefore critical to include a model for the aerothermal response of the coating in ongoing code development and validation efforts. Figure 1 illustrates the PICA-N model implemented in PATO with a test case that shows a temperature jump and a change of recession rate at the wall after 20 seconds of simulation when the NuSil coating was fully removed.

Aerospace↗

NASA NTRSDate not supplied

Strategy for Developing Technologies for Megawatt-class Nuclear Electric Propulsion Systems

A strategy for maturing the technologies required for a megawatt-class nuclear electric propulsion (NEP) system is presented. The effort is responsive to recent non-advocate reviews stating high-power NEP technologies were relatively immature and significant maturation was required before contemplating the use of NEP on a flight mission. The maturation strategy presented accomplishes this through hardware test and evaluation at the sizes, scales, and conditions expected during high-power NEP missions. The development effort is accompanied by modeling of such a system to demonstrate thorough understanding and verification of the performance, lifetime, and failure modes. The proposed effort uses a building-block approach, maturing technologies for a 1 MWe block under the assumption that a future high-power NEP mission will have requirements that can be met either through straightforward scaling of this building block to the levels required or through the use of multiple blocks to meet the overall power needs. The plan is outlined for maturation to technology readiness level 5, characterized by test and evaluation using brassboard-fidelity hardware in a relevant environment and by demonstration of agreement between test data and analytical predictions.

Kurt A. Polzin↗

NASA NTRSDate not supplied

Utilizing 3D-DIC on Mars 2020 Rover Wheel Assembly: Test-Analysis Correlation

Following the successful implementation of full-field photogrammetry, more specifically three-dimensional Digital Image Correlation (3D-DIC), on the Mars 2020 Heat Shield Structural Failure Review assessment, 3D-DIC was selected as one of the primary measurement techniques for the Mars 2020 Rover wheel assembly qualification test at the NASA Jet Propulsion Laboratory (JPL). To validate the Rover wheel landing loads simulations, it was extremely important to have high confidence in the wheel models. Due to the large deformations and strains that the wheel would be subject to during landing, traditional instrumentation such as linear variable displacement transducers (LVDTs), electrical-resistance strain gages and string potentiometers, would not be sufficient on their own to provide all the necessary validation data. Therefore, the NASA Engineering and Safety Center (NESC) provided the 3D-DIC expertise and support to measure the high deformation and strain in the wheel flexures and qualify the overall structural response of the Mars 2020 rover wheel assembly. There were two key objectives for the photogrammetry technique: (1) monitor the wheel response in real-time, guarding against anomalous behavior and failure, and (2) provide test data for test-analysis correlation to validate and/or improve the high-fidelity computational model. The contents of this paper will focus on the challenges of applying 3D-DIC to the Mars 2020 Rover wheel assembly and how these challenges were overcome. Examples of test-analysis correlation during the stiffness characterization and structural qualification will be presented and discussed in detail. Experimental results were compared with the analysis and showed excellent agreement between the predicted behavior and helped validate the high-fidelity models.

Mars 2020 Rover Wheel↗

NASA NTRS2021

Mars 2020 Radiometric Data and Telemetry Processing, Attitude Estimation, and Thruster Calibration for Orbit Determination

The Mars 2020 spacecraft was spin-stabilized during cruise, just like its predecessor, the Mars Science Laboratory. This spinning motion imparts a signature in the radiometric tracking data that must be dealt with in order to properly model the motion of the spacecraft's center of mass. We discuss how the Orbit Determination team pre-processed the data for efficient computations while also providing other benefits such as high-fidelity attitude modeling and on-board clock verification. Finally, we discuss the analysis and results of the in-flight thruster calibration activity.

Seubert, Jill↗

NASA NTRSDate not supplied

Aerothermodynamic Measurements in Hypersonic Non-Equilibrium Flows

High enthalpy arc-jets are unique facilities particularly suited for simulating complex flows in the aerospace field, such as the aerothermodynamics of a re-entry vehicle. Arc-jets are often used to evaluate important design factors such as heat shield materials and vehicle design. Characterization of these facilities is important, as studies often match specific in-flight environments during experiments. Due to the complex environment produced by an arc-jet, with effects such as thermodynamic and chemical non-equilibrium occurring in the flow, characterization experiments are significantly more difficult than in traditional blow-down wind tunnels. The current work aims to characterize an arc-jet facility in terms of flow temperature, velocity and unsteadiness, in a spatially-resolved analysis. To achieve this goal, a non-intrusive imaging technique called “planar laser-induced fluorescence” was performed in the NASA Langley Hypersonic Materials Environ-mental Test System arc-jet facility. The experimental data was analyzed to produce the quantitative measurements in multiple regions of the flow around a blunt body sample. A quasi-1D low fidelity solver was created to numerically simulate the flow to investigate non-equilibrium effects occur-ring outside the imaging region in the arc-jet nozzle. Unsteadiness in the test section was minimized by analyzing a subset of data assessing the gas injection location. Radial velocity, rotational temperature and translational temperature measurements are provided that can be used to validate future high-fidelity computational studies. The temperature measurements revealed rotational non-equilibrium potentially occurring in the flow. This work provides a methodology for characterizing arc-jet facilities, identifying and correcting sources of systematic error, and estimating the uncertainty associated with the quantitative measurements.

laser induced fluorescence↗

DOE OSTI2023

DeepSurveySim: Simulation Software and Benchmark Challenges for Astronomical Observation Scheduling

Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection of simulated telescope schedules. Automated methods, such as reinforcement learning, have recently been explored to accelerate scheduling. However, there do not yet exist benchmark data sets or user-friendly software frameworks for testing and comparing these methods. We present DeepSurveySim -- a high-fidelity and flexible simulation tool for use in telescope scheduling. DeepSurveySim provides methods for tracking and approximating sky conditions for a set of observations from a user-supplied telescope configuration. We envision this tool being used to produce benchmark data sets and for evaluating the efficacy of ground-based telescope scheduling algorithms, particularly for machine learning algorithms that would suffer in efficacy if limited to real data for training.We introduce three example survey configurations and related code implementations as benchmark problems that can be simulated with DeepSurveySim.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗

DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗

NASA NTRS2021

Coral Oxygen Isotopic Records Capture the 2015/2016 El Niño Event in the Central Equatorial Pacific

Coral oxygen isotopes (δ^(18)O) from the central equatorial Pacific provide monthly resolved records of El Niño-Southern Oscillation activity over past centuries to millennia. However, calibration studies using in situ data to assess the relative contributions of warming and freshening to coral δ^(18)O records are exceedingly rare. Furthermore, the fidelity of coral δ^(18)O records under the most severe thermal stress events is difficult to assess. Here, we present six coral δ^(18)O records and in situ temperature, salinity, and seawater δ^(18)O data from Kiritimati Island (2°N, 157°W) spanning the very strong 2015/16 El Niño event. Local sea surface temperature (SST) anomalies of +2.4 ± 0.4°C and seawater δ^(18)O anomalies of −0.19 ± 0.02‰ contribute to the observed coral δ^(18)O anomalies of −0.58 ± 0.05‰, consistent with a ∼70% contribution from SST and ∼30% from seawater δ^(18)O. Our results demonstrate that Kiritimati coral δ^(18)O records can provide reliable reconstructions even during the largest class of El Niño events.

Oxygen isotope anomalies↗

NASA NTRS2021

A Study into Validating a Coupled Method of Characteristics and Direct Simulation Monte Carlo Method Against Empirical Data

The following will outline the methodology and results of validating a coupled Method of Characteristics (MOC) and Direct Simulation Monte Carlo (DSMC) method. This research focused specifically on modeling plume impingement, induced by Reaction Control System (RCS) thrusters that flew on the National Aeronautics and Space Administration’s (NASA’s) space shuttle Discovery. For each simulation, the continuum portion of the RCS thruster was simulated using MOC for solving hyperbolic Partial Differential Equations (PDEs) and computed with the NASA code, Reacting and Multi-phase Program (RAMP). The solution was then implemented as a starting condition into the NASA DSMC code, Direct Simulation and Monte Carlo Analysis Code (DAC). Typically, DSMC models rely on code-to-code validation for fidelity. The significance of this research is in its ability to validate its models against empirical data. Prior to computing solutions for these simulations, the mesh size and structure were optimized and many variants of DSMC input parameters were iterated on in order to acquire a reliable, mesh-independent, fully optimized numerical solution. This research will discuss the mathematical formulation of MOC for nozzle flow and DSMC for rarefied gases. Additionally, it will provide an explanation of how to implement these mathematical concepts into the two solvers: RAMP and DAC. Ultimately, this research will demonstrate that the overall process illustrated produces results in good agreement with empirical data. As a consequence, the methodology presented is granted an increased level of confidence and will greatly contribute to the aerospace industry and its effort in understanding and predicting rarefied flow fields.

Direct Simulation Monte Carlo Analysis Code↗

NASA NTRS2021

A Study into Validating A Coupled Method of Characteristics And Direct Simulation Monte Carlo Method Against Empirical Data

DAC↗

NASA NTRSDate not supplied

L'Ralph's Advanced Thermal Model Correlation Using Veritrek

Thermal model correlation uses data from thermal balance tests to better estimate uncertain input parameter values. During the correlation process, input parameters are modified in an iterative manner which can become computationally expensive since this requires that the high-fidelity thermal model be run for each iteration. Depending on the number of thermal balance test points there can be many sets of correlation parameters that satisfy correlation criteria; and having enough data to ascertain the best set of correlation parameters to use, further increases the computational expense. Reduced-order models (ROMs) provide computationally efficient surrogates of high-fidelity models and are often built to reduce development cycle times and cost. By leveraging the speed of reduced-order models and the Correlation Analysis feature in the Veritrek software, the typical computational expense of a traditional thermal model correlation process can be significantly reduced and having access to hundreds of thousands of iteration results provides an advanced means of intelligently determining the best set of correlation parameters to use. The L’Ralph thermal team at NASA Goddard Space Flight Center explored the use of the Veritrek software for their thermal model correlation efforts. The ROM that was created allowed for the variation of 15 input parameters to match 70 temperature sensor readouts from 3 thermal balance plateus and required 125 runs of the high-fidelity Thermal Desktop® model to generate a ROM that could predict the detailed model’s results to within 0.2 K (RMS). The ROM was then used to find dozens of plausible correlation parameter values based on L’Ralph instrument test data within a few seconds. By providing several plausible correlation parameter combinations, Veritrek allowed the thermal team to explore different uncertain parameter value combinations and provided insight into how deterministic each input parameter was. This allowed for a more confident decision on the best set of correlation parameters to use, compared to traditional model correlation techniques. In this presentation, the L’Ralph thermal team will be presenting their experience with the Veritrek software and how the software was utilized to provide additional insights during the correlation process.

Daniel Bae↗