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James B Scoggins

Publications and source records attributed to James B Scoggins.

14 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry Descent and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRSDate not supplied

Multihierarchy Gaussian Process Models for Probabilistic Aerodynamic Databases using Uncertain Nominal and Off-Nominal Configuration Data

Probabilistic aerodynamic databases are a crucial component of the development lifecycle for aerospace vehicles. A key challenge when building aerodynamic databases is that most data used to construct them represent various simplifications of the real flight vehicle. For example, wind tunnel models often simplify the vehicle geometry and surface roughness characteristics, while CFD computations often make simplifications to the physics being modeled, such as fully laminar or turbulent calculations. Multifidelity data fusion models rely on a user being able to define a hierarchy of fidelity levels anchored to some "truth" data. This approach is unsatisfactory when no data can be considered to accurately reflect real flight conditions. In this work, we provide an alternative approach by presenting a consistent mathematical framework for building probabilistic aerodynamic databases in the form of a conditional probability distribution described by an ensemble of multifidelity Gaussian Processes. Instead of relying on a single hierarchy of data fidelity levels, the presented framework identifies a "nominal" configuration and potential corrections to the nominal which represent specific physical phenomena not represented in the nominal data. The nominal and correction functions themselves are constructed as multifidelity Gaussian Processes and linearly combined to form an ensemble model which fuses the uncertainties associated nominal and correction models. Results obtained using the proposed framework on a simplified Orion Crew Module wind tunnel dataset demonstrate the predictive capability of the multihierarchy framework. We further demonstrate the benefits of such a probabilistic aerodynamic database approach through function sampling and computing the conditional distributions of derived quantities, such as the trim angle of attack and aerodynamic coefficients at trim.

Gaussian Processes↗
NASA NTRSDate not supplied

AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

AeroFusion is a NASA Langley initiative to incorporate advances in data science into the aerodynamic modeling process to improve efficiency. The effort can largely be categorized in three components: reduced-order modeling techniques, surrogate modeling techniques, and uncertainty quantification. By combining various methods from these categories, AeroFusion aims to reduce the development cost of aerodynamic models, both in terms of time and money.

Steven Snyder↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRSDate not supplied

Aeroheating Environments of Aerocapture Systems for Uranus Orbiters

Aeroheating environments for an aerocapture system enabling flagship-class science at Uranus are presented. Applicability of a low lift-to-drag entry vehicle aeroshell with flight heritage for Martian entries is considered in the context of an end-to-end Uranus orbiter and probe mission design using aerocapture for orbit insertion. A feasible trajectory space for aerocapture orbit insertion based on various launch opportunities and interplanetary trajectories yield atmosphere-relative entry velocities between 22 km/s and 31 km/s with maximum freestream densities on the order of 10−5 kg/m3. A preliminary study of the aerothermal environments for the entire trajectory space is discussed followed by a more detailed study of the nominal design trajectories. Design conditions are based on laminar, thermochemical nonequilibrium Navier-Stokes flowfield simulations with coupled radiation transport for full lift-up (deep) and lift-down (shallow) design trajectories. Convective heating is found to be the dominant heating mode, with radiation accounting for only 1 % to 5 % of the total heat flux for the design trajectories. The peak design conditions are found to be 422 W/cm2 for heat flux, 10.5 kPa for pressure, 149 Pa for shear stress, and 88.1 kJ/cm2 for the total heat load.

Uranus↗
NASA NTRSDate not supplied

Uranus Flagship-class Orbiter and Probe Using Aerocapture

Exploration of the Ice Giants, especially Uranus, via orbiter and atmospheric probes, is required to answer pressing science questions that have been raised in the latest National Academies of Sciences Planetary Decadal Survey. Since the Ice Giants are the farthest planets from Earth, traditional fully-propulsive orbit insertion missions have transit times to the planetary bodies bordering 13-15 years and require a large amount of propellant (wet mass percentages of around 60-70%) for the orbit insertion maneuver, leaving less mass for the scientific payload and a planetary probe. Aerocapture uses aerodynamic forces generated by flight within a planetary atmosphere to decelerate and achieve orbit insertion. Aerocapture has been considered for several past missions but it has not been demonstrated. However, recent developments in thermal protection systems (TPS), guidance and control (G&C), and interplanetary navigation capabilities show the potential for using rigid, heritage entry vehicle configurations already flown at other planetary bodies for Ice Giants aerocapture. Aerocapture can robustly deliver spacecraft to Ice Giant orbits, while substantially increasing on-orbit payload mass (more than 40%) that can be used for a robust atmospheric entry probe. Additionally, the aerocapture maneuver would reduce the interplanetary transit time by 2-5 years (15-30%) relative to fully-propulsive orbit insertion. Recent work has shown that a flagship-class mission can be conducted in a shorter time than fully-propulsive missions if using aerocapture. This paper will consider the merits of including aerocapture as the orbit-insertion mechanism for a Uranus mission. Specifically, the implications of aerocapture orbit insertion for in-situ atmospheric probes will be discussed. The Uranus Orbiter and Probe concept mission study [3] is considered as the potential payload. Results from a recent NASA Space Technology Mission Directorate (STMD)-funded activity that is designing an aerocapture mission for a Uranus orbiter will be presented.

Soumyo Dutta↗
NASA NTRSDate not supplied

Thermal Protection System Design of Aerocapture Systems for Uranus Orbiters

The National Academies Planetary Science and Astrobiology Decadal Survey recently identified Uranus and Neptune - the Ice Giants - as the priority destinations for science. A mission to Uranus, the highest priority destination due to proximity to Earth, is viable with existing launch vehicle providers during launch windows starting in 2031. However, a nominal interplanetary trajectory (between 12 and 15 years) would still necessitate more than half the initial launch mass in propellant to achieve orbital insertion. Aerocapture, a method of orbital control that directs aerodynamic forces generated on a vehicle by the planet's atmosphere, allows mission designers to achieve the desired orbital state while saving time to the final destination and increasing the available mass for the science payload. Achieving orbital insertion via aerocapture requires novel algorithms for Guidance, Navigation and Control, and mass-efficient Thermal Protection Systems (TPS) performing in an atmosphere unlike any other NASA has flown through. Multiple TPS in NASA's repertoire are suitable for the unique aerothermal environment on the forebody, and the results of predicted sizing and challenges in implementation are discussed below. Results for aftbody TPS made by NASA as well as commercial vendors are discussed, along with the discussion of alternative solutions that may save time, reduce complexity, and increase mass-efficiency for the recommended Uranus Orbiter and Probe mission.

Uranus↗
NASA NTRSDate not supplied

Aerocapture Solutions for Uranus Flagship-class Orbiter and Probe

Recent planetary mission assessments, such as the 2022 Planetary Science Decadal Survey released by the National Academies of Science, have focused on the need for a flagship-class mission to the Ice Giant planets, particularly Uranus. The Uranus Orbit and Probe mission proposal was used by the National Academies of Science as the baseline while making its recommendation as the top flagship-class mission for NASA in the 2020-2030\'s. This mission which was based on a launch date of 2031 or 2032, used a fully-propulsive orbit insertion maneuver at Uranus (requiring 60-70% of total mass for propellant), and reached the planet after 13 years of interplanetary cruise before the 2049 equinox, a goal of the science community. A pre-2033 launch date is required to conduct a necessary Jupiter fly-by as the giant planet will be not be in the proper alignment for a later launch date. However, as the budget constraints of the NASA budget have pushed back the start date of a Uranus mission, a launch date of a flagship mission before 2033 seems unreasonable. Aerocapture, an orbit insertion maneuver that uses the atmosphere to decelerate, can reduce the propellant load needed for a captured orbit. Additionally, the aerocapture maneuver can decelerate safely while approaching the planet at higher arrival velocities, thus allowing a mission to use a highly energetic trajectory and reduce the cruise time by several years. An aerocapture-enabled solution has launch opportunities in the mid 2030's, including as late as 2038 to reach Uranus by 2049. This paper will discuss the feasibility of an aerocapture option for a Flagship-class mission to Uranus.

Soumyo Dutta↗
NASA NTRSDate not supplied

Aerodynamics of a Uranus Aerocapture System Using a Mars-Heritage Entry Vehicle

Aerodynamic characteristics of an aerocapture system intended to deliver a flagship-class orbiter and probe planetary science mission to Uranus are presented. The aeroshell of the Mars Science Laboratory and Mars 2020 entry vehicles is proposed as a baseline for this system to reduce the amount of necessary technology development. Direct Simulation Monte Carlo and Navier-Stokes computational fluid dynamics solutions are used to characterize the aerodynamic performance of the Mars-heritage vehicle for aerocapture flight at Uranus. These results are incorporated into an aerodatabase for use in six degree-of-freedom trajectory studies and mission design. Updates are made to the Mars-heritage aerodynamic uncertainty model based on observations in the Uranus-specific computational data to ensure the model is conservatively bounding for the proposed flight space. Necessary modifications to the aeroshell for system packaging are found to have minimal effect on aerodynamic performance. The resulting aerodatabase and uncertainty model are used to show the existing Mars-heritage entry vehicles have sufficient aerodynamic performance to achieve required control margin for Uranus aerocapture.

Eli R Shellabarger↗
NASA NTRSDate not supplied

Aerocapture Solutions for Uranus Flagship-class Orbiter and Probe

Recent planetary mission assessments, such as the 2022 Planetary Science Decadal Survey released by the National Academies of Science, have focused on the need for a flagship-class mission to the Ice Giant planets, particularly Uranus. The Uranus Orbit and Probe mission proposal was used by the National Academies of Science as the baseline while making its recommendation as the top flagship-class mission for NASA in the 2020-2030\'s. This mission which was based on a launch date of 2031 or 2032, used a fully-propulsive orbit insertion maneuver at Uranus (requiring 60-70% of total mass for propellant), and reached the planet after 13 years of interplanetary cruise before the 2049 equinox, a goal of the science community. A pre-2033 launch date is required to conduct a necessary Jupiter fly-by as the giant planet will be not be in the proper alignment for a later launch date. However, as the budget constraints of the NASA budget have pushed back the start date of a Uranus mission, a launch date of a flagship mission before 2033 seems unreasonable. Aerocapture, an orbit insertion maneuver that uses the atmosphere to decelerate, can reduce the propellant load needed for a captured orbit. Additionally, the aerocapture maneuver can decelerate safely while approaching the planet at higher arrival velocities, thus allowing a mission to use a highly energetic trajectory and reduce the cruise time by several years. An aerocapture-enabled solution has launch opportunities in the mid 2030's, including as late as 2038 to reach Uranus by 2049. This paper will discuss the feasibility of an aerocapture option for a Flagship-class mission to Uranus.

Soumyo Dutta↗