Engineering topics

Green, Justin S.

Publications and source records attributed to Green, Justin S..

7 records

NASA NTRS2022

Precision Landing Performance and Technology Assessments of a Human-Scale Lunar Lander Using a Generalized Simulation Framework

NASA has established goals of returning humans to Moon with an initial landing by 2024 and a subsequent sustained presence by 2028, which will require technological advances in spacecraft navigation to enable precision landing. The ability to assess the navigation performance of these new and existing technologies is critical to identifying areas of risk reduction and investment. To that end, the Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project has demonstrated that a detailed six degree-of-freedom integrated performance simulation framework can provide information on and assessment of expected navigation performance. This framework incorporates engineering models of the on-board spacecraft guidance, navigation, and control systems at varying levels of fidelity. Recent advances in the development of this integrated performance simulation permit running these systems “in-the-loop,” rather than assuming perfect knowledge of the spacecraft states. This development, coupled with fast simulation time and modularization of the various system models, enables a wide variety of system trades to be assessed at once. This paper presents a summary of the advances in the SPLICE simulation framework, updates to the spacecraft navigation models, and an application of the framework to characterize the precision landing performance of a human-scale lunar lander. A series of trade studies examining effects of ground state update qualities shows that given all other assumptions, sufficiently accurate Deep Space Network (DSN) measurements can enable safe and precise human-scale Lunar landings.

"D'Souza, Sarah", 'Pensado, Alegandro R.↗

NASA NTRS2019

ADEPT Sounding Rocket One Flight Test Overview

On September 12th, 2018, a sounding rocket flight test was conducted on a mechanically-deployed atmospheric entry system known as the Adaptable Deployable Entry and Placement Technology (ADEPT). The purpose of the Sounding Rocket One (SR-1) test was to gather critical flight data for evaluating the vehicle's in-space deployment performance and supersonic stability. This flight test was a major milestone in a technology development campaign for Nano-ADEPT: the application of ADEPT for small secondary payloads. The test was conducted above White Sands Missile Range, New Mexico on a SpaceLoft XL rocket manufactured by UP Aerospace. This paper describes the system components, hardware development campaign, test execution, and test conclusions.

Cassell, Alan M.↗

NASA NTRS2019

Subsonic Dynamic Testing of a Subscale ADEPT Entry Vehicle

The Adaptive Deployable Entry and Placement Technology (ADEPT) is a mechanically-deployed entry system. A sounding rocket test flight of an ADEPT vehicle, known as ADEPT SR-1, was conducted in September 2018. Prior to this sounding rocket test, an investigation was performed using the NASA Langley Research Center 20-ft Vertical Spin Tunnel (VST) to assess the free-flight dynamic characteristics of ADEPT SR-1 at subsonic speeds. The model of ADEPT SR-1 for this VST test was fabricated at 50-percent geometric scale, with dynamically scaled mass properties (Froude scaled) to represent full-scale flight at an altitude of 1.2 km above sea level. The subsonic dynamic characteristics of ADEPT SR-1 were of interest prior to the sounding rocket test because of payload recovery considerations. At low roll rates the model was found to have acceptable dynamic characteristics. It was statically stable in pitch and yaw, exhibiting limit cycle pitch/yaw oscillations of no greater than 20 degrees (the angle between the model’s longitudinal axis and nadir). The model was able to recover from large upsets in pitch and yaw, although if sufficiently provoked it tumbled. Damping in roll was low. At high roll rates the pitch and yaw oscillations grew in magnitude and rate. This behavior was also observed during the sounding rocket flight test.

Cruz, Juan R.↗

NASA NTRS2019

Flight Mechanics Modeling and Post-Flight Analysis of ADEPT SR-1

Sounding Rocket One (SR-1), the first flight test of the Adaptable Deployable Entry and Placement Technology (ADEPT), was performed on Sept. 12, 2018. ADEPT is a deployable aeroshell that can be stowed during launch and then opened after launch to increase the drag area of the spacecraft when entering into a planetary atmosphere. The main objectives of the SR-1 flight test were to demonstrate that the ADEPT vehicle can be opened exo-atmospherically and to characterize the stability of the vehicle during atmospheric flight. The SR-1 test vehicle was a 0.7 m diameter 70 degree half-angle, faceted, sphere-cone, which was the primary payload on an UP Aerospace Spaceloft (SL) launch vehicle from the White Sands Missile Range (WSMR). ADEPT successfully separated from the spent booster in its stowed configuration, opened above 100 km altitude, and then landed in the deployed configuration within WSMR. The flight mechanics of the vehicle was modeled pre-flight for performance and range safety predictions. This paper describes the pre-flight ADEPT trajectory simulation and how the flight data compared with the predictions from the simulations.

Dutta, Soumyo↗

NASA NTRS2018

An Investigation of Parallel Programming Techniques Applied to Monte Carlo Simulations for Post-Flight Reconstruction of Spacecraft Trajectory

Parallelizing software to execute on multi-core central processing units (CPUs) and graphics processing units (GPUs) can be challenging. For some fields outside of Computer Science, this transition comes with new issues. For example, memory limitations can require modifications to code not initially developed to run on GPUs. This work applies the Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) directive-based parallelization strategies on a Monte Carlo simulation approach for trajectory reconstruction enabling it to run on multi-core CPUs and GPUs. Large matrix operations are the most common use of GPUs, which are not present in this algorithm; however, the natural parallelism of independent trajectories in Monte Carlo simulations is exploited. Benchmarking data are presented comparing execution times of the software for single-thread CPUs, multi-thread CPUs with OpenMP, and multi-thread GPUs using OpenACC. These data were collected using nodes with Intel® Xeon® E5-2670 (Sandy Bridge) CPUs enhanced with NVIDIA® Tesla® K40 GPUs on the Pleiades Supercomputer cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC) and a local Intel® Xeon Phi™ node at NASA Langley Research Center (LaRC).

Williams, R. Anthony↗

NASA NTRS2018

Parallelization of a Six Degree of Freedom Entry Vehicle Trajectory Simulation Using OpenMP and OpenACC

The art and science of writing parallelized software, using methods such as Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC), is dominated by computer scientists. Engineers and non-computer scientists looking to apply these techniques to their project applications face a steep learning curve, especially when looking to adapt their original single threaded software to run multi-threaded on graphics processing units (GPUs). There are significant changes in mindset that must occur; such as how to manage memory, the organization of instructions, and the use of if statements (also known as branching). The purpose of this work is twofold: 1) to demonstrate the applicability of parallelized coding methodologies, OpenMP and OpenACC, to tasks outside of the typical large scale matrix mathematics; and 2) to discuss, from an engineer’s perspective, the lessons learned from parallelizing software using these computer science techniques. This work applies OpenMP, on both multi-core central processing units (CPUs) and Intel® Xeon Phi™ 7210, and OpenACC on GPUs. These parallelization techniques are used to tackle the simulation of thousands of entry vehicle trajectories through the integration of six degree of freedom (DoF) equations of motion (EoM). The forces and moments acting on the entry vehicle, and used by the EoM, are estimated using multiple models of varying levels of complexity. Several benchmark comparisons are made on the execution of six DoF trajectory simulation: single thread Intel® Xeon® E5-2670 CPU, multi-thread CPU using OpenMP, multi-thread Xeon Phi™ 7210 using OpenMP, and multi-thread NVIDIA® Tesla® K40 GPU using OpenACC. These benchmarks are run on the Pleiades Supercomputer Cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC), and a Xeon Phi™ 7210 node at NASA Langley Research Center (LaRC).

Green, Justin S.↗

NASA NTRS2018

Adaptive Control Allocation for Powered Descent Vehicles

The following work details a study into real-time failure adaptive control allocation method for powered descent vehicle systems. The motivation for this work is to enable future human and robotic missions utilizing a powered descent system to tolerate engine failures in flight without the loss of crew or assets. This study is conducted using a six degree-of-freedom trajectory simulation of a PDV (Powered Descent Vehicle) experiencing either a loss of thrust or an engine stuck full on failure scenario. Sequential least squares in the frequency domain is used on-board to process inertial measurement unit (IMU) data and generate an estimate of the PDV plant model, which is then fed to the guidance and control system. Data used by the sequential least squares method is generated from an in-flight maneuver. The work herein focuses on determining a maneuver that is least impactful to the PDV trajectory and enables a suitable plant model estimate. A 1.5-second-long maneuver with an amplitude of 5 percent throttle is determined to provide suitable data for the sequential least squares method to estimate a plant model. A PDV implementing this method can adapt to a single engine failure and continue to reach its touchdown conditions.

Green, Justin S.↗