Results for “parameter optimization” — Page 28

NASA NTRS2014

Space Launch System Implementation of Adaptive Augmenting Control

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to robustly demonstrate stable and high performance flight. On its development path to preliminary design review (PDR), the stability of the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant dynamics. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an adaptive augmenting control (AAC) algorithm previously presented by Orr and VanZwieten, has been shown to extend the envelope of failures and flight anomalies for which the SLS control system can accommodate while maintaining a direct link to flight control stability criteria (e.g. gain & phase margin). In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the SLS digital 3-axis autopilot, including existing load-relief elements, and necessary steps for integration with the production flight software prototype have been implemented. Several updates to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

VanZwieten, Tannen S.↗

NASA NTRS2014

Space Launch System Implementation of Adaptive Augmenting Control

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to provide stable and high-performance flight. On its development path to Preliminary Design Review (PDR), the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an Adaptive Augmenting Control (AAC) algorithm has been shown to extend the envelope of failures and flight anomalies the SLS control system can accommodate while maintaining a direct link to flight control stability criteria such as classical gain and phase margin. In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the full SLS digital 3-axis autopilot, including existing load-relief elements, and the necessary steps for integration with the production flight software prototype have been implemented. Several updates which have been made to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are also shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

Wall, John H.↗

NASA NTRS2014

Human Outer Solar System Exploration via Q-Thruster Technology

Propulsion technology development efforts at the NASA Johnson Space Center continue to advance the understanding of the quantum vacuum plasma thruster (QThruster), a form of electric propulsion. Through the use of electric and magnetic fields, a Q-thruster pushes quantum particles (electrons/positrons) in one direction, while the Qthruster recoils to conserve momentum. This principle is similar to how a submarine uses its propeller to push water in one direction, while the submarine recoils to conserve momentum. Based on laboratory results, it appears that continuous specific thrust levels of 0.4 - 4.0 N/kWe are achievable with essentially no onboard propellant consumption. To evaluate the potential of this technology, a mission analysis tool was developed utilizing the Generalized Reduced Gradient non-linear parameter optimization engine contained in the Microsoft Excel® platform. This tool allowed very rapid assessments of "Q-Ship" minimum time transfers from earth to the outer planets and back utilizing parametric variations in thrust acceleration while enforcing constraints on planetary phase angles and minimum heliocentric distances. A conservative Q-Thruster specific thrust assumption (0.4 N/kWe) combined with "moderate" levels of space nuclear power (1 - 2 MWe) and vehicle specific mass (45 - 55 kg/kWe) results in continuous milli-g thrust acceleration, opening up realms of human spaceflight performance completely unattainable by any current systems or near-term proposed technologies. Minimum flight times to Mars are predicted to be as low as 75 days, but perhaps more importantly new "retro-phase" and "gravity-augmented" trajectory shaping techniques were revealed which overcome adverse planetary phasing and allow virtually unrestricted departure and return opportunities. Even more impressively, the Jovian and Saturnian systems would be opened up to human exploration with round-trip times of 21 and 32 months respectively including 6 to 12 months of exploration at the destinations. Finally, interstellar trip times are assessed at milli-g acceleration levels.

Joosten, B. Kent↗

NASA NTRS2014

Determination of Ammonia Oxidizing Bacteria and Nitrate Oxidizing Bacteria in Wastewater and Bioreactors

The process of water purification has many different physical, chemical, and biological processes. One part of the biological process is the task of ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB). Both play critical roles in the treatment of wastewater by oxidizing toxic compounds. The broad term is nitrification, a naturally occurring process that is carried out by AOB and NOB by using oxidation to convert ammonia to nitrite and nitrite to nitrate. To monitor this biological activity, bacterial staining was performed on wastewater contained in inoculum tanks and biofilm samples from bioreactors. Using microscopy and qPCR, the purpose of this experiment was to determine if the population of AOB and NOB in wastewater and membrane bioreactors changed depending on temperature and hibernation conditions to determine the optimal parameters for AOB/NOB culture to effectively clean wastewater.

Bioreactors↗

NASA NTRS2009

Neural Network Target Identification System for False Alarm Reduction

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

ATR↗

NASA NTRS2014

Design and Control of Modular Spine-Like Tensegrity Structures

We present a methodology enabled by the NASA Tensegrity Robotics Toolkit (NTRT) for the rapid structural design of tensegrity robots in simulation and an approach for developing control systems using central pattern generators, local impedance controllers, and parameter optimization techniques to determine effective locomotion strategies for the robot. Biomimetic tensegrity structures provide advantageous properties to robotic locomotion and manipulation tasks, such as their adaptability and force distribution properties, flexibility, energy efficiency, and access to extreme terrains. While strides have been made in designing insightful static biotensegrity structures, gaining a clear understanding of how a particular structure can efficiently move has been an open problem. The tools in the NTRT enable the rapid exploration of the dynamics of a given morphology, and the links between structure, controllability, and resulting gait efficiency. To highlight the effectiveness of the NTRT at this exploration of morphology and control, we will provide examples from the designs and locomotion of four different modular spine-like tensegrity robots.

Modular↗

NASA NTRS2019

Lamb wave defect detection and evaluation using a fully non-contact laser system

Traditional Lamb wave structural health monitoring (SHM)/nondestructive evaluation (NDE) system employs contact type transducers such as PZT, ultrasonic transducers, and optical fibers. In application, transducer attachment and maintenance can be time and labor consuming. In addition, the use of couplant and adhesives can introduce additional materials on structures, and the interface coupling is often not well understood. To overcome these limitations, we proposed a fully non-contact NDE system by employing pulsed laser (PL) for Lamb wave actuation and scanning laser Doppler vibrometer (SLDV) for Lamb wave sensing. The proposed system is implemented on aluminum plates. The PL Lamb wave excitation is calibrated, and the optimal parameters are obtained. Lamb wave modes are then characterized through 1D wavefield analysis. With the calibrated and characterized system, defect detection and evaluation are achieved on aluminum plates with simulated defects (surfaced-bonded quartz rod, and machine milled crack) through 1D and 2D inspection in both time-space and frequency-wavenumber domains.

Ma, Zhaoyun↗

NASA NTRSDate not supplied

TPSAS-NF1676L-33512-DND

INTRODUCTION: In an effort to quantify the predictive capability of the Livermore Software Technology Corporation (LSTC)-provided Hybrid III (small female, mid-size male, and large male) anthropomorphic test device (ATD) finite element models (FEMs) within aerospace loading environments, the National Aeronautics and Space Administration (NASA) performed a series of sled tests with the isolated head-neck complex of each ATD. These tests and subsequent modeling efforts revealed significant deficiencies in the large male head neck FEM. Differences in both the shape and material properties between the FEM and the physical ATD were identified. In order to improve the predictive response of the FEM, a model development effort was undertaken to update the head-neck complex to better represent the geometric and material properties of the physical ATD.METHODS: To better match the physical geometry, a new neck component model was meshed and implemented into the Hybrid III large male head-neck complex. The updated geometry and weight were verified against measurements of the physical ATD to insure accuracy. The primary source of material discrepancy between the physical ATD and FEM was found to be in the material definition of the rubber neck pucks. Using LS-Opt the material properties of this part were calibrated to match physical response of the head-neck complex in the isolated head-neck testing performed. RESULTS AND DISCUSSION: The updated geometry and calibrated material properties were shown to significantly improve the FEM predictive accuracy under lateral, rear, and frontal impacts with combined horizontal and vertical loading. The results of this study demonstrate an effective means for improving ATD FEM response through isolated component level testing and material parameter optimization. The improved and quantified accuracy of the Hybrid III large male FEM head-neck complex stemming from with work lends to confidence in its future use for occupant protection evaluation of large male occupants

J Putnam↗

NASA NTRS2018

Automatic detection of microlensing events in the galactic bulge using machine learning techniques

The Wide Field Infrared Survey Telescope (WFIRST) is a NASA ﬂagship mission scheduled to launch in mid-2020, with more than one year of its lifetime dedicated to microlensing survey. The survey is to discover thousands of exoplanets near or beyond the snowline via their microlensing light curve signatures, enabling a Kepler-like statistical analysis of planets at ~1-10 AU from their host stars and potentially revolutionizing our understanding of planet formation. The goal of our work is to create an automated system that has the ability to eﬃciently process and classify large-scale astronomical datasets that missions such as WFIRST will produce. In this paper, we discuss our framework that utilizes features election and parameter optimization for classiﬁcation models to automatically diﬀerentiate the diﬀerent types of stellar variability and detect microlensing events.

Shvartzvald, Yossi↗

NASA NTRS2020

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

vapor pressure↗

NASA NTRSDate not supplied

Influence of evaporation rate and chamber pressure on the FUV reflectance and physical characteristics of aluminum films

Space telescopes for studying astrophysical phenomena from the far ultraviolet (FUV) to the near infrared (NIR) require durable mirror coatings with high and uniform reflectance over a very broad spectral region. While coatings for the optical and NIR region are well developed with proven performance, the FUV band presents significant challenges, particularly below 115 nm. Recent developments in physical vapor deposition (PVD) coating processes of aluminum mirrors that are protected with a metal-fluoride overcoat to prevent oxidation (such as LiF, MgF2, or AlF3) have improved reflectance in the FUV. While the emphasis in these studies has been placed on improving the deposition conditions of the metal-fluoride overcoats, less attention has been devoted to how deposition parameters (such as vacuum conditions or deposition rates) may affect the quality of the aluminum mirrors. This paper presents characterization of Al+MgF2 coupons made by flash evaporation of aluminum followed by resistive evaporation of MgF2. Samples were manufactured under a variety of processing conditions and the relationship between processing variables and mirror FUV reflectivity is analyzed. Performance characterization was based on the measured near-normal reflectance in the FUV (90-180 nm), and normal-incidence transmittance in the visible was done to analyze the possible presence of pinholes in the mirror. We demonstrated pinhole-free Al/MgF2 mirrors deposited at room temperature with a reflectivity of 0.91 at 122 nm wavelength. This reflectivity enhancement was achieved solely through parameter optimization.

Manuel A. Quijada↗

NASA NTRSDate not supplied

Influence of Evaporation Rate and Chamber Pressure on the FUV Reflectance and Physical Characteristics of Aluminum Films

Space telescopes for studying astrophysical phenomena from the far ultraviolet (FUV) to the near infrared (NIR) require durable mirror coatings with high and uniform reflectance over a very broad spectral region. While coatings for the optical and NIR region are well developed with proven performance, the FUV band presents significant challenges, particularly below 115 nm. Recent developments in physical vapor deposition (PVD) coating processes of aluminum mirrors that are protected with a metal-fluoride overcoat to prevent oxidation (such as LiF, MgF2, or AlF3) have improved reflectance in the FUV. While the emphasis in these studies has been placed on improving the deposition conditions of the metal-fluoride overcoats, less attention has been devoted to how deposition parameters (such as vacuum conditions or deposition rates) may affect the quality of the aluminum mirrors. This paper presents characterization of Al+MgF2 coupons made by flash evaporation of aluminum followed by resistive evaporation of MgF2. Samples were manufactured under a variety of processing conditions and the relationship between processing variables and mirror FUV reflectivity is analyzed. Performance characterization was based on the measured near-normal reflectance in the FUV (90-180 nm), and normal-incidence transmittance in the visible was done to analyze the possible presence of pinholes in the mirror. We demonstrated pinhole-free Al/MgF2 mirrors deposited at room temperature with a reflectivity of 0.91 at 122 nm wavelength. This reflectivity enhancement was achieved solely through parameter optimization.

FUV reflectance, aluminum, PVD, deposition rate, A↗

NASA NTRSDate not supplied

Progress on Inverse Estimation Technique of Non-Linear Pitch Damping Coefficient Curves Using Free-Flight CFD Generated Trajectories

Characterization of entry vehicle pitch damping coefficient curves is crucial to ensure appropriate re-entry and overall mission success. The pitch damping coefficient (C_(m_q )+C_(m_α ̇ )) is used to encapsulate the oscillatory growth or decay of a body during a trajectory. The inverse estimation technique utilizes an existing Free-Flight CFD (FF-CFD) dataset and wraps a reconstruction algorithm in an optimizer. The reconstruction integrates the planar equations of motion derived by Schoenenberger, Queen [1] using Python’s scipy.integrate.solve_ivp. The optimizer’s objective function is the normalized 𝐿2 residual of the angle of attack peaks between the reconstructed trajectory and the original data produced with FF-CFD. Inclusion of the peak times in this residual calculation allows for simultaneous optimization of the pitch moment coefficient, C_(m_α ). This residual equation is shown below in Eq. 1. The optimizer scipy.optimize.minimize was used with the gradient-based Powell method for the analysis presented, however the differential evolution method was investigated as means of comparison, and was found to produce marginally lower residual values with prohibitively longer run times. Further, the pitch damping curve is found by fitting a cubic interpolation function to a set of (α, (C_(m_q )+C_(m_α ̇ ))) control points, where the α points are held constant and the (C_(m_q )+C_(m_α ̇ )) values are the optimized parameters. The pitch moment curve uses a linear interpolation between the minimum and maximum α in the dataset. FF-CFD generated trajectories using the Dragonfly capsule geometry with the Genesis ballistic range model parameters were simulated and used for this analysis. These FF-CFD trajectories simulate planar motion, as restricted by the reconstructing the equations of motion, of three different cases: 1-DoF (free-to-pitch), 2-DoF (free-to-pitch and heave), and 3-DoF (free-to-pitch, heave, and decelerate). Pitch damping coefficient curves generated using this inverse estimation curve technique with FF-CFD 1-DoF Dragonfly data are found in Fig. 1. Preliminary results reconstructing ballistic range shots using these FF-CFD derived predictions of the pitch damping curve (Fig. 1) are shown in Fig. 2. It should be noted that the ballistic range shot used a Genesis model whereas the FF-CFD data used a Dragonfly geometry, however these geometries are similar.

entry↗

NASA NTRSDate not supplied

Packing and flow particle simulations

Granular material is present across natural and industrial processes on Earth and other planets. Granular particles show up in space exploration (lunar regolith), avalanches (boulders), food (coffee), construction (concrete powder) and manufacturing (additive manufacturing powder, battery slurries). Important phenomena emerge from large collections of granular particles. The size scales of the particles in granular material, 10 μm diameter or larger, makes particle-based simulations a tractable computational method. This seminar will present the packing and flow of granular matter using particle-based discrete element modeling simulations. Particles modeled with rotational friction only require as few as 2.6 contacts for mechanical stability, as opposed to 6 contacts for frictionless particles. Optimal parameters for in-space manufacturing particulate material are identified. Specifically, the maximum density and contacts occurs for a large-to-small particle volume ratio of 0.265. Stress and contact fabric fluctuations of flowing dry granular matter have power-law scaling with strain rate, and a kink. The pressure-dependent slope change kink could identify the transition between slower, quasistatic and faster, inertial flows.

granular↗

NASA NTRSDate not supplied

Packing and flow particle simulations

Granular material is present across natural and industrial processes on Earth and other planets. Granular particles show up in space exploration (lunar regolith), avalanches (boulders), food (coffee), construction (concrete powder) and manufacturing (additive manufacturing powder, battery slurries). Important phenomena emerge from large collections of granular particles. The size scales of the particles in granular material, 10 μm diameter or larger, makes particle-based simulations a tractable computational method. This seminar will present the packing and flow of granular matter using particle-based discrete element modeling simulations. Particles modeled with rotational friction only require as few as 2.6 contacts for mechanical stability, as opposed to 6 contacts for frictionless particles. Optimal parameters for in-space manufacturing particulate material are identified. Specifically, the maximum density and contacts occurs for a large-to-small particle volume ratio of 0.265. Stress and contact fabric fluctuations of flowing dry granular matter have power-law scaling with strain rate, and a kink. The pressure-dependent slope change kink could identify the transition between slower, quasistatic and faster, inertial flows.

granular↗

NASA NTRSDate not supplied

Markov Decision Process based Trajectory Planning for UAVs under Uncertain Wind Conditions

In this paper we propose a Markov Decision Process (MDP) algorithm for path-planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to path-planning for UAVs are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs needs to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health and off-nominal battery power consumption. Machine learning methods such as Markov Decision Process (MDPs) have the potential to revolutionize how vehicles navigate in such uncertain environments. Previous papers have demonstrated the use of MDPs to optimize UAV path-planning for energy consumption under time-varying wind distribution. In this study, UAV trajectories from a pre-determined waypoint to target cell, will be computed on a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption, and operation time. The UAV navigates the grid by taking actions to move in either of the eight cardinal and intercardinal directions, under constant thrust profile. The next state of the UAV is calculated by considering its action, transition probability, obstacle cells and the wind speed magnitude and direction. Both constant and stochastic wind will be considered in this paper, the parameters being extracted from real wind measurements in proximity to an experimental UAV flight. One of the studies to be demonstrated in this paper is that as the unmanned airspace gets more complex with multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance needs to be made. Further, MDPs are capable of fast computation of UAV trajectories under varying wind, hence making them suitable for in-flight path planners.

decision-making↗

NASA NTRS2023

Plasma Activated Water: A Technology for Acid Generation and Space Crop Production

As humanity returns to the moon and onward to Mars, sustainable space travel becomes essential for cost and independence from Earth. Astronaut activities in low Earth orbit resupply consumable materials and can require alternative technologies if transportation of chemicals is deemed hazardous. Plasma technology and applications are able to generate commodities that assist in achieving Earth independence. Plasma interaction with water generates acid in-situ using on-board resources of electricity and breathable air without the need for large infrastructure. At Kennedy Space Center, researchers have shown that plasma is able to produce nitric acid, and therefore nitrates, in a plant-usable form in water. In this report, the team presents results on acid generation in g/kW-hr as well as provides results on plasma activated water for plant growth assistance. Plasma was generated utilizing DC, AC, and pulsed power supplies in order to determine optimizing parameters for generation of this useful commodity.

Plasma↗

NASA NTRSDate not supplied

Plasma Activated Water: A Technology for Acid Generation and Space Crop Production

As humanity returns to the moon and onward to Mars, sustainable space travel becomes essential for cost and independence from Earth. Astronaut activities in low Earth orbit resupply consumable materials and can require alternative technologies if transportation of chemicals is deemed hazardous. Plasma technology and applications are able to generate commodities that assist in achieving Earth independence. Plasma interaction with water generates acid in-situ using on-board resources of electricity and breathable air without the need for large infrastructure. At Kennedy Space Center, researchers have shown that plasma is able to produce nitric acid, and therefore nitrates, in a plant-usable form in water. In this report, the team presents results on acid generation in g/kW-hr as well as provides results on plasma activated water for plant growth assistance. Plasma was generated utilizing DC, AC, and pulsed power supplies in order to determine optimizing parameters for generation of this useful commodity.

Plasma↗