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NASA NTRS1993

An application of the IMC software to controller design for the JPL LSCL Experiment Facility

A software package which Integrates Model reduction and Controller design (The IMC software) is applied to design controllers for the JPL Large Spacecraft Control Laboratory Experiment Facility. Modal Cost Analysis is used for the model reduction, and various Output Covariance Constraints are guaranteed by the controller design. The main motivation is to find the controller with the 'best' performance with respect to output variances. Indeed it is shown that by iterating on the reduced order design model, the controller designed does have better performance than that obtained with the first model reduction.

Zhu, Guoming↗

NASA NTRS1977

Airframe noise of component interactions on a large transport model

Airframe noise produced by aircraft components and their interactions on a model of a wide body transport was investigated in an open jet anechoic flow facility. The two-meter span model had leading edge and multigapped trailing edge flaps, undercarriage, and associated stowage compartments. Each of these components were extended and retracted so their individual and interactive contributions to the measured airframe noise could be determined. An analysis of variance was performed which revealed a statistical significance of the component interactions. Various airframe noise reduction techniques were tried to reduce airframe noise. The effects of these techniques on the model's lift and drag were also measured.

Shearin, J. G.↗

NASA NTRS2004

Employing Sensitivity Derivatives to Estimate Uncertainty Propagation in CFD

Two methods that exploit the availability of sensitivity derivatives are successfully employed to predict uncertainty propagation through Computational Fluid Dynamics (CFD) code for an inviscid airfoil problem. An approximate statistical second-moment method and a Sensitivity Derivative Enhanced Monte Carlo (SDEMC) method are successfully demonstrated on a two-dimensional problem. First- and second-order sensitivity derivatives of code output with respect to code input are obtained through an efficient incremental iterative approach. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables); these sensitivity derivatives enable one to formulate first- and second-order Taylor Series approximations for the mean and variance of CFD output quantities. Additionally, incorporation of the first-order sensitivity derivatives into the data reduction phase of a conventional Monte Carlo (MC) simulation allows for improved accuracy in determining the first moment of the CFD output. Both methods are compared to results generated using a conventional MC method. The methods that exploit the availability of sensitivity derivatives are found to be valid when considering small deviations from input mean values.

Putko, Michele M.↗

NASA NTRS2006

Operational Concept for Flight Crews to Participate in Merging and Spacing of Aircraft

The predicted tripling of air traffic within the next 15 years is expected to cause significant aircraft delays and create a major financial burden for the airline industry unless the capacity of the National Airspace System can be increased. One approach to improve throughput and reduce delay is to develop new ground tools, airborne tools, and procedures to reduce the variance of aircraft delivery to the airport, thereby providing an increase in runway throughput capacity and a reduction in arrival aircraft delay. The first phase of the Merging and Spacing Concept employs a ground based tool used by Air Traffic Control that creates an arrival time to the runway threshold based on the aircraft s current position and speed, then makes minor adjustments to that schedule to accommodate runway throughput constraints such as weather and wake vortex separation criteria. The Merging and Spacing Concept also employs arrival routing that begins at an en route metering fix at altitude and continues to the runway threshold with defined lateral, vertical, and velocity criteria. This allows the desired spacing interval between aircraft at the runway to be translated back in time and space to the metering fix. The tool then calculates a specific speed for each aircraft to fly while enroute to the metering fix based on the adjusted land timing for that aircraft. This speed is data-linked to the crew who fly this speed, causing the aircraft to arrive at the metering fix with the assigned spacing interval behind the previous aircraft in the landing sequence. The second phase of the Merging and Spacing Concept increases the timing precision of the aircraft delivery to the runway threshold by having flight crews using an airborne system make minor speed changes during enroute, descent, and arrival phases of flight. These speed changes are based on broadcast aircraft state data to determine the difference between the actual and assigned time interval between the aircraft pair. The airborne software then calculates a speed adjustment to null that difference over the remaining flight trajectory. Follow-on phases still under development will expand the concept to all types of aircraft, arriving from any direction, merging at different fixes and altitudes, and to any airport. This paper describes the implementation phases of the Merging and Spacing Concept, and provides high-level results of research conducted to date.

Baxley, Brian T.↗

NASA NTRS2016

Green Propellant Landing Demonstration at U.S. Range

The Green Propellant Loading Demonstration (GPLD) was conducted December 2015 at Wallops Flight Facility (WFF), leveraging work performed over recent years to bring lower toxicity hydrazine replacement green propellants to flight missions. The objective of this collaboration between NASA Goddard Space Flight Center (GSFC), WFF, the Swedish National Space Board (SNSB), and Ecological Advanced Propulsion Systems (ECAPS) was to successfully accept LMP-103S propellant at a U.S. Range, store the propellant, and perform a simulated flight vehicle propellant loading. NASA GSFC Propulsion (Code 597) managed all aspects of the operation, handling logistics, preparing the procedures, and implementing the demonstration. In addition to the partnership described above, Moog Inc. developed an LMP-103S propellant-compatible titanium rolling diaphragm flight development tank and loaned it to GSFC to act as the GPLD flight vessel. The flight development tank offered the GPLD an additional level of flight-like propellant handling process and procedures. Moog Inc. also provided a compatible latching isolation valve for remote propellant expulsion. The GPLD operation, in concert with Moog Inc. executed a flight development tank expulsion efficiency performance test using LMP-103S propellant. As part of the demonstration work, GSFC and WFF documented Range safety analyses and practices including all elements of shipping, storage, handling, operations, decontamination, and disposal. LMP-103S has not been previously handled at a U.S. Launch Range. Requisite for this activity was an LMP-103S Risk Analysis Report and Ground Safety Plan. GSFC and WFF safety offices jointly developed safety documentation for application into the GPLD operation. The GPLD along with the GSFC Propulsion historical hydrazine loading experiences offer direct comparison between handling green propellant versus safety intensive, highly toxic hydrazine propellant. These described motives initiated the GPLD operation in order to investigate the handling and process safety variances in project resources between LMP-103S and typical in-space propellants. The GPLD risk reduction operation proved successful for many reasons including handling the green propellant at a U.S. Range, loading and pressurizing a flight-like tank, expelling the propellant, measuring the tank expulsion efficiency, and most significantly, GSFC propulsion personnel's new insight into the LMP-103S propellant handling details.

Propellant↗

NASA NTRS2016

Green Propellant Loading Demonstration at U.S. Range

Green Propellant↗

NASA NTRS2010

Relationships Among Lower Body Strength, Power, and Performance of Functional Tasks

There is a large degree of variability among crewmembers with respect to decrements in muscle strength and power following long duration spaceflight, ranging from 0 to approx.30% reductions. The purpose of this study was to investigate the influence of varying decrements in lower body muscle strength and power (relative to body weight) on the performance of 2 occupationally relevant tasks (ladder climb and supine egress & walk). Seventeen participants with leg strength similar to US crewmembers performed a leg press power test, an isokinetic knee extension strength test and they were asked to complete the 2 functional tasks as quickly as possible. On additional test days the participants were asked to repeat the functional tasks under 3 conditions where a different external load was applied each time using a weighted suit in order to experimentally manipulate participants strength/body weight and power/body weight ratios. The weight in the suit ranged from 20-120% of body weight and was distributed in proportion to limb segment weights to minimize changes in center of gravity. The ladder task consisted of climbing 40 rungs on a ladder treadmill as fast as possible. The supine egress & walk task consisted of rising from a supine position and walking through an obstacle course. Results show a relatively linear relationship between strength/body weight and task time and power/body weight with task time such that the fastest performance times are associated with higher strength and power with about half the variance in task time is accounted for by a single variable (either strength or power). For the average person, a 20% reduction in power/body weight (from 18 to 14.4 W/kg) induces an increase (slowing) of about 10 seconds in the ladder climb task from 14 to 24 seconds (approx.70%) and a slowing of the supine egress & walk task from 14 to 21 seconds (approx.50%). Similar relationships were observed with strength/body weight and task performance. For the average person, a 20% reduction in strength/body weight (from 2.1 to 1.7 Nm/kg) resulted in a slowing of the ladder climb from 10.5 to 24 seconds (approx.128%) and a slowing of the supine egress & walk from 11 to 20 seconds (approx.82%). These data suggest that the single variable of either low body muscle strength or power, relative to body weight is predictive of about 50% of the variance in task performance time, and that considerable slowing in task performance is associated with relatively typical decrements in muscle performance seen with long duration spaceflight. The observation of a relatively linear relationship between strength/power and task time suggests that across the full spectrum of initial crew strengths and typical decrements in strength previously observed, that task performance would be expected to be slowed following long duration spaceflight. These data will be confirmed in actual spaceflight with subsequent studies.

Ploutz-Snyder, Lori↗

NASA NTRS2023

Multiple Scattering Theory for Modeling Sonic Booms in Atmospheric Turbulence

Sonic booms are often modeled using Burgers equations accounting for dominant propagation effects. Scattering effects of turbulence, however, have not been incorporated into such equations, although these effects are ubiquitous in measured sonic booms. This paper formulates the mean scattering effects, including backscattering, using multiple scattering theory and ensemble averaging. It obtains an acousto-turbulence interaction energy representing the interaction of an acoustic wave field with a turbulence field. The interaction energy gives rise to a scattering wavenumber, a complex-valued correction to the free-space wavenumber. The scattering wavenumber leads to a dispersion and attenuation of the mean waveform due to backscattering, and can be obtained from a derived exact solution. An existing Burgers equation is extended to include the scattering effects of turbulence as an additional linear term. Numerical simulations of an N-wave and a low boom show that the mean scattering effects lead to shock thickening in the mean waveforms as well as reduce the peak amplitudes and total energy contents. The energy reduction is less severe in the low boom than in the N-wave and is dependent on the variance, correlation length, and thickness of the turbulence field.

sonic booms↗

NASA NTRS1986

A data base and analysis program for shuttle main engine dynamic pressure measurements

A dynamic pressure data base management system is described for measurements obtained from space shuttle main engine (SSME) hot firing tests. The data were provided in terms of engine power level and rms pressure time histories, and power spectra of the dynamic pressure measurements at selected times during each test. Test measurements and engine locations are defined along with a discussion of data acquisition and reduction procedures. A description of the data base management analysis system is provided and subroutines developed for obtaining selected measurement means, variances, ranges and other statistics of interest are discussed. A summary of pressure spectra obtained at SSME rated power level is provided for reference. Application of the singular value decomposition technique to spectrum interpolation is discussed and isoplots of interpolated spectra are presented to indicate measurement trends with engine power level. Program listings of the data base management and spectrum interpolation software are given. Appendices are included to document all data base measurements.

Coffin, T.↗

NASA NTRS2005

Global Precipitation Variations and Long-term Changes Derived from the GPCP Monthly Product

Global and large regional rainfall variations and possible long-term changes are examined using the 25-year (1979-2004) monthly dataset from the Global Precipitation Climatology Project (GPCP). The emphasis is to discriminate among the variations due to ENSO, volcanic events and possible long-term changes. Although the global change of precipitation in the data set is near zero, the data set does indicate an upward trend (0.13 mm/day/25yr) and a downward trend (-0.06 mm/day/25yr) over tropical oceans and lands (25S-25N), respectively. This corresponds to a 4% increase (ocean) and 2% decrease (land) during this time period. Techniques are applied to attempt to eliminate variations due to ENSO and major volcanic eruptions. The impact of the two major volcanic eruptions over the past 25 years is estimated to be about a 5% reduction in tropical rainfall. The modified data set (with ENSO and volcano effect removed) retains the same approximate change slopes, but with reduced variance leading to significance tests with results in the 90-95% range. Inter-comparisons between the GPCP, SSWI (1988-2004), and TRMM (1998-2004) rainfall products are made to increase or decrease confidence in the changes seen in the GPCP analysis.

Adler, Robert F.↗

NASA NTRS2014

NASA Tech Briefs, February 2014

Topics include: JWST Integrated Simulation and Test (JIST) Core; Software for Non-Contact Measurement of an Individual's Heart Rate Using a Common Camera; Rapid Infrared Pixel Grating Response Testbed; Temperature Measurement and Stabilization in a Birefringent Whispering Gallery Resonator; JWST IV and V Simulation and Test (JIST) Solid State Recorder (SSR) Simulator; Development of a Precision Thermal Doubler for Deep Space; Improving Friction Stir Welds Using Laser Peening; Methodology of Evaluating Margins of Safety in Critical Brazed Joints; Interactive Inventory Monitoring; Sensor for Spatial Detection of Single-Event Effects in Semiconductor-Based Electronics; Reworked CCGA-624 Interconnect Package Reliability for Extreme Thermal Environments; Current-Controlled Output Driver for Directly Coupled Loads; Bulk Metallic Glasses and Matrix Composites as Spacecraft Shielding; Touch Temperature Coating for Electrical Equipment on Spacecraft; Li-Ion Electrolytes Containing Flame-Retardant Additives; Autonomous Robotic Manipulation (ARM); CARVE Log; Platform Perspective Toolkit; Convex Hull-Based Plume and Anomaly Detection; Pre-Filtration of GOSAT Data Using Only Level 1 Data and an Intelligent Filter to Remove Low Clouds; Affordability Comparison Tool - ACT; "Ascent - Commemorating Shuttle" for iPad; Cassini Mission App; Light-Weight Workflow Engine: A Server for Executing Generic Workflows; Model for System Engineering of the CheMin Instrument; Timeline Central Concepts; Parallel Particle Filter Toolkit; Particle Filter Simulation and Analysis Enabling Non-Traditional Navigation; Quasi-Terminator Orbits for Mapping Small Primitive Bodies; The Subgrid-Scale Scalar Variance Under Supercritical Pressure Conditions; Sliding Gait for ATHLETE Mobility; and Automated Generation of Adaptive Filter Using a Genetic Algorithm and Cyclic Rule Reduction.

Source record↗

NASA NTRS1975

Canonical analysis and transformation of Skylab multispectral scanner data

The author has identified the following significant results. The original transformation matrix, C, had sixteen axes. However, the first three axes contained 98.83% of the variance contained within the transformation. The values for axes one, two, and three were 83.61%, 14.49%, and 0.72%, respectively. The result was an 81.25% reduction in data bulk. It is expected that using transformed data for classification will result in significant reductions in computer cost.

Mcmurtry, G. J.↗

NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗

NASA NTRS1972

An improved multiple linear regression and data analysis computer program package

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Sidik, S. M.↗

NASA NTRS1984

Reflectance Modeling

The main thrust of the work conducted is in the modeling of spectral responses of discontinuous conifer forest canopies, considered as collections of conical forms that are illuminated at an angle and cast shadows on contracting background. The three first-year objectives are: (1) modification of the variance-driven model to incorporate overlapping of shadows and crowns; (2) collection and analysis of photographic data to calibrate tree-spacing functions; and (3) collection of field data to determine height, spacing, and shape of conifers in open and closed stands in the Goosenest (California) test area. Modification of the invertible model uses a linear approximation to correct for the reduction in shadow and canopy area that occurs when shadows fall on crowns or tree crowns intersect. Comparison of the results of the approximation with a series of Monte Carlo simulations shows that the mean area covered by crowns or shadows is approximated quite accurately, but the variance calculated departs significantly from simulated values. The analysis of tree spacing patterns using air photos showed that spacing tended to be somewhat regular at a scale approximately equal to the average distance between trees, but at larger distance scales counts of trees in cells could be approximated acceptably by a Poisson (random) function.

Strahler, A. H.↗

NASA NTRS2015

Characterization of Machine Variability and Progressive Heat Treatment in Selective Laser Melting of Inconel 718

The absence of an economy of scale in spaceflight hardware makes additive manufacturing an immensely attractive option for propulsion components. As additive manufacturing techniques are increasingly adopted by government and industry to produce propulsion hardware in human-rated systems, significant development efforts are needed to establish these methods as reliable alternatives to conventional subtractive manufacturing. One of the critical challenges facing powder bed fusion techniques in this application is variability between machines used to perform builds. Even with implementation of robust process controls, it is possible for two machines operating at identical parameters with equivalent base materials to produce specimens with slightly different material properties. The machine variability study presented here evaluates 60 specimens of identical geometry built using the same parameters. 30 samples were produced on machine 1 (M1) and the other 30 samples were built on machine 2 (M2). Each of the 30-sample sets were further subdivided into three subsets (with 10 specimens in each subset) to assess the effect of progressive heat treatment on machine variability. The three categories for post-processing were: stress relief, stress relief followed by hot isostatic press (HIP), and stress relief followed by HIP followed by heat treatment per AMS 5664. Each specimen (a round, smooth tensile) was mechanically tested per ASTM E8. Two formal statistical techniques, hypothesis testing for equivalency of means and one-way analysis of variance (ANOVA), were applied to characterize the impact of machine variability and heat treatment on six material properties: tensile stress, yield stress, modulus of elasticity, fracture elongation, and reduction of area. This work represents the type of development effort that is critical as NASA, academia, and the industrial base work collaboratively to establish a path to certification for additively manufactured parts. For future flight programs, NASA and its commercial partners will procure parts from vendors who will use a diverse range of machines to produce parts and, as such, it is essential that the AM community develop a sound understanding of the degree to which machine variability impacts material properties.

Prater, T.↗

NASA NTRS2015

Characterization of Machine Variability and Progressive Heat Treatment in Selective Laser Melting of Inconel 718

The absence of an economy of scale in spaceflight hardware makes additive manufacturing an immensely attractive option for propulsion components. As additive manufacturing techniques are increasingly adopted by government and industry to produce propulsion hardware in human‐rated systems, significant development efforts are needed to establish these methods as reliable alternatives to conventional subtractive manufacturing. One of the critical challenges facing powder bed fusion techniques in this application is variability between machines used to perform builds. Even with implementation of robust process controls, it is possible for two machines operating at identical parameters with equivalent base materials to produce specimens with slightly different material properties. The machine variability study presented here evaluates 60 specimens of identical geometry built using the same parameters. 30 samples were produced on machine 1 (M1) and the other 30 samples were built on machine 2 (M2). Each of the 30‐sample sets were further subdivided into three subsets (with 10 specimens in each subset) to assess the effect of progressive heat treatment on machine variability. The three categories for post‐processing were: stress relief, stress relief followed by hot isostatic press (HIP), and stress relief followed by HIP followed by heat treatment per AMS 5664. Each specimen (a round, smooth tensile) was mechanically tested per ASTM E8. Two formal statistical techniques, hypothesis testing for equivalency of means and one‐way analysis of variance (ANOVA), were applied to characterize the impact of machine variability and heat treatment on six material properties: tensile stress, yield stress, modulus of elasticity, fracture elongation, and reduction of area. This work represents the type of development effort that is critical as NASA, academia, and the industrial base work collaboratively to establish a path to certification for additively manufactured parts. For future flight programs, NASA and its commercial partners will procure parts from vendors who will use a diverse range of machines to produce parts and, as such, it is essential that the AM community develop a sound understanding of the degree to which machine variability impacts material properties.

Prater, Tracie↗

NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗