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Results for “Constrained regression”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Sources of Sodium in the Lunar Exosphere: Modeling Using Ground-Based Observations of Sodium Emission and Spacecraft Data of the Plasma

Observations of the equatorial lunar sodium emission are examined to quantify the effect of precipitating ions on source rates for the Moon's exospheric volatile species. Using a model of exospheric sodium transport under lunar gravity forces, the measured emission intensity is normalized to a constant lunar phase angle to minimize the effect of different viewing geometries. Daily averages of the solar Lyman alpha flux and ion flux are used as the input variables for photon-stimulated desorption (PSD) and ion sputtering, respectively, while impact vaporization due to the micrometeoritic influx is assumed constant. Additionally, a proxy term proportional to both the Lyman alpha and to the ion flux is introduced to assess the importance of ion-enhanced diffusion and/or chemical sputtering. The combination of particle transport and constrained regression models demonstrates that, assuming sputtering yields that are typical of protons incident on lunar soils, the primary effect of ion impact on the surface of the Moon is not direct sputtering but rather an enhancement of the PSD efficiency. It is inferred that the ion-induced effects must double the PSD efficiency for flux typical of the solar wind at 1 AU. The enhancement in relative efficiency of PSD due to the bombardment of the lunar surface by the plasma sheet ions during passages through the Earth's magnetotail is shown to be approximately two times higher than when it is due to solar wind ions. This leads to the conclusion that the priming of the surface is more efficiently carried out by the energetic plasma sheet ions.

Sarantos, Menelaos↗
NASA NTRS1978

Microwave radiometer measurement of water vapor path delay: Data reduction techniques

Data reduction techniques are developed to compensate for water vapor path delay, a limiting error source in geodetic measurements made with very long baseline interferometry and in radio ranging to spacecraft. It is shown that water vapor path delay is proportional to a linear combination of saturation-corrected sky brightness temperatures, measured on and off the water vapor line. The effects of emission from liquid water droplets in clouds as well as most of the oxygen emission are removed by the off-line channel. Sky brightness temperatures are saturation-corrected or 'linearized' using estimates of effective sky temperatures made from surface temperature. Tipping curves are used to remove instrumental error. Coefficients are found by two methods: from a regression analysis of measured brightness temperatures versus radiosonde measured delay, and from a regression analysis of theoretical brightness temperatures versus radiosonde measured delay. In each case the coefficients are adjusted for differing climatic conditions by measurements of surface temperature, pressure, and relative humidity. Regression solutions are constrained to remove liquid water contributions and to give the correct slope for radiometer versus radiosonde path delay.

Claflin, E. S.↗
NASA NTRS2006

A LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) FOR NONLINEAR SYSTEM IDENTIFICATION

Identification of parametric nonlinear models involves estimating unknown parameters and detecting its underlying structure. Structure computation is concerned with selecting a subset of parameters to give a parsimonious description of the system which may afford greater insight into the functionality of the system or a simpler controller design. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear systems. The LASSO minimises the residual sum of squares by the addition of a 1 penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. The performance of this LASSO structure detection method was evaluated by using it to estimate the structure of a nonlinear polynomial model. Applicability of the method to more complex systems such as those encountered in aerospace applications was shown by identifying a parsimonious system description of the F/A-18 Active Aeroelastic Wing using flight test data.

Kukreja, Sunil L.↗
NASA NTRS2007

Aeroelastic Model Structure Computation for Envelope Expansion

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear aeroelastic systems. The LASSO minimises the residual sum of squares by the addition of an l(sub 1) penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 Active Aeroelastic Wing using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Kukreja, Sunil L.↗
NASA NTRS2007

Salience Assignment for Multiple-Instance Regression

We present a Multiple-Instance Learning (MIL) algorithm for determining the salience of each item in each bag with respect to the bag's real-valued label. We use an alternating-projections constrained optimization approach to simultaneously learn a regression model and estimate all salience values. We evaluate this algorithm on a significant real-world problem, crop yield modeling, and demonstrate that it provides more extensive, intuitive, and stable salience models than Primary-Instance Regression, which selects a single relevant item from each bag.

regression↗
NASA NTRS2007

Aeroelastic Model Structure Computation for Envelope Expansion

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion that may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of non-linear aeroelastic systems. The LASSO minimises the residual sum of squares with the addition of an l(Sub 1) penalty term on the parameter vector of the traditional l(sub 2) minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudo-linear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Active Aeroelastic Wing project using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Kukreja, Sunil L.↗
NASA NTRS1996

System Synthesis in Preliminary Aircraft Design using Statistical Methods

This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).

DeLaurentis, Daniel↗
NASA NTRS1996

System Synthesis in Preliminary Aircraft Design Using Statistical Methods

This paper documents an approach to conceptual and early preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically Design of Experiments (DOE) and Response Surface Methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an Overall Evaluation Criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in an innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting in solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a High Speed Civil Transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabilistic designs (and eventually robust ones).

DeLaurentis, Daniel↗
NASA NTRS2011

Development of Head Injury Assessment Reference Values Based on NASA Injury Modeling

NASA is developing a new capsule-based, crewed vehicle that will land in the ocean, and the space agency desires to reduce the risk of injury from impact during these landings. Because landing impact occurs for each flight and the crew might need to perform egress tasks, current injury assessment reference values (IARV) were deemed insufficient. Because NASCAR occupant restraint systems are more effective than the systems used to determine the current IARVs and are similar to NASA s proposed restraint system, an analysis of NASCAR impacts was performed to develop new IARVs that may be more relevant to NASA s context of vehicle landing operations. Head IARVs associated with race car impacts were investigated by completing a detailed analysis of all of the 2002-2008 NASCAR impact data. Specific inclusion and exclusion criteria were used to select 4071 impacts from the 4015 recorder files provided (each file could contain multiple impact events). Of the 4071 accepted impacts, 274 were selected for numerical simulation using a custom NASCAR restraint system and Humanetics Hybrid-III 50th percentile numerical dummy model in LS-DYNA. Injury had occurred in 32 of the 274 selected impacts, and 27 of those injuries involved the head. A majority of the head injuries were mild concussions with or without brief loss of consciousness. The 242 non-injury impacts were randomly selected and representative of the range of crash dynamics present in the total set of 4071 impacts. Head dynamics data (head translational acceleration, translational change in velocity, rotational acceleration, rotational velocity, HIC-15, HIC-36, and the Head 3ms clip) were filtered according to SAE J211 specifications and then transformed to a log scale. The probability of head injury was estimated using a separate logistic regression analysis for each log-transformed predictor candidate. Using the log transformation constrains the estimated probability of injury to become negligible as IARVs approach zero. For the parameters head translational acceleration, head translational velocity change, head rotational acceleration, HIC-15, and HIC-36, conservative values (in the lower 95% confidence interval) that gave rise to a 5% risk of any injury occurring were estimated as 40.0 G, 7.9 m/s, 2200 rad/s2, 98.4, and 77.4 respectively. Because NASA is interested in the consequence of any particular injury on the ability of the crew to perform egress tasks, the head injuries that occurred in the NASCAR dataset were classified according to a NASA-developed scale (Classes I - III) for operationally relevant injuries, which classifies injuries on the basis of their operational significance. Additional analysis of the data was performed to determine the probability of each injury class occurring, and this was estimated using an ordered probit model. For head translational acceleration, head translational velocity change, head rotational acceleration, head rotational velocity, HIC-36, and head 3ms clip, conservative values of IARVs that produced a 5% risk of Class II injury were estimated as 50.7 G, 9.5 m/s, 2863 rad/s2, 11.0 rad/s, 30.3, and 46.4 G respectively. The results indicate that head IARVs developed from the NASCAR dataset may be useful to protect crews during landing impact.

Somers, Jeffrey T.↗
NASA NTRS2013

Statistical Issues in Galaxy Cluster Cosmology

The number and growth of massive galaxy clusters are sensitive probes of cosmological structure formation. Surveys at various wavelengths can detect clusters to high redshift, but the fact that cluster mass is not directly observable complicates matters, requiring us to simultaneously constrain scaling relations of observable signals with mass. The problem can be cast as one of regression, in which the data set is truncated, the (cosmology-dependent) underlying population must be modeled, and strong, complex correlations between measurements often exist. Simulations of cosmological structure formation provide a robust prediction for the number of clusters in the Universe as a function of mass and redshift (the mass function), but they cannot reliably predict the observables used to detect clusters in sky surveys (e.g. X-ray luminosity). Consequently, observers must constrain observable-mass scaling relations using additional data, and use the scaling relation model in conjunction with the mass function to predict the number of clusters as a function of redshift and luminosity.

Galaxy↗
NASA NTRS1982

Sensitivity of constrained linear inversions to the selection of the Lagrange multiplier

The influence of the choice of the Lagrange multiplier on constrained linear inversions is explored, with reference made to applications in inferring the columnar aerosol size distributions from spectral aerosol optical depth measurements. A range of the Lagrange multiplier is examined to find all positive solutions for the solution vector, which represents modifying factors to the assumed form of the size distribution. An iterative method is devised to constrain the calculations to consideration of only positive quantities and a requirement that the regression fit to data be consistent with measurement errors. The determination of the variances and covariances is formulated and applied to existing data sets for optical depth. Variances in the solution are found to be large for particle radii when the information content of the data is small.

King, M. D.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-14454-DND

FUN3D Core Capabilities - Established as a research code in late 1980’s; now supports numerous internal and external efforts across the speed range - Solves 2D/3D steady and unsteady Euler and RANS equations on node-based mixed element grids for compressible and incompressible flows - General dynamic mesh capability: any combination of rigid / overset / morphing grids, including 6-DOF effects - Aeroelastic modeling using mode shapes, full FEM, CC, etc. - Constrained / multipoint adjoint-based design and mesh adaptation - Distributed development team using agile/extreme software practices including 24/7 regression and performance testing - Capabilities fully integrated, online documentation, training videos, tutorials

Eric J Nielsen↗
NASA NTRSDate not supplied

NASA LaRC Contribution to the High Angle Working Group of the Third Aeroelastic Prediction Workshop: BSCW Shock Buffet

FUN3D Core Capabilities - Established as a research code in late 1980s; now supports numerous internal and external efforts across the speed range - Solves 2D/3D steady and unsteady Euler and RANS equations on node-based mixed element grids for compressible and incompressible flows - General dynamic mesh capability: any combination of rigid / overset / morphing grids, including 6-DOF effects - Aeroelastic modeling using mode shapes, full FEM, etc. - Constrained / multipoint adjoint-based design and mesh adaptation - Distributed development team using agile/extreme software practices including 24/7 regression, performance testing - Capabilities fully integrated, online documentation, training videos, tutorials

Pawel Chwalowski↗
NASA NTRS2005

Attribution of Recovery in Lower-Stratospheric Ozone

Multiple satellite and ground-based observations provide consistent evidence that the thickness of Earth's protective ozone layer has stopped declining since 1997, close to the time of peak stratospheric halogen loading. Regression analyses with Effective Equivalent Stratospheric Chlorine (EESC) in conjunction with further analyses using more sophisticated photochemical model calculations constrained by satellite data demonstrate that the cessation of ozone depletion between 18-25 km altitude is consistent with a leveling off of stratospheric abundances of chlorine and bromine, due to the Montreal Protocol and its amendments. However, ozone increases in the lowest part of the stratosphere, from the tropopause to 18 km, account for about half of the improvement in total column ozone during the past 9 years at northern hemisphere mid-latitudes. The increase in ozone for altitudes below 18 km is most likely driven by changes in transport, rather than driven by declining chlorine and bromine. Even with this evidence that the Montreal Protocol and its amendments are having the desired, positive effect on ozone above 18 km, total column ozone is recovering faster than expected due to the apparent transport driven changes at lower altitudes. Accurate prediction of future levels of stratospheric ozone will require comprehensive understanding of the factors that drive temporal changes at various altitudes, and partitioning of the recent transport-driven increases between natural variability and changes in atmospheric structure perhaps related to anthropogenic climate change.

Yang, Eun-Su↗
NASA NTRS2006

Attribution of Recovery in Lower-stratospheric Ozone

Multiple satellite and ground-based observations provide consistent evidence that the thickness of Earth's protective ozone layer has stopped declining since 1997, close to the time of peak stratospheric halogen loading. Regression analyses with Effective Equivalent Stratospheric Chlorine (EESC) in conjunction with further analyses using more sophisticated photochemical model calculations constrained by satellite data demonstrate that the cessation of ozone depletion between 18-25 km altitude is consistent with a leveling off of stratospheric abundances of chlorine and bromine, due to the Montreal Protocol and its amendments. However, ozone increases in the lowest part of the stratosphere, from the tropopause to 18 km, account for about half of the improvement in total column ozone during the past 9 years at northern hemisphere mid-latitudes. The increase in ozone for altitudes below 18 km is most likely driven by changes in transport, rather than driven by declining chlorine and bromine. Even with this evidence that the Montreal Protocol and its amendments are having the desired, positive effect on ozone above 18 km, total column ozone is recovering faster than expected due to the apparent transport driven changes at lower altitudes. Accurate prediction of future levels of stratospheric ozone will require comprehensive understanding of the factors that drive temporal changes at various altitudes, and partitioning of the recent transport-driven increases between natural variability and changes in atmospheric structure perhaps related to anthropogenic climate change.

Yang, Eun-Su↗
NASA NTRS2021

Global Intercomparison of hyper-resolution ECOSTRESS coastal sea surface temperature measurements from the Space Station with VIIRS-N20

The ECOSTRESS multi-channel thermal radiometer on the Space Station has an unprecedented spatial resolution of 70 m and a return time of hours to 5 days. It resolves details of oceanographic features not detectable in imagery from MODIS or VIIRS, and has open-ocean coverage, unlike Landsat. We calibrated two years of ECOSTRESS sea surface temperature observations with L2 data from VIIRS-N20 (2019–2020) worldwide but especially focused on important upwelling systems currently undergoing climate change forcing. Unlike operational SST products from VIIRS-N20, the ECOSTRESS surface temperature algorithm does not use a regression approach to determine temperature, but solves a set of simultaneous equations based on first principles for both surface temperature and emissivity. We compared ECOSTRESS ocean temperatures to well-calibrated clear sky satellite measurements from VIIRS-N20. Data comparisons were constrained to those within 90 min of one another using co-located clear sky VIIRS and ECOSTRESS pixels. ECOSTRESS ocean temperatures have a consistent 1.01 ◦C negative bias relative to VIIRS-N20, although deviation in brightness temperatures within the 10.49 and 12.01 µm bands were much smaller. As an alternative, we compared the performance of NOAA, NASA, and U.S. Navy operational split-window SST regression algorithms taking into consideration the statistical limitations imposed by intrinsic SST spatial autocorrelation and applying corrections on brightness temperatures. We conclude that standard bias-correction methods using already validated and well-known algorithms can be applied to ECOSTRESS SST data, yielding highly accurate products of ultra-high spatial resolution for studies of biological and physical oceanography in a time when these are needed to properly evaluate regional and even local impacts of climate change.

Nicolas Weidberg↗
NASA NTRS2000

Influence of central set on anticipatory and triggered grip-force adjustments

The effects of predictability of load magnitude on anticipatory and triggered grip-force adjustments were studied as nine normal subjects used a precision grip to lift, hold, and replace an instrumented test object. Experience with a predictable stimulus has been shown to enhance magnitude scaling of triggered postural responses to different amplitudes of perturbations. However, this phenomenon, known as a central-set effect, has not been tested systematically for grip-force responses in the hand. In our study, predictability was manipulated by applying load perturbations of different magnitudes to the test object under conditions in which the upcoming load magnitude was presented repeatedly or under conditions in which the load magnitudes were presented randomly, each with two different pre-load grip conditions (unconstrained and constrained). In constrained conditions, initial grip forces were maintained near the minimum level necessary to prevent pre-loaded object slippage, while in unconstrained conditions, no initial grip force restrictions were imposed. The effect of predictable (blocked) and unpredictable (random) load presentations on scaling of anticipatory and triggered grip responses was tested by comparing the slopes of linear regressions between the imposed load and grip response magnitude. Anticipatory and triggered grip force responses were scaled to load magnitude in all conditions. However, regardless of pre-load grip force constraint, the gains (slopes) of grip responses relative to load magnitudes were greater when the magnitude of the upcoming load was predictable than when the load increase was unpredictable. In addition, a central-set effect was evidenced by the fewer number of drop trials in the predictable relative to unpredictable load conditions. Pre-load grip forces showed the greatest set effects. However, grip responses showed larger set effects, based on prediction, when pre-load grip force was constrained to lower levels. These results suggest that anticipatory processes pertaining to load magnitude permit the response gain of both voluntary and triggered rapid grip force adjustments to be set, at least partially, prior to perturbation onset. Comparison of anticipatory set effects for reactive torque and lower extremity EMG postural responses triggered by surface translation perturbations suggests a more general rule governing anticipatory processes.

Non-NASA Center↗