Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “multivariate analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Tailored Excitation for Multivariable Stability-Margin Measurement Applied to the X-31A Nonlinear Simulation

Safety and productivity of the initial flight test phase of a new vehicle have been enhanced by developing the ability to measure the stability margins of the combined control system and vehicle in flight. One shortcoming of performing this analysis is the long duration of the excitation signal required to provide results over a wide frequency range. For flight regimes such as high angle of attack or hypersonic flight, the ability to maintain flight condition for this time duration is difficult. Significantly reducing the required duration of the excitation input is possible by tailoring the input to excite only the frequency range where the lowest stability margin is expected. For a multiple-input/multiple-output system, the inputs can be simultaneously applied to the control effectors by creating each excitation input with a unique set of frequency components. Chirp-Z transformation algorithms can be used to match the analysis of the results to the specific frequencies used in the excitation input. This report discusses the application of a tailored excitation input to a high-fidelity X-31A linear model and nonlinear simulation. Depending on the frequency range, the results indicate the potential to significantly reduce the time required for stability measurement.

Bosworth, John T.↗
NASA NTRS1991

Helicopter air resonance modeling and suppression using active control

A coupled rotor/fuselage helicopter analysis with the important effects of blade torsional flexibility, unsteady aerodynamics, and forward flight is presented. Using this mathematical model, a nominal configuration is selected with an air resonance instability throughout most of its flight envelope. A multivariable compensator is then designed using two swashplate inputs and a single-body roll rate measurement. The controller design is based on the linear quadratic Gaussian technique and the loop transfer recovery method. The controller is shown to suppress the air resonance instability throughout a wide range of helicopter loading conditions and forward flight speeds.

Takahashi, M. D.↗
NASA NTRS2014

National Space Biomedical Research Institute (NSBRI) JSC Summer Projects

This project optimized the calorie content in a breakfast meal replacement bar for the Advanced Food Technology group. Use of multivariable optimization yielded the highest weight savings possible while simultaneously matching NASA Human Standards nutritional guidelines. The scope of this research included the study of shelf-life indicators such as water activity, moisture content, and texture analysis. Key metrics indicate higher protein content, higher caloric density, and greater mass savings as a result of the reformulation process. The optimization performed for this study demonstrated wide application to other food bars in the Advanced Food Technology portfolio. Recommendations for future work include shelf life studies on bar hardening and overall acceptability data over increased time frames and temperature fluctuation scenarios.

Dowdy, Forrest Ryan↗
DOE OSTI2020

Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.

Nandanoori, Sai Pushpak↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2023

Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation

Correct function and calibration of instrumentation is a crucial assumption for any scientific experiment. One such instrument, tandem inductively coupled plasma mass spectrometer (ICP-MS/MS), has in-depth calibration settings that range across 30+ different parameters, making it difficult to determine optimal conditions without expertise and some degree of trial and error. Often, these settings are hand-tuned, a time-intensive process prone to local maxima and human error. While some automation is available, the automation also may favor local optimizations over a global optimum. In addition to these difficulties, day to day instrument variability can further complicate the calibration process. We propose a solution to this problem as a machine learning (ML) algorithm that learns how each parameter helps determine the calibration sensitivity across several elements, and re-weights parameters over time as instrument variability changes (e.g., a global neural network (NN) with a time-dependent transfer learning (TL) component). This model would be able to generate a surface of predicted calibration sensitivities and their respective parameters, and a simple multivariate algorithm would be able to pull out the optimum results with the settings associated with them. Here-in, we describe our initial findings in working towards this goal, including data extraction from historical files, exploratory data analysis, and some initial model building to better describe the data and the feasibility of our goal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Editorial: Applications of spectroscopy and chemometrics in nuclear materials analysis

Optical analysis techniques, including spectroscopy and image analysis, have many advantages when applied to the study of nuclear materials. They require small sample sizes, can be performed remotely, and can be proceduralized through consistent practice. Most importantly, they provide a wealth of information by generating multivariate data. For example, ultraviolet–visible–near-infrared absorbance spectroscopy of actinides in aqueous and organic solutions is dependent on the oxidation state, anionic complexation, and temperature. These variables are important for solution-based separation processes, and sensitivity to these factors, combined with online monitoring, can drive the efficiency and control of these processes. The morphology and chemical composition of actinide particles can also provide a vital clue to the mechanisms by which the particles were formed, providing forensic information on the origins of the particles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1998

Integrated Data Visualization and Virtual Reality Tool

The Integrated Data Visualization and Virtual Reality Tool (IDVVRT) Phase II effort was for the design and development of an innovative Data Visualization Environment Tool (DVET) for NASA engineers and scientists, enabling them to visualize complex multidimensional and multivariate data in a virtual environment. The objectives of the project were to: (1) demonstrate the transfer and manipulation of standard engineering data in a virtual world; (2) demonstrate the effects of design and changes using finite element analysis tools; and (3) determine the training and engineering design and analysis effectiveness of the visualization system.

Dryer, David A.↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2023

Revisiting Néel 60 years on: The magnetic anisotropy of L1 0 FeNi (tetrataenite)

The magnetocrystalline anisotropy energy of atomically ordered L1 0 FeNi (the meteoritic mineral tetrataenite) is studied within a first-principles electronic structure framework. Two compositions are examined: equiatomic Fe 0.5 Ni 0.5 and an Fe-rich composition, Fe 0.56 Ni 0.44 . It is confirmed that, for the single crystals modeled in this work, the leading-order anisotropy coefficient K 1 dominates the higher-order coefficients K 2 and K 3 . To enable comparison with experiment, the effects of both imperfect atomic long-range order and finite temperature are included. While our computational results initially appear to undershoot the measured experimental values for this system, careful scrutiny of the original analysis due to Néel et al. [J. Appl. Phys. 35, 873 (1964)] suggests that our computed value of K 1 is, in fact, consistent with experimental values, and that the noted discrepancy has its origins in the nanoscale polycrystalline, multivariant nature of experimental samples, that yields much larger values of K 2 and K 3 than expected a priori. These results provide fresh insight into the existing discrepancies in the literature regarding the value of tetrataenite’s uniaxial magnetocrystalline anisotropy in both natural and synthetic samples.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Improving inference with matrix elements and machine learning

Particle physics processes bring together a high-energy amplitude described by quantum field theory and nonperturbative effects and detector interactions described by complex computer simulations. We review some recently developed multivariate inference techniques that leverage this structure and combine matrix-element information with machine learning. Automated by the MadMiner package, the new techniques have been applied to multiple problems in particle physics, allowing for stronger limits than traditional analysis methods and showing their potential to improve the sensitivity of the LHC legacy measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1988

Design of a simple active controller to suppress helicopter air resonance

A coupled rotor/fuselage helicopter analysis with the important effects of blade torsional flexibility, unsteady aerodynamics, and forward flight is presented. Using this mathematical model, a nominal configuration is selected that experiences an air resonance instability throughout most of its flight envelope. A simple multivariable compensator using conventional swashplate inputs and a single body roll rate measurement is then designed. The controller design is based on a linear estimator in conjunction with optimal feedback gains, and the design is done in the frequency domain using the Loop Transfer Recovery method. The controller is shown to suppress the air resonance instability throughout wide range helicopter loading conditions and forward flight speeds.

Takahashi, M. D.↗
NASA NTRS1992

A simplified dynamic model of the T700 turboshaft engine

A simplified open-loop dynamic model of the T700 turboshaft engine, valid within the normal operating range of the engine, is developed. This model is obtained by linking linear state space models obtained at different engine operating points. Each linear model is developed from a detailed nonlinear engine simulation using a multivariable system identification and realization method. The simplified model may be used with a model-based real time diagnostic scheme for fault detection and diagnostics, as well as for open loop engine dynamics studies and closed loop control analysis utilizing a user generated control law.

Duyar, Ahmet↗
NASA NTRS2018

Prediction and Predictability of the Madden Julian Oscillation in the NASA GEOS-5 Seasonal-to-Subseasonal System

In this study, we examine the prediction skill and predictability of the Madden Julian Oscillation (MJO) in a recent version of the NASA GEOS-5 atmosphere-ocean coupled model run at at 1/2 degree horizontal resolution. The results are based on a suite of hindcasts produced as part of the NOAA SubX project, consisting of seven ensemble members initialized every 5 days for the period 1999-2015. The atmospheric initial conditions were taken from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the ocean and the sea ice were taken from a GMAO ocean analysis. The land states were initialized from the MERRA-2 land output, which is based on observation-corrected precipitation fields. We investigated the MJO prediction skill in terms of the bivariate correlation coefficient for the real-time multivariate MJO (RMM) indices. The correlation coefficient stays at or above 0.5 out to forecast lead times of 26-36 days, with a pronounced increase in skill for forecasts initialized from phase 3, when the MJO convective anomaly is located in the central tropical Indian Ocean. A corresponding estimate of the upper limit of the predictability is calculated by considering a single ensemble member as the truth and verifying the ensemble mean of the remaining members against that. The predictability estimates fall between 35-37 days (taken as forecast lead when the correlation reaches 0.5) and are rather insensitive to the initial MJO phase. The model shows slightly higher skill when the initial conditions contain strong MJO events compared to weak events, although the difference in skill is evident only from lead 1 to 20. Similar to other models, the RMM-index-based skill arises mostly from the circulation components of the index. The skill of the convective component of the index drops to 0.5 by day 20 as opposed to day 30 for circulation fields. The propagation of the MJO anomalies over the Maritime Continent does not appear problematic in the GEOS-5 hindcasts implying that the Maritime Continent predictability barrier may not be a major concern in this model. Finally, the MJO prediction skill in this version of GEOS-5 is superior to that of the current seasonal prediction system at the GMAO; this could be partly attributed to a slightly better representation of the MJO in the free running version of this model and partly to the improved atmospheric initialization from MERRA-2.

Achuthavarier, Deepthi↗
DOE OSTI2023

Environmental exposure to industrial air pollution is associated with decreased male fertility

Objective: To understand how chronic exposure to industrial air pollution is associated with male fertility through semen parameters. Design: Retrospective cohort study. Subjects: Men in the Subfertility, Health and Assisted Reproduction cohort who underwent a semen analysis 2005-2017 with ≥1 measured semen parameter (N=21,563). Intervention(s): Residential histories for each man were constructed using locations from administrative records linked through the Utah Population Database. Industrial facilities with air emissions of nine endocrine disrupting compound chemical classes were identified from the Environmental Protection Agency Risk-Screening Environmental Indicators microdata. Chemical levels were linked with residential histories for the 5 years prior to each semen analysis. Main Outcome Measures: Semen analyses were classified as azoospermic or oligozoospermic (< 15 M/mL) using World Health Organization cutoffs for concentration. Bulk semen parameters such as concentration, total count, ejaculate volume, total motility, total motile count, and total progressive motile count were also measured. Multivariable regression models with robust standard errors were used to associate exposure quartiles for each of the nine chemical classes with each semen parameter, adjusting for age, race, and ethnicity, as well as neighborhood socioeconomic disadvantage. Results: After adjustment for demographic covariates, several chemical classes were associated with azoospermia and decreased total motility and volume. For exposure in the 4th relative to 1st quartile, significant associations were observed for acrylonitrile (β total motility = -0.87 pp), aromatic hydrocarbons (odds ratio [OR]azoospermia = 1.53; β volume = -0.14 mL), dioxins (OR azoospermia = 1.31; β volume = -0.09 mL; β total motility = -2.65 pp), heavy metals (β total motility = -2.78pp), organic solvents (OR azoospermia = 1.75; β volume = -0.10 mL), organochlorines (OR azoospermia = 2.09; β volume = -0.12 mL), phthalates (OR azoospermia = 1.44; β volume = -0.09 mL; β total motility = -1.21 pp), and silver particles (OR azoospermia = 1.64; β volume = -0.11 mL). All semen parameters significantly decreased with increasing socioeconomic disadvantage. Men who lived in the most disadvantaged areas had concentration, volume, and total motility of 6.70 M/mL, 0.13 mL, and 1.79 pp lower, respectively. Count, motile count, and total progressive motile count all decreased by 30–34 M.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Evidence for top quark production in nucleus-nucleus collisions

We report droplets of quark-gluon plasma (QGP), an exotic state of strongly interacting quantum chromodynamics (QCD) matter, are routinely produced in heavy nuclei high-energy collisions. Although the experimental signatures marked a paradigm shift away from expectations of a weakly coupled QGP, a challenge remains as to how the locally deconfined state with a lifetime of a few fm can be resolved. The only colored particle that decays mostly within the QGP is the top quark. Here we demonstrate, for the first time, that top quark decay products are identified, irrespective of whether interacting with the medium (bottom quarks) or not (leptonically decaying W bosons). Using 1.7±0.1 nb -1 of lead-lead (A = 208) collision data recorded by the CMS experiment at a nucleon-nucleon center-of-mass energy of 5.02 TeV, we report evidence of top quark pair ($t\bar{t}$) production. Dilepton final states are selected, and the cross section ($σ_{t\bar{t}}$) is measured from a likelihood fit to a multivariate discriminator using lepton kinematic variables. The $σ_{t\bar{t}}$ measurement is additionally performed considering the jets originating from the hadronization of bottom quarks, which improve the sensitivity to the $t\bar{t}$ signal process. After background subtraction and analysis corrections, the measured $σ_{t\bar{t}}$ is 2.56 ± 0.82(tot) and 2.02 ± 0.69(tot)μb in the two cases, respectively, consistent with predictions from perturbative QCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1994

Hypersonic vehicle model and control law development using H(infinity) and micron synthesis

The control system design for a Single Stage To Orbit (SSTO) air breathing vehicle will be central to a successful mission because a precise ascent trajectory will preserve narrow payload margins. The air breathing propulsion system requires the vehicle to fly roughly halfway around the Earth through atmospheric turbulence. The turbulence, the high sensitivity of the propulsion system to inlet flow conditions, the relatively large uncertainty of the parameters characterizing the vehicle, and continuous acceleration make the problem especially challenging. Adequate stability margins must be provided without sacrificing payload mass since payload margins are critical. Therefore, a multivariable control theory capable of explicitly including both uncertainty and performance is needed. The H(infinity) controller in general provides good robustness but can result in conservative solutions for practical problems involving structured uncertainty. Structured singular value mu framework for analysis and synthesis is potentially much less conservative and hence more appropriate for problems with tight margins. An SSTO control system requires: highly accurate tracking of velocity and altitude commands while limiting angle-of-attack oscillations, minimized control power usage, and a stabilized vehicle when atmospheric turbulence and system uncertainty are present. The controller designs using H(infinity) and mu-synthesis procedures were compared. An integrated flight/propulsion dynamic mathematical model of a conical accelerator vehicle was linearized as the vehicle accelerated through Mach 8. Vehicle acceleration through the selected flight condition gives rise to parametric variation that was modeled as a structured uncertainty. The mu-analysis approach was used in the frequency domain to conduct controller analysis and was confirmed by time history plots. Results demonstrate the inherent advantages of the mu framework for this class of problems.

Gregory, Irene M.↗
NASA NTRS1994

Flight Dynamics and Control of Elastic Hypersonic Vehicles Uncertainty Modeling

It has been shown previously that hypersonic air-breathing aircraft exhibit strong aeroelastic/aeropropulsive dynamic interactions. To investigate these, especially from the perspective of the vehicle dynamics and control, analytical expressions for key stability derivatives were derived, and an analysis of the dynamics was performed. In this paper, the important issue of model uncertainty, and the appropriate forms for representing this uncertainty, is addressed. It is shown that the methods suggested in the literature for analyzing the robustness of multivariable feedback systems, which as a prerequisite to their application assume particular forms of model uncertainty, can be difficult to apply on real atmospheric flight vehicles. Also, the extent to which available methods are conservative is demonstrated for this class of vehicle dynamics.

Chavez, Frank R.↗