Search NASA⌕ Search

SEARCH · Search NASA

Results for “Surrogate Modelling”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Intelligent Planning and Scheduling for Controlled Life Support Systems

Planning in Controlled Ecological Life Support Systems (CELSS) requires special look ahead capabilities due to the complex and long-term dynamic behavior of biological systems. This project characterizes the behavior of CELSS, identifies the requirements of intelligent planning systems for CELSS, proposes the decomposition of the planning task into short-term and long-term planning, and studies the crop scheduling problem as an initial approach to long-term planning. CELSS is studied in the realm of Chaos. The amount of biomass in the system is modeled using a bounded quadratic iterator. The results suggests that closed ecological systems can exhibit periodic behavior when imposed external or artificial control. The main characteristics of CELSS from the planning and scheduling perspective are discussed and requirements for planning systems are given. Crop scheduling problem is identified as an important component of the required long-term lookahead capabilities of a CELSS planner. The main characteristics of crop scheduling are described and a model is proposed to represent the problem. A surrogate measure of the probability of survival is developed. The measure reflects the absolute deviation of the vital reservoir levels from their nominal values. The solution space is generated using a probability distribution which captures both knowledge about the system and the current state of affairs at each decision epoch. This probability distribution is used in the context of an evolution paradigm. The concepts developed serve as the basis for the development of a simple crop scheduling tool which is used to demonstrate its usefulness in the design and operation of CELSS.

Leon, V. Jorge↗
NASA NTRS1999

A Probabilistic Approach of Incorporating Safety and Reliability in System Designs for a Manned Mission to Mars

Conceptual stages in mission design often lack the input of quantitative safety and reliability assessments, simply because failure rates or other data are not yet available for systems that have not yet been designed. Absence of such data should not, however, prevent the development of a quantitative risk models with placeholders for missing data. Functions (that is, actions the systems must perform) in mission design will eventually require system probabilities of success, and there could be much learned from surrogate data, adequately bounded in uncertainty, used in a large event tree model of a complex mission.

Railsback, Jan W.↗
NASA NTRS2016

The Impact of Truth Surrogate Variance on Quality Assessment/Assurance in Wind Tunnel Testing

Minimum data volume requirements for wind tunnel testing are reviewed and shown to depend on error tolerance, response model complexity, random error variance in the measurement environment, and maximum acceptable levels of inference error risk. Distinctions are made between such related concepts as quality assurance and quality assessment in response surface modeling, as well as between precision and accuracy. Earlier research on the scaling of wind tunnel tests is extended to account for variance in the truth surrogates used at confirmation sites in the design space to validate proposed response models. A model adequacy metric is presented that represents the fraction of the design space within which model predictions can be expected to satisfy prescribed quality specifications. The impact of inference error on the assessment of response model residuals is reviewed. The number of sites where reasonably well-fitted response models actually predict inadequately is shown to be considerably less than the number of sites where residuals are out of tolerance. The significance of such inference error effects on common response model assessment strategies is examined.

DeLoach, Richard↗
NASA NTRS1991

Interpretation of snow-climate feedback as produced by 17 general circulation models

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Cess, R. D.↗
NASA NTRS2020

Tuning Neural Network Models for Improved Prediction of Boundary Layer Transition

Boundary layer transition can strongly impact flight vehicle performance as it influences surface skin friction and aerodynamic heating, making accurate transition prediction a key to designing next generation aircraft. Artificial neural networks (ANNs) have shown promise toward predicting laminar-turbulent transition based on linear stability correlations. The computational efficiency of ANNs and the substantially reduced user involvement in relation to direct computations based on the linear stability theory (LST) makes them an attractive methodology for integrating the LST based correlations in computational fluid dynamics codes. Tollmien-Schlichting (TS) waves correspond to the dominant transition mechanism in 2D or weakly 3D subsonic boundary layers, such as those encountered in general aviation applications. Improvements to neural network model accuracy in predicting the amplification rates of TS instability waves have been investigated by leveraging recent machine learning developments in conjunction with surrogate optimization techniques and via suitable augmentation of the data used to train the networks. The optimized models trained on the modified dataset reduced the average transition location errors on different airfoils at several flow conditions by 51% of the original manually-tuned network’s errors on the same flow cases. The actual transition locations were derived from the Langley Stability and Transition Analysis Code (LASTRAC).

Machine Learning↗
NASA NTRS2009

Nitric Oxide and Oxygen Air-Contamination Effects on Extinction Limits of Non-Premixed Hydrocarbon-Air Flames for a HIFiRE Scramjet

Unique nitric oxide (NO) and oxygen air-contamination effects on the extinction Flame Strength (FS) of non-premixed hydrocarbon (HC) vs. air flames are characterized for 7 gaseous HCs, using a new idealized 9.3 mm straight-tube Opposed Jet Burner (OJB) at 1 atm. FS represents a laminar strain-induced extinction limit based on cross-section-average air jet velocity, Uair, that sustains combustion of a counter jet of gaseous fuel just before extinction. Besides ethane, propane, butane, and propylene, the HCs include ethylene, methane, and a 64 mole-% ethylene / 36 % methane mixture, the writer s previously recommended gaseous surrogate fuel for HIFiRE scramjet tests. The HC vs. clean air part of the work is an extension of a May 2008 JANNAF paper that characterized surrogates for the HIFiRE project that should mimic the flameholding of reformed (thermally- or catalytically-cracked) endothermic JP-like fuels. The new FS data for 7 HCs vs. clean air are thus consolidated with the previously validated data, normalized to absolute (local) axial-input strain rates, and co-plotted on a dual kinetically dominated reactivity scale. Excellent agreement with the prior data is obtained for all 7 fuels. Detailed comparisons are also made with recently published (Univ. Va) numerical results for ethylene extinction. A 2009-revised ethylene kinetic model (Univ. Southern Cal) led to predicted limits within approx. 5 % (compared to 45 %, earlier) of this writer s 2008 (and present) ethylene FSs, and also with recent independent data (Univ. Va) obtained on a new OJB system. These +/- 5 % agreements, and a hoped-for "near-identically-performing" reduced kinetics model, would greatly enhance the capability for accurate numerical simulations of surrogate HC flameholding in scramjets. The measured air-contamination effects on normalized FS extinction limits are projected to assess ongoing Arc-Heater-induced "facility test effects" of NO production (e.g., 3 mole-%) and resultant oxygen depletion (from 21 to 19.5 %), for testing the "64/36" surrogate fuel in Langley s Arc-Heated Scramjet Test Facility for HIFiRE engine designs. The FS results show a generally small (< 4 %) "nitric oxide enhancement" effect, relative to clean air, for up to 3 % NO (freestream Mach number up to 7 in Arc Jet testing). However, a progressively large "oxygendeficiency weakening" effect develops. For 3 % NO, a net weakening of 26 % in FS is derived for the "64/36" fuel vs. air. The corresponding net weakening for pure ethylene is 20 %. A number of practical recommendations regarding facility test effects are offered.

Pellett, Gerald L.↗
NASA NTRS2021

Transmission Telescope Optical Dynamic Alignment

The Integrated Radio Optical Communication System (iROC) is designed to transmit data between Mars and Earth by means of radio waves at 32.67 GHz (Ka Band) and laser beam at 1550 nm, both transmitted via a combined teletenna. The iROC terminal will provide ‘beaconless’ operations to allow full function from the outer planets. In order to point without the aid of an uplink beacon, the proof of concept presented here is addressing the need for an accurate determination and control of the relative position of the laser beam with respect to a reference star. The experiment presented simulates a surrogate transmission telescope system in laboratory setting and presents the model used in the correction of the outgoing beam. The results of the model show a nonlinear dependence between the outgoing and the reference beam, indicating the necessity of a minimum of two metrology instruments placed along the optical system for increased pointing precision.

optical communication↗
NASA NTRS2005

Surrogate-based Analysis and Optimization

A major challenge to the successful full-scale development of modem aerospace systems is to address competing objectives such as improved performance, reduced costs, and enhanced safety. Accurate, high-fidelity models are typically time consuming and computationally expensive. Furthermore, informed decisions should be made with an understanding of the impact (global sensitivity) of the design variables on the different objectives. In this context, the so-called surrogate-based approach for analysis and optimization can play a very valuable role. The surrogates are constructed using data drawn from high-fidelity models, and provide fast approximations of the objectives and constraints at new design points, thereby making sensitivity and optimization studies feasible. This paper provides a comprehensive discussion of the fundamental issues that arise in surrogate-based analysis and optimization (SBAO), highlighting concepts, methods, techniques, as well as practical implications. The issues addressed include the selection of the loss function and regularization criteria for constructing the surrogates, design of experiments, surrogate selection and construction, sensitivity analysis, convergence, and optimization. The multi-objective optimal design of a liquid rocket injector is presented to highlight the state of the art and to help guide future efforts.

Queipo, Nestor V.↗
NASA NTRSDate not supplied

Fast Machine Learning Lidar Surrogate Simulator: Pristine Clear Sky

The simulations of lidar signals and retrievals rely on a range of optic-physical models, such as radiative transfer models, particle scattering and absorption models, along with the output data from atmospheric physical models. Integrating these different models to represent signals of a lidar system is computationally expensive, and performing backward retrievals can be complex and ambiguous. However, with the advantages of Machine Learning, there is a new potential for building effective lidar signal database linked to corresponding atmospheric profiles. For this project, we are developing a fast pre-trained neural network as the lidar surrogate simulator using simulated data for a CALIPSO-like lidar (355 nm, 532nm, and 1064nm), and a CO2 differential absorption lidar (DIAL) near 1571nm. Specifically, we utilize a long short-term memory (LSTM) model to map the relationships between atmospheric profiles (pressure, temperature, air density and CO2 mixing ratio) and lidar signals. This approach allows us to build machine learning based simulators that can reconstruct lidar signals at specific bands from MERRA reanalysis data, and perform retrievals of atmospheric profiles using lidar signals at various wavelengths. As a first step, the results show the potential of this method to establish a foundational model for sensor signals. This model offers the promise of enabling both accurate predictions and rapid retrievals, providing a more efficient approach to signal processing and analysis.

Shan Zeng↗
NASA NTRS2014

NASA Numerical and Experimental Evaluation of UTRC Low Emissions Injector

Computational and experimental analyses of a PICS-Pilot-In-Can-Swirler technology injector, developed by United Technologies Research Center (UTRC) are presented. NASA has defined technology targets for near term (called "N+1", circa 2015), midterm ("N+2", circa 2020) and far term ("N+3", circa 2030) that specify realistic emissions and fuel efficiency goals for commercial aircraft. This injector has potential for application in an engine to meet the Pratt & Whitney N+3 supersonic cycle goals, or the subsonic N+2 engine cycle goals. Experimental methods were employed to investigate supersonic cruise points as well as select points of the subsonic cycle engine; cruise, approach, and idle with a slightly elevated inlet pressure. Experiments at NASA employed gas analysis and a suite of laser-based measurement techniques to characterize the combustor flow downstream from the PICS dump plane. Optical diagnostics employed for this work included Planar Laser-Induced Fluorescence of fuel for injector spray pattern and Spontaneous Raman Spectroscopy for relative species concentration of fuel and CO2. The work reported here used unheated (liquid) Jet-A fuel for all fuel circuits and cycle conditions. The initial tests performed by UTRC used vaporized Jet-A to simulate the expected supersonic cruise condition, which anticipated using fuel as a heat sink. Using the National Combustion Code a PICS-based combustor was modeled with liquid fuel at the supersonic cruise condition. All CFD models used a cubic non-linear k-epsilon turbulence wall functions model, and a semi-detailed Jet-A kinetic mechanism based on a surrogate fuel mixture. Two initial spray droplet size distribution and spray cone conditions were used: (1) an initial condition (Lefebvre) with an assumed Rosin-Rammler distribution, and 7 degree Solid Spray Cone; and (2) the Boundary Layer Stripping (BLS) primary atomization model giving the spray size distribution and directional properties. Contour and line plots are shown in comparison with experimental data (where this data is available) for flow velocities, fuel, and temperature distribution. The CFD results are consistent with experimental observations for fuel distribution and vaporization. Analysis of gas sample results, using a previously-developed NASA NOx correlation, indicates that for sea-level takeoff, the PICS configuration is predicted to deliver an EINOx value of about three for the targeted supersonic aircraft. Emissions results at supersonic cruise conditions show potential for meeting the NASA goals with liquid fuel.

Hicks, Yolanda R.↗
NASA NTRS2014

NASA Numerical and Experimental Evaluation of UTRC Low Emissions Injector

Computational and experimental analyses of a PICS-Pilot-In-Can-Swirler technology injector, developed by United Technologies Research Center (UTRC) are presented. NASA has defined technology targets for near term (called "N+1", circa 2015), midterm ("N+2", circa 2020) and far term ("N+3", circa 2030) that specify realistic emissions and fuel efficiency goals for commercial aircraft. This injector has potential for application in an engine to meet the Pratt & Whitney N+3 supersonic cycle goals, or the subsonic N+2 engine cycle goals. Experimental methods were employed to investigate supersonic cruise points as well as select points of the subsonic cycle engine; cruise, approach, and idle with a slightly elevated inlet pressure. Experiments at NASA employed gas analysis and a suite of laser-based measurement techniques to characterize the combustor flow downstream from the PICS dump plane. Optical diagnostics employed for this work included Planar Laser-Induced Fluorescence of fuel for injector spray pattern and Spontaneous Raman Spectroscopy for relative species concentration of fuel and CO2. The work reported here used unheated (liquid) Jet-A fuel for all fuel circuits and cycle conditions. The initial tests performed by UTRC used vaporized Jet-A to simulate the expected supersonic cruise condition, which anticipated using fuel as a heat sink. Using the National Combustion Code a PICS-based combustor was modeled with liquid fuel at the supersonic cruise condition. All CFD models used a cubic non-linear k-epsilon turbulence wall functions model, and a semi-detailed Jet-A kinetic mechanism based on a surrogate fuel mixture. Two initial spray droplet size distribution and spray cone conditions were used: 1) an initial condition (Lefebvre) with an assumed Rosin-Rammler distribution, and 7 degree Solid Spray Cone; and 2) the Boundary Layer Stripping (BLS) primary atomization model giving the spray size distribution and directional properties. Contour and line plots are shown in comparison with experimental data (where this data is available) for flow velocities, fuel, and temperature distribution. The CFD results are consistent with experimental observations for fuel distribution and vaporization. Analysis of gas sample results, using a previously-developed NASA NOx correlation, indicates that for sea-level takeoff, the PICS configuration is predicted to deliver an EINOx value of about 3 for the targeted supersonic aircraft. Emissions results at supersonic cruise conditions show potential for meeting the NASA goals with liquid fuel.

Hicks, Yolanda R.↗
NASA NTRS2006

Beam Heating of Samples: Modeling and Verification

Energy absorbed from the X-ray beam by the sample requires cooling by forced convection (i.e. cryostream) to minimize temperature increase and the damage caused to the sample by the X-ray heating. In this presentation we will first review the current theoretical models and recent studies in the literature, which predict the sample temperature rise for a given set of beam parameters. It should be noted that a common weakness of these previous studies is that none of them provide actual experimental confirmation. This situation is now remedied in our investigation where the problem of x-ray sample heating is taken up once more. We have theoretically investigated, and at the same time, in addition to the numerical computations, performed experiments to validate the predictions. We have modeled, analyzed and experimentally tested the temperature rise of a 1 mm diameter glass sphere (sample surrogate) exposed to an intense synchrotron X-ray beam, while it is being cooled in a uniform flow of nitrogen gas. The heat transfer, including external convection and internal heat conduction was theoretically modeled using CFD to predict the temperature variation in the sphere during cooling and while it was subjected to an undulator (ID sector 19) X-ray beam at the APS. The surface temperature of the sphere during the X-ray beam heating was measured using the infrared camera measurement technique described in a previous talk. The temperatures from the numerical predictions and experimental measurements are compared and discussed. Additional results are reported for the two different sphere sizes and for two different supporting pin orientations.

Kazmierczak, Michael↗
NASA NTRS1999

Measuring Directional Wave Spectra and Wind Speed with a Scanning Radar Altimeter

The geometry for the NASA Scanning Radar Altimeter (SRA) is shown. It transmits a 8-ns duration pulse at Ka-band (8.3 mm) and measures time of flight as it scans a 1 degree (two-way) beam from left to right across the aircraft ground track. The most recent configuration determines the surface elevation at 64 points spaced at uniform angular intervals of about 0.7 across a swath whose width is about 0.8 times the aircraft altitude. The system generates these raster lines of the surface topography beneath the aircraft at about a 10 Hz rate. In postflight processing the SRA wave topographic data are transformed with a two-dimensional Fast Fourier Transformation (FFT) and Doppler corrected to produce directional wave spectra. The SRA is not absolutely calibrated in power, but by measuring the relative fall-off of backscatter with increasing incidence angle, the SRA can also determine the mean square slope (mss) of the sea surface, a surrogate for wind speed. For the slope-dependent specular point model of radar sea surface scattering, an expression approximated by a geometric optics form, for the relative variation with incidence angle of the normalized backscatter radar cross section would be sigma (sup 0) (sub rel) = sec (exp 4) theta exp (-tan squared theta/mss) where theta is the off-nadir incidence angle.

Walsh, E. J.↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
NASA NTRS1983

A mathematical model for crop spectral-temporal trajectories based on a plant growth model

The Kubelka-Munk radiative transfer model is combined with an approximation of Kauth-Thomas greeness and brightness transforms to derive approximate closed form expressions for crop greeness and brightness surrogates in terms of canopy biomass. The greeness relation derived resembles an existing empirical relation between leaf area index and greeness. A simple growth model based on interception and utilization of photosynthetically active radiation is developed and used to describe the time evolution of greeness and brightness. The model developed does not yet yield definitive profile calculations but suggests a conceptual framework which may be found useful for further profile analysis.

Woolford, T. L.↗