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At least 793 records · Page 44

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DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗

DOE OSTI2021

Assessing Self-Definition and Relatedness in Level of Personality Functioning

The two polarities model (TPM) of personality organizes psychological assessment and psychotherapy and connects to personality disorder diagnosis using the DSM-5 Alternative Model for Personality Disorders (AMPD). The authors developed scales assessing the TPM from an existing self-report measure for level of personality functioning (LPF), a core component of the AMPD. Iterative content analyses of the LPF measure yielded scales for Autonomy and Communion corresponding to dimensions of the TPM. The scales were refined via internal consistency analyses using a measure of psychological attachment and studied in development and validation samples. Associations with relevant external criteria were explored in a series of multiple regressions. Furthermore, the new content-based LPF scales were illustrated with a case vignette. Although the new Autonomy/Communion scales await further validation prior to clinical use, initial evidence suggests that they may bridge the nomological nets of the TPM and AMPD and potentially offer clinical utility in assessment and treatment planning.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

Sampling Functions from Gaussian Processes and Structured Covariance Gaussian Networks

When learning aerodynamic models from data, it is critical to incorporate estimates of model uncertainty. This motivates the design of probabilistic aerodynamic databases which can be sampled to generate physically and statistically plausible aerodynamic models. In this talk we discuss how to sample deterministic functions from two different kinds of probabilistic models and demonstrate their use. First, Gaussian Process Regressors (GPRs) are a widely used probabilistic kernel-based model which can be thought of as Gaussian distributions over functions. GPRs are generally trained by maximizing the marginal likelihood of seeing the training data over the kernel parameter space. Sample functions are easily generated by drawing points from the Gaussian distribution at desired input points. However, when the points are not known ahead of time, the classical sampling approach is not possible since successive function samples will generate different function realizations. We present an approach for sampling consistent function evaluations from a GPR over multiple samples. Second, we describe a neural network architecture which learns a conditional Gaussian distribution by maximizing the marginal likelihood at each point in the input space. We then discuss and compare several options for generating sample functions which match this distribution. Finally, we demonstrate the use of these probabilistic aerodynamic models in an atmospheric reentry simulation.

Gaussian process regression↗

DOE OSTI2023

Response of Ecosystem Productivity to High Vapor Pressure Deficit and Low Soil Moisture: Lessons Learned From the Global Eddy-Covariance Observations

Although there is mounting concern about how high vapor pressure deficit (VPD) and low soil moisture (SM) affect ecosystem productivity, their relative importance is still under debate. Here, we comprehensively quantified the relative impacts of these two factors on ecosystem gross primary production (GPP) using observations from a global network of eddy-covariance towers and two approaches (sensitivity analysis and linear regression model). Both approaches agree that a higher percentage of sites experience GPP reduction from high VPD than from low SM over the growing season. However, the constraint of high VPD and low SM on GPP reduction is tightly linked with climates and plant functional types. Humid and mesic ecosystems including forests and grasslands are dominated by VPD, while the semi-arid and arid ecosystems including shrublands and savannas are dominated by SM. The varying dominant role of these two factors on GPP is closely related to plant stomatal behavior, as predicted by a stomatal conductance model. Additionally, we highlight the non-linear impact of SM on GPP during droughts and the possible underestimation of the SM effects for deep-rooted plants when only using surface-layer SM. Our results shed light on a better understanding of the impacts of VPD and SM on vegetation productivity, with important implications for modeling the response and feedback of ecosystem dynamics to current and future climates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Aemulus ν: precision halo mass functions in wνCDM cosmologies

Precise and accurate predictions of the halo mass function for cluster mass scales in wνCDM cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys. Here, we present a halo mass function emulator for cluster mass scales (≳ 1013 M ⊙/h) up to redshift z = 2 with comprehensive support for the parameter space of wνCDM cosmologies allowed by current data. Based on the Aemulus ν suite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models. This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies. We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies. Our emulator is publicly available (https://github.com/DelonShen/aemulusnu_hmf), providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid.

cluster counts↗

NASA NTRS1975

Remotely sensing wheat maturation with radar

The scattering properties of wheat were studied in the 8-18 GHz band as a function of frequency, polarization, incidence angle, and crop maturity. Supporting ground truth was collected at the time of measurement. The data indicate that the radar backscattering coefficient is sensitive to both radar system parameters and crop characteristics particularly at incidence angles near nadir. Linear regression analyses of the radar backscattering coefficient on both time and plant moisture content result in rather good correlation. Furthermore, by calculating the average time rate of change of the radar backscattering coefficient it is found that it undergoes rapid variations shortly before and after the wheat is harvested. Both of these analyses suggest methods for estimating wheat maturity and for monitoring the progress of harvest.

Bush, T. F.↗

NASA NTRS1976

On estimating gravity anomalies: A comparison of least squares collocation with least squares techniques

The least squares collocation algorithm for estimating gravity anomalies from geodetic data is shown to be an application of the well known regression equations which provide the mean and covariance of a random vector (gravity anomalies) given a realization of a correlated random vector (geodetic data). It is also shown that the collocation solution for gravity anomalies is equivalent to the conventional least-squares-Stokes' function solution when the conventional solution utilizes properly weighted zero a priori estimates. The mathematical and physical assumptions underlying the least squares collocation estimator are described, and its numerical properties are compared with the numerical properties of the conventional least squares estimator.

Argentiero, P.↗

NASA NTRS1977

On estimating gravity anomalies - A comparison of least squares collocation with conventional least squares techniques

Argentiero, P.↗

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↗

DOE OSTI2015

Consideration for the application of artificial neural networks to criticality code validation - 14270

The artificial neural network (ANN) is a consistent example for non-parametric parameter estimation and regression, with powerful universal approximation properties, that is distribution independent. ANN application to criticality code validation centers on aligning mathematical relationships between code validation parameters and neural network prediction for those parameter values. Two specific goals are: (1) an appropriate ANN architecture to determine validation parameter values, and (2) bootstrapping individual ANNs as a 'Prediction Interval' (PI) technique to estimate the Calculation Margin and Upper Subcritical Limit. The individual ANN workflow is segregated into Training, Validation (network), and Testing Sets, where the network predictive properties and performance are expressed in the latter set. The Bootstrap ANN replicates a large set of individual ANNs (taken from the original data distribution) where (1-α/2)100% PI estimates can be determined. Results from the ANN method are compared to a variety of validation examples: a 'toy example' from ANSI/ANS-8.24, Appendix D, a variety of methods noted in a Nuclear Criticality Safety Division 2009 paper, and several validation campaigns for highly enriched uranium and plutonium systems. The ANN method is robust and can be applied to single or multivariate inputs. A trained ANN can provide both an average estimate (e.g., single value) and an estimate over a range of independent variable values where the kUSL can be formed in a regression relationship suitable for extrapolation. The input dependence has been tested, ranging from 15 to over 400 sample size distributions. An expanded Shapiro-Wilk test (for sample sizes up to 5000) was implemented to confirm that the ANN method is transparent to distribution function. The Principle Component Analysis method was applied as an input preprocessing step embedded within the ANN in order to reduce dimensionality and correlation for multivariable inputs. This can potentially aid in determining accurate margins over applying arbitrary margins. The automatic optimization of the Bootstrap ANN PI is left for future consideration. (authors)

42 ENGINEERING↗

DOE OSTI2018

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ), which is not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for online estimation and control of MIQ indices. First, to completely capture the nonlinear dynamics of the BF process, the nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multi-output problem, the multi- task transfer learning is proposed to design a novel multi-output LS-SVR (M-LS-SVR) for learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS- SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can be properly distinguished, thus a robust modeling result can be achieved. Finally, a novel multi- objective evaluation index on modeling performance is developed by comprehensively considering the root mean square error of modeling and the correlation coefficient on trend fitting, based on which the NSGA-II algorithm is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently, which indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

blast cavities, Control↗

DOE OSTI2014

Rare key functional domain missense substitutions in MRE11A, RAD50 , and NBN contribute to breast cancer susceptibility: results from a Breast Cancer Family Registry case-control mutation-screening study

The MRE11A-RAD50-Nibrin (MRN) complex plays several critical roles related to repair of DNA double-strand breaks. Inherited mutations in the three components predispose to genetic instability disorders and the MRN genes have been implicated in breast cancer susceptibility, but the underlying data are not entirely convincing. Here, we address two related questions: (1) are some rare MRN variants intermediate-risk breast cancer susceptibility alleles, and if so (2) do the MRN genes follow a BRCA1/BRCA2 pattern wherein most susceptibility alleles are protein-truncating variants, or do they follow an ATM/CHEK2 pattern wherein half or more of the susceptibility alleles are missense substitutions? Using high-resolution melt curve analysis followed by Sanger sequencing, we mutation screened the coding exons and proximal splice junction regions of the MRN genes in 1,313 early-onset breast cancer cases and 1,123 population controls. Rare variants in the three genes were pooled using bioinformatics methods similar to those previously applied to ATM, BRCA1, BRCA2, and CHEK2, and then assessed by logistic regression. Results: Re-analysis of our ATM, BRCA1, and BRCA2 mutation screening data revealed that these genes do not harbor pathogenic alleles (other than modest-risk SNPs) with minor allele frequencies >0.1% in Caucasian Americans, African Americans, or East Asians. Limiting our MRN analyses to variants with allele frequencies of <0.1% and combining protein-truncating variants, likely spliceogenic variants, and key functional domain rare missense substitutions, we found significant evidence that the MRN genes are indeed intermediate-risk breast cancer susceptibility genes (odds ratio (OR) = 2.88, P = 0.0090). Key domain missense substitutions were more frequent than the truncating variants (24 versus 12 observations) and conferred a slightly higher OR (3.07 versus 2.61) with a lower P value (0.029 versus 0.14). These data establish that MRE11A, RAD50, and NBN are intermediate-risk breast cancer susceptibility genes. Like ATM and CHEK2, their spectrum of pathogenic variants includes a relatively high proportion of missense substitutions. However, the data neither establish whether variants in each of the three genes are best evaluated under the same analysis model nor achieve clinically actionable classification of individual variants observed in this study.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Enhancing the multi-functional properties of renewable lignin carbon fibers via defining the structure–property relationship using different biomass feedstocks

Lignin has been explored extensively as a renewable precursor for carbon materials, considering its abundance as a major component of plant cell walls and its sustainability as a byproduct of both lignocellulosic biorefinery and the paper-making industry. Despite the extensive efforts to define the process–property relationship, it remains largely unknown how lignin biosynthesis and its chemistry would impact the resultant carbon fiber properties, for both mechanical and electroconductive performances. Such inadequate understanding fundamentally limits feedstock design and selection to improve carbon fiber properties toward broader commercial applications. Using lignin from a broad range of biomass feedstocks for carbon fiber manufacturing, we have fundamentally explored the structure–function relationship between lignin chemistry and carbon fiber performance. Specifically, lignin extracted from hardwood (sugar maple), softwood (loblolly pine and red cedar), and herbaceous plants (corn stover and switchgrass) was used for carbon fiber manufacturing, considering the very different lignin structures from these feedstocks. Linear regression models were established to define the relationship between carbon fiber mechanical properties and lignin structural characteristics. The results highlighted that the content of β-O-4 linkages correlates significantly with the tensile strength and elastic modulus of lignin carbon fibers, indicating that more linear β-O-4 linkages would promote the carbon fiber mechanical performance. Moreover, electroconductive properties are essential for broader energy device application of lignin-based carbon fibers, yet the mechanisms controlling their electroconductivity are largely unknown. Furthermore, we hereby demonstrated that a higher β-O-4 content also promotes the electroconductivity of lignin carbon fibers. Microstructure analysis further revealed that the crystallite size and content of the pre-graphitic turbostratic carbon structure in lignin-based carbon fibers were enhanced as the β-O-4 linkages increased. The content of β-O-4 linkages has shown a strong correlation with the crystallite content in a linear regression model. This study thus revealed the underlying mechanisms regarding how the lignin structure in planta defines the resultant carbon fiber properties. Moreover, the study also highlighted the correlation between the mechanical and electroconductive properties of lignin-based carbon fibers, both of which were defined by the lignin structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1983

Fitting of satellite and in-situ ocean surface temperatures Results for polymode during the winter of 1977-1978

For the period considered, December 1977 through February 1978, bivariate Gaussian discriminant function cloud identification revealed that more than 93 percent of the 8-km resolution GOES infrared pixels were cloud contaminated. Cloud-free in-situ calibration points were distributed in nonrandom groups; this resulted in systematic errors when using least squares techniques. Surfaces and regression lines were least squares fitted between satellite and in-situ data; use was also made of differences and ratios. The best results were achieved with a regression in the form of the infrared radiative transfer equation; but this was no better than + or - 0.9 K. Because of extensive cloudiness, the linear regressions were seldom useful, and temperature ratios with + or - 1.3 K experimental errors best represent the applicability of GEOS data to sea surface temperatures.

Maul, G. A.↗

NASA NTRS1998

Molecular Static Third-Order Polarizabilities of Carbon-Cage Fullerene and Their Correlation with Three Geometric Properties: Symmetry, Aromaticity, and Size

The static third-order polarizabilities (gamma) of C60, C70, five isomers of C78 and two isomers of C84 were analyzed in terms of three properties, from a geometric point of view: symmetry, aromaticity and size. The polarizability values were based on the finite field approximation using a semiempirical Hamiltonian (AM1) and applied to molecular structures obtained from density functional theory calculations. Symmetry was characterized by the molecular group order. The selection of 6-member rings as aromatic was determined from an analysis of bond lengths. Maximum interatomic distance and surface area were the parameters considered with respect to size. Based on triple linear regression analysis, it was found that the static linear polarizability (alpha) and gamma in these molecules respond differently to geometrical properties: alpha depends almost exclusively on surface area while gamma is affected by a combination of number of aromatic rings, length and group order, in decreasing importance. In the case of alpha, valence electron contributions provide the same information as all-electron estimates. For gamma, the best correlation coefficients are obtained when all-electron estimates are used and when the dependent parameter is ln(gamma) instead of gamma.

Moore, C. E.↗

NASA NTRS2005

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rai, Man Mohan↗

NASA NTRS2007

Hybrid Neural Network and Support Vector Machine Method for Optimization

Rai, Man Mohan↗