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At least 1,243 records · Page 69

Experiments and Simulations on the Turbulent, Rarefaction Wave Driven Rayleigh–Taylor Instability

Experiments were performed to observe the growth of the turbulent, Rayleigh–Taylor unstable mixing layer generated between air and SF 6 , with an Atwood number of A=(ρ 2 –ρ 1 )/(ρ 2 +ρ 1 ) = 0.64, where ρ 1 and ρ 2 are the densities of air and SF 6 , respectively. A nonconstant acceleration with an average value of 2300g 0 , where g 0 is the acceleration due to gravity, was generated by interaction of the interface between the two gases with a rarefaction wave. Three-dimensional, multimode perturbations were generated on the diffuse interface, with a diffusion layer thickness of δ = 3.6 mm, using a membraneless vertical oscillation technique, and 20 experiments were performed to establish a statistical ensemble. The average perturbation from this ensemble was extracted and used as input for a numerical simulation using the Lawrence Livermore National Laboratory (LLNL) Miranda code. Good qualitative agreement between the experiment and simulation was observed, while quantitative agreement was best at early to intermediate times. Several methods were used to extract the turbulent growth constant α from experiments and simulations while accounting for time varying acceleration. Experimental, average bubble and spike asymptotic self-similar growth rate values range from α = 0.022 to α = 0.032 depending on the method used, and accounting for variable acceleration. Values found from the simulations range from α = 0.024 to α = 0.041. Here, the values of α measured in the experiments are lower than what are typically measured in the literature but are more in line with those found in recent simulations.

42 ENGINEERING↗

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA

Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even when gradient information can be computed efficiently. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this paper, we propose a novel Bayesian stochastic inversion methodology, which is characterized by a tight coupling between the gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). This approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated parameter space. In addition, non-Gaussian posterior distributions are estimated via an efficient LMCMC method on the projected low-dimensional feature space. We will demonstrate this computational framework by integrating and adapting our recent data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to a linear elasticity model.

42 ENGINEERING↗

Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA

Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even with gradient information provided. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this work, we propose a novel Bayesian stochastic inversion methodology, characterized by a tight coupling between a gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). This approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated spatial random field. Moreover, non-Gaussian full posterior probability distribution functions are estimated via an efficient LMCMC method on both the projected low-dimensional feature space and the recovered high-dimensional parameter space. We demonstrate this computational framework by integrating and adapting recent developments such as data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to solve inverse problems in linear elasticity.

97 MATHEMATICS AND COMPUTING↗

Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches

Focal Area(s): 1. Data acquisition enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, and hardware-related efforts involving AI. 2. Insight gleaned from complex measurements using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI Science Challenge and Rationale: Atmospheric processes are stochastic, occur at scales from the micrometer to many kilometers, and are constantly changing over time. Characterizing these interactions and associated environmental conditions using traditional measurement techniques is difficult and can take years to build statistics on atmospheric phenomena that occurs episodically. Developing new innovative approaches to modify sampling strategies in real-time would enable the routine collection of targeted measurements focused on a specific set of science questions.

54 ENVIRONMENTAL SCIENCES↗

Toward Discovering the Structure and Dynamics of the Sun-Interstellar Medium System (Los Alamos LDRD Report)

We have taken major steps toward developing the tools necessary to explore with unprecedented resolution the plasma structure and dynamics of the Sun-interstellar medium (ISM) interaction region, known as the heliosheath. We have applied a rigorous imaging technique known as Generalized Ridge Regression (GRR) to construct statistically robust sky maps of hydrogen energetic neutral atoms (ENAs) emanating from this region and detected by the Los Alamos-led IBEX-Hi ENA imager [1] on NASA’s Interstellar Boundary Explorer (IBEX) mission [2]. Our methods go far beyond the map reconstruction process currently applied by the IBEX Science Operations Center (ISOC), opening up the possibility of new discovery science with the IBEX data set and positioning LANL for a lead science role for the upcoming Interstellar Mapping and Acceleration Probe (IMAP) mission [3] for which LANL is providing two key experiments. We have successfully demonstrated that the new maps are higher resolution and are capable of revealing structures that are not presently resolveable in standard ISOC maps

58 GEOSCIENCES↗

Advanced Laboratory and Field Arrays: Accelerate the development of next-generation marine energy conversion systems (Task 3)

This task successfully improved the representation of extreme conditions in wave resource characterization. The previous state of the art to characterize extreme conditions was a purely statistical representation of buoy data; Generalized Extreme Value (GEV) techniques are used to extrapolate joint histograms of significant wave height Hs and energy period T e for a given return period. The task confirmed that H s and T e alone are a poor representation of the actual hazards in extreme events, during which WECs many experience significant impacts from wave groups and breaking waves (i.e., it is not simply the largest wave that may produce the worst condition for a WEC). This task improved both prediction of extreme conditions with “third generation” spectral models, such as WAVEWATCHIII and WAM, and observational quantification of the wave breaking impacts during extreme conditions.

13 HYDRO ENERGY↗

Validity test for linear error analysis

To determine whether estimation process simulated by linear error analysis will converge, criterion has been developed based on extension of classical observability. Particular application of technique is with groups of batched navigation data where statistics of estimation errors are derived with classical minimum-variance methods.

Diamant, L. S.↗

Data handling and analysis for the 1971 corn blight watch experiment

The overall corn blight watch experiment data flow is described and the organization of the LARS/Purdue data center is discussed. Data analysis techniques are discussed in general and the use of statistical multispectral pattern recognition methods for automatic computer analysis of aircraft scanner data is described. Some of the results obtained are discussed and the implications of the experiment on future data communication requirements for earth resource survey systems is discussed.

Anuta, P. E.↗

The influence of surface waves on water circulation in a mid-Atlantic continental shelf region

The importance of wave-induced currents in different weather conditions and water depths (18.3 m and 36.6 m) is assessed in a mid-Atlantic continental-shelf region. A review of general circulation conditions is conducted. Factors which perturb the general circulation are examined using analytic techniques and limited experimental data. Actual wind and wave statistics for the region are examined. Relative magnitudes of the various currents are compared on a frequency of annual occurrence basis. Results indicated that wave-induced currents are often the same order of magnitude as other currents in the region and become more important at higher wind and wave conditions. Wind-wave and ocean-swell characteristics are among those parameters which must be monitored for the analytical computation of continental-shelf circulation.

Whitlock, C. H.↗

Light beams and radiation patterns in random media

The purpose of this paper is to apply a numerical technique in solving problems involving light beams in a random medium. The technique starts by generating numerically sample media with prescribed statistical properties. Rays are then traced in these sample media, and the ray statistics are compiled. These statistics are utilized to find the mean square displacement and distribution of a beam. Problems on beam broadening and distortion of radiation patterns are considered. Fluctuations in wave amplitude and phase are also investigated. When possible, the numerical results are compared with the analytical results and experimental results.

Youakim, M. Y.↗

The LANDSAT-1 multispectral scanner as a tool in the classification of inland lakes

Relationships between LANDSAT-1 multispectral scanner (MSS) data and the trophic status of a group of lakes in the north-northeastern part of the United States were studied by predicting the magnitudes of two trophic state indicators, estimating lake position on a multivariate trophic scale, and automatically classifying lakes according to their trophic state. Initially, the principal component ordination was employed with 100 lakes. MSS data for some 20 lakes was then extracted from computer-compatible tapes (CCT) using a binary marking technique. The output was in the form of descriptive statistics and photographic concatenations. Color ratios were incorporated into regression models for the prediction of Secchi disc transparency, chlorophyll a, and lake position on the tropic scale. Results indicate that the LANDSAT-1 system, although handicapped by low spectral and spatial resolutions as well as excessive cloud cover, can be used as a supplemental data source in lake survey programs.

Boland, D. H. P.↗

Geological mapping in northwestern Saudi Arabia using LANDSAT multispectral techniques

Various computer enhancement and data extraction systems using LANDSAT data were assessed and used to complement a continuing geologic mapping program. Interactive digital classification techniques using both the maximum-likelihood and thresholding statistical approaches achieve very limited success in areas of highly dissected terrain. Computer enhanced imagery developed by color compositing stretched MSS ratio data was constructed for a test site in northwestern Saudi Arabia. Initial results indicate that several igneous and sedimentary rock-types can be discriminated.

Blodget, H. W.↗

Vector statistics of LANDSAT imagery

A digitized multispectral image, such as LANDSAT data, is composed of numerous four dimensional vectors, which quantitatively describe the ground scene from which the data are acquired. The statistics of unique vectors that occur in LANDSAT imagery are studied to determine if that information can provide some guidance on reducing image processing costs. A second purpose of this report is to investigate how the vector statistics are changed by various types of image processing techniques and determine if that information can be useful in choosing one processing approach over another.

Jayroe, R. R., Jr.↗

Contextual classification of multispectral image data - An unbiased estimator for the context distribution

Recent investigations have demonstrated the effectiveness of a contextual classifier that combines spatial and spectral information employing a general statistical approach. This statistical classification algorithm exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. Indeed, a key input to this algorithm is a statistical characterization of the context: the context distribution. Here a discussion is given of an unbiased estimator of the context distribution which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real Landsat data sets are presented and contrasted with results from noncontextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

Tilton, J. C.↗

Contextual classification of multispectral image data: An unbiased estimator for the context distribution

A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

Tilton, J. C.↗

Estimation of context for statistical classification of multispectral image data

Recent investigations have demonstrated the effectiveness of a contextual classifier that combines spatial and spectral information employing a general statistical approach. This statistical classification algorithm exploits the tendency of certain ground cover classes to occur more frequently in some spatial contexts than in others. Indeed, a key input to this algorithm is a statistical characterization of the context: the context function. An unbiased estimator of the context function is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context-function estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real Landsat data sets are presented and contrasted with results from noncontextual classifications and from contextual classifications utilizing other context-function estimation techniques.

Tilton, J. C.↗

Data and results of a laboratory investigation of microprocessor upset caused by simulated lightning-induced analog transients

A methodology was developed a assess the upset susceptibility/reliability of a computer system onboard an aircraft flying through a lightning environment. Upset error modes in a general purpose microprocessor were studied. The upset tests involved the random input of analog transients which model lightning induced signals onto interface lines of an 8080 based microcomputer from which upset error data was recorded. The program code on the microprocessor during tests is designed to exercise all of the machine cycles and memory addressing techniques implemented in the 8080 central processing unit. A statistical analysis is presented in which possible correlations are established between the probability of upset occurrence and transient signal inputs during specific processing states and operations. A stochastic upset susceptibility model for the 8080 microprocessor is presented. The susceptibility of this microprocessor to upset, once analog transients have entered the system, is determined analytically by calculating the state probabilities of the stochastic model.

Belcastro, C. M.↗