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

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Experiments, Statistical Analysis, and Modeling to Evaluate the Porosity Influence in SPS Coatings

Suspension plasma spray (SPS) is far more complicated than conventional plasma spray and requires a deep knowledge about the influence of process parameters and their correlations. In this study, YSZ coatings were manufactured by SPS with six different process parameters such as plasma power, suspension mass load, original powder size, substrate surface topology, spray distance, and spray step. Then, the porosity of as-prepared coatings was investigated by image method and X-ray transmission technique. A multivariate analysis on the collected experimental data was carried out by employing mathematical statistics methods. The results showed that: 1) Coating porosity has a negative correlation with plasma power and suspension mass load and a positive correlation with the original powder size, spray distance, spray step, and substrate roughness; 2) Spraying distance is the main factor affecting to coating porosity, followed by suspension mass load and substrate surface roughness, respectively. A linear model for porosity prediction was developed and was verified by experiments. Furthermore, the mechanism by which process parameters influence coating porosity is additionally discussed.

36 MATERIALS SCIENCE↗

Multivariate Analogs of Classical Univariate Discrete Distributions and Their Properties

Some discrete distributions such as Bernoulli, binomial, geometric, negative binomial, Poisson, Polya–Aeppli, and others play an important role in applied problems of probability theory and mathematical statistics. We propose a variant of a multivariate distribution whose components have a given univariate discrete distribution. In fact we consider some very general variant of the so-called reduction method. We find the explicit form of the mass function and generating function of such distribution and study their properties. We prove that our construction is unique in natural exponential families of distributions. Our results are the generalization and unification of many results of other authors.

97 MATHEMATICS AND COMPUTING↗

DOE Advanced Scientific Advisory Committee (ASCAC): Workforce Subcommittee Letter

Simulation and computing are essential to much of the research conducted at the DOE national laboratories. Experts in the ASCR ¬relevant Computing Sciences, which encompass a range of disciplines including Computer Science, Applied Mathematics, Statistics and domain Computational Sciences, are an essential element of the workforce in nearly all of the DOE national laboratories. This report seeks to identify the gaps and challenges facing DOE with respect to this workforce. This letter is ASCAC’s response to the charge of February 19, 2014 to identify disciplines in which significantly greater emphasis in workforce training at the graduate or postdoctoral levels is necessary to address workforce gaps in current and future Office of Science mission needs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty [Brochure]

The four priority research directions outlined in this brochure represent a cohesive vision for advancing the science of inverse problems for complex systems under uncertainty. Together, they address the critical challenges of: discovering, exploiting, and preserving physical and problem structure; overcoming model limitations; integrating disparate, multimodal, and/or dynamic data; and tailoring the solution of inverse problems to downstream tasks. While each PRD focuses on a distinct aspect of inverse-problem research, their interconnected nature highlights the importance of a holistic approach that leverages progress across all areas to achieve transformative solutions. This agenda calls for research across mathematics, statistics, and computer science disciplines, which are guided and complemented by rapid advances in artificial intelligence, high-performance computing, and experimental facilities, to unlock new capabilities, maximize scientific impact, and meet the growing demands of inverse problems that arise across applications that are critical to DOE's mission.

97 MATHEMATICS AND COMPUTING↗

Investigations of Beam Dynamics Issues at Current and Future Hadron Accelerators (Final Report)

There is a synergy between the fields of Beam Dynamics (BD) in modern particle accelerators and Applied Mathematics (AMa). We have formulated significant problems in BD and have developed and applied tools within the contexts of dynamical systems, topological methods, numerical analysis and scientific computing, probability and stochastic processes, and mathematical statistics. We summarize the three main areas of our AMa work since 2011. First, we continued our study of Vlasov-Maxwell systems. Previously, we developed a state of the art algorithm and code (VM3@A) to calculate coherent synchrotron radiation in single pass systems. In this cycle we carefully analyzed the major expense, namely the integral-over-history (IOH), and developed two approaches to speed up integration. The first strategy uses a representation of the Bessel function J0 in terms of exponentials. The second relies on “local sequences” developed recently for radiation boundary conditions, which are used to reduce computational domains. Although motivated by practicality, both strategies involve interesting and rather deep analysis and approximation theory. As an alternative to VM3@A, we are integrating Maxwell’s equations by a time-stepping method, bypass- ing the IOH, using a Discontinuous Galerkin (DG) method. DG is a generalization of Finite Element and Finite Volume methods. It is spectrally convergent, unlike the commonly used Finite Difference methods, and can handle complicated vacuum chamber geometries. We have applied this in several contexts and have obtained very nice results including an explanation of an experiment at the Canadian Light Source, where the geometry is quite complex. Second, we continued our study of spin dynamics in storage rings. There is much current and proposed activity where spin polarized beams are being used in testing the Standard Model and its modifications. Our work has focused on invariant spin fields (ISFs) and amplitude dependent spin tunes (ADSTs), which are essential for estimating beam polarization. Several algorithms have been developed since the 1980s for computing the ISF, among them the Heinemann- Hoffstaetter method of stroboscopic averaging (SA) which is implemented in the code SPRINT. SA, which computes the ISF by using spin tracking data, can find the ISF to computer precision if it exists and thus can give evidence for existence of the ISF. Central to our work is resolving the ISF conjecture, which says that, off orbital resonance, an ISF exists. Thus Heinemann developed, in his 2010 PhD thesis, a new framework which unifies and generalizes the notions of ISF and ADST by using bundle theory. This lead to a long paper which was a major collaborative effort during the recent cycle. In a nutshell, our bundle approach elegantly unifies the dynamics of spin-1/2 and spin-1 particles, e.g., protons and deuterons. In fact it is well known that these two kinds of dynamics are driven by the same spin transfer matrix and in our approach one sees the deeper reason for this: the spin transfer matrix carries the natural dynamics of a principal bundle whereas the difference between the spin-1/2 and spin-1 dynamics lies in their different geometrical situation, i.e., different underlying associated bundles. Thus one arrives at new results for polarized beams, among them the Invariant Reduction Theorem (IRT) and the Cross Section Theorem (CST). The IRT gives a necessary and sufficient condition for the ISF to exist. The SA technique revealed, 20 years ago, that each ISF can be viewed as a complex agglomerate of spin tracking data. The bundle approach goes one step further by using the IRT and the CST to glue spin tracking data into agglomerates which are candidates for ISFs. We gain insight because the “good” agglomerates, in the presence of an ISF, are very different from the “bad” ones. Finally we mention that the bundle approach has analogies to the approach used in geometrical Yang-Mills theory. Third, we studied X-Ray Free Electron Lasers (FELs), which are electron accelerators producing coherent undulator radiation over a wide range of frequencies from microwaves to x-rays. The photon beams produced in FEL undulators are used to study material samples in biology, material science etc. We developed a mathematical analysis, based on the 6D Lorentz system, of energetic electrons moving through a planar undulator excited by a Maxwell traveling wave field of wavelength λ. Our Method of Averaging perturbation analysis yields non-resonant and near-to-resonant normal form approximations as a function of λ, which we present in two averaging theorems. We prove the theorems in detail, error bounds and giving a tutorial on mathematically rigorous perturbation theory in a context where proofs are easily understood. To our knowledge the planar problem has not been analyzed with the generality here nor has the standard FEL pendulum system, which appears on resonance, been derived with error bounds. In addition to the domains of validity of the normal forms we obtain new insights that require further study, including a more general low gain theory. With a firm foundation in the non-collective case above we are studying the 3D collective case from start up from noise to high gain and saturation. We have formulated the noise start up as a problem of going from the microscopic Klimontovich-Maxwell to the macroscopic Vlasov-Maxwell with a Vlasov correction term. In the 1D setting, we seek an alternative to the phenomenological Vlasov approach which models shot noise by a perturbation on an initial “smooth” density. The 1D wave equation with a Klimontovich source is often the starting point for the 1D FEL high gain theory. We have a new representation of solutions which may lead to new insights.

43 PARTICLE ACCELERATORS↗

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learn- ing to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

machine learning, multiscale modeling, physics-bas↗

High voltage stability performance of a gamma ray detection device

An industrial grade digital radiation survey meter device is currently being developed at Malaysian Nuclear Agency. This device used a cylindrical type Geiger Mueller (GM) which acts as a detector. GM detector operates at relatively high direct current voltages depend on the type of GM tube. This thin/thick walled cylindrical type of GM tube operates at 450-650 volts range. Proper value and stability performance of high voltage are important parameters to ensure that this device give a reliable radiation dose measurement. This paper will present an assessment of the stability and performance of the high voltage supply for radiation detector. The assessment is performed using System Identification tools box in MATLAB and mathematical statistics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Stochastic Parameterization: Toward a New View of Weather and Climate Models

The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined

54 ENVIRONMENTAL SCIENCES↗

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative , sparse , and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Weylandt, Michael↗

Hedberg Research Conference on Fundamental Controls on Flow in Carbonates: Request for Travel Support for Post-Doctoral Fellows

Carbonate reservoirs pose a scientific and engineering challenge to geophysical prediction and monitoring of fluid flow in the subsurface. Difficulties in interpreting hydrological, reservoir and other exploration data arise because carbonates are composed of a hierarchy of geological structures, constituents and processes that span a wide spectrum of length and time scales. What makes this problem particularly challenging is that length scales associated with physical structure and processes are often not discrete, but overlap, preventing the definition of discrete elements at one scale to become the building blocks of the next scale. This is particularly true for carbonates where complicated depositional environments, subsequent post-deposition diagenesis and geochemical interactions result in pores that vary in scale from submicron to centimeters to fractures, variation in fabric composition with fossils, minerals and cement, as well as variations in structural features (e.g., oriented inter- and intra layered - interlaced bedding and/or discontinuous rock units). In addition, this complexity is altered by natural and anthropogenic processes such as changes in stress, fluid content, reactive fluid flow, etc. Thus an accurate geophysical assessment of the flow behavior of carbonate reservoirs requires a fundamental understanding of the interplay of textural and structural features subjected to physical processes that affect and occur on various length and time scales. To address this complexity related to carbonates, a Hedberg conference on “Fundamental Controls on Flow in Carbonates” was held July 8 to 13, 2012, to bring together industry and academic scientists to stimulate innovative ideas that can accelerate research advances related to flow prediction and recovery in carbonate reservoirs. Participants included scientist and engineers from multiple disciplines (such as hydrology, structural geology, geochemistry, reservoir engineering, geophysics, geomechanics, numerical modeling, physical experiments, sedimentology, well-testing, statistics, mathematics, visualization, etc.) who encompass experience as well as the latest advances in these multi-faceted fields. One of the goals was to include early career scientists and engineers (post-doctoral fellows, assistant professors, etc.). With this grant 10 early career scientists and engineers were supported to attend the conference. This reports contains a brief overview of the conference and the list of support participants supported by this grant. Full details of the outcomes of the conference are given in the publication found in the Attachment section of this report.

58 GEOSCIENCES↗

Transdisciplinary Foundations of Geospatial Data Science

Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.

Xie, Yiqun↗

Program for Analysis and Enhancement of Images

Land Analysis System (LAS) is collection of image-analysis computer programs designed to manipulate and analyze multispectral image data. Provides user with functions ingesting various sensor data, radiometric and geometric corrections, image registration, training site selection, supervised and unsupervised classification, Fourier domain filtering, and image enhancement. Sufficiently modular and includes extensive library of subroutines to permit inclusion of new algorithmic programs. Commercial package International Mathematical & Statistical Library (IMSL) required for full implementation of LAS. Written in VAX FORTRAN 77, C, and Macro assembler for DEC VAX operating under VMS 4.0.

Lu, Yun-Chi↗

TOAD Editor

Transferable Output ASCII Data (TOAD) computer program (LAR-13755), implements format designed to facilitate transfer of data across communication networks and dissimilar host computer systems. Any data file conforming to TOAD format standard called TOAD file. TOAD Editor is interactive software tool for manipulating contents of TOAD files. Commonly used to extract filtered subsets of data for visualization of results of computation. Also offers such user-oriented features as on-line help, clear English error messages, startup file, macroinstructions defined by user, command history, user variables, UNDO features, and full complement of mathematical statistical, and conversion functions. Companion program, TOAD Gateway (LAR-14484), converts data files from variety of other file formats to that of TOAD. TOAD Editor written in FORTRAN 77.

Bingle, Bradford D.↗

The analysis of star catalogs. 1: An intercomparison among the FK3, the FK4, and the FK5

Progress in the construction of the fundamental catalogs allows one to ask the following questions: 'Now that we have the FK5 with which to measure by, how good was the FK4? The FK3? How realistic were the error estimates they gave for their equatorial coordinates and their proper motions? Have their residual systematic differences continued to decrease in amplitude?' and so forth. Similar questions could be asked of the GC and the N30, and a companion paper will similarly analyze them. Finally, the identical series of questions could be asked of the AGK2, AGK3, AGK3U sequence. In this paper we develop a general method to objectively investigate, post facto, the true quality of star catalog error estimates. We present a complete mathematical formulation of the questions above and delineate the calculations needed to address them. Our initial numerical applications are restricted to a straightforward examination of the mean errors of the coordinates and proper motions in the FK3 (using the FK4 and the FK5 for comparison) and in the FK4 (using the FK5 for comparison). However, we can simply and consistently interpret the results of these computations -- for the FK catalogs fail to follow the predictions of mathematical statistics -- only if the FK3 and the FK4 contain higher angular frequency systematic errors. These errors, at the mean epochs of place of the older catalogs, are larger than the random errors in the FK5 system. We expect that our new ability to discern these features means that we can also prevent them during compilation. Finally, note that the concepts behind our procedures are completely general and can be applied to any set of compiled data -- astrometric, photometric, geodetic, and so forth.

Bucciarelli, B.↗

The analysis of star catalogs. 2: An intercomparison among the GC, the N30, and the FK5(B1950.0/FK4)

We use the general method of compiled data set analysis developed in our first paper (Bucciarelli et. al. 1994) and extend our analysis of fundamental star catalogs to include the two American ones. The problems found in the FK3/FK4/FK5 sequence are not present in either the GC or the N30. This lends support to the hypothesis advanced in our previous paper that the FK3, and to a lesser extent the FK4 declination system, are distorted by high angular frequency systematics. On the other hand, both the GC and N30 equatorial coordinate and proper motion distributions nicely follow the predictions of mathematical statistics. As with the FK3/FK4/FK5 analysis, the new results are as seen by the FK5 (on the FK4/B1950.0 system).

Taff, L. G.↗