Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

The standard κ-ϵ equations and other turbulence models are evaluated with respect to their applicability in swirling, recirculating flows. The turbulence models are formulated on the basis of two separate viewpoints. The first perspective assumes that an isotropic eddy viscosity and the modified Boussinesq hypothesis adequately describe the stress distributions, and that the source of predictive error is a consequence of the modeled terms in the κ-ϵ equations. Both stabilizing and destabilizing Richardson number corrections are incorporated to investigate this line of reasoning. A second viewpoint proposes that the eddy viscosity approach is inherently inadequate and that a redistribution of the stress magnitudes is necessary. Investigation of higher-order closure is pursued on the level of an algebraic stress closure. Various turbulence model predictions are compared with experimental data from a variety of isothermal, confined studies. Supportive swirl comparisons are also performed for a laminar flow case, as well as reacting flow cases. Parallel predictions or contributions from other sources are also consulted where appropriate. Predictive accuracy was found to be a partial function of inlet boundary conditions and numerical diffusion. Despite prediction sensitivity to inlet conditions and numerics, the data comparisons delineate the relative advantages and disadvantages of the various modifications. Possible research avenues in the area of computational modeling of strongly swirling, recirculating flows are reviewed and discussed.

Bubbles, Drops, and Particles

Presentation d'une methode numerique par volume fini pour la resolution des equations de Navier-Stokes bidimensionnelles, incompressibles, et stationnaires, en coordonnees generales curvilignes Application de la methode aux ecoulements turbulents sur des profils avec et sans separation au bord de sortie posterieur Comparaison des calculs avec des donnees experimentales

Numerical Study of the Turbulent Flow Past an Airfoil with Trailing Edge Separation

In this review the theory and application of Smoothed particle hydrodynamics (SPH) since its inception in 1977 are discussed. Emphasis is placed on the strengths and weaknesses, the analogy with particle dynamics and the numerous areas where SPH has been successfully applied.

https://iopscience.iop.org/article/10.1088/0034-4885/68/8/R01/pdf

Smoothed particle hydrodynamics

PDF methods for turbulent reactive flows

https://www3.nd.edu/~powers/ame.60636/maas1992.pdf

Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space

In the past 30 years, there has been considerable progress in the development of large-eddy simulation (LES) for turbulent flows, which has been greatly facilitated by the substantial increase in computer power. In this paper, we raise some fundamental questions concerning the conceptual foundations of LES and about the methodologies and protocols used in its application. The 10 questions addressed are stated at the end of the introduction. Several of these questions highlight the importance of recognizing the dependence of LES calculations on the artificial parameter Δ (i.e. the filter width or, more generally, the turbulence resolution length scale). The principle that LES predictions of turbulence statistics should depend minimally on Δ provides an alternative justification for the dynamic procedure.

https://robots.iopscience.iop.org/article/10.1088/1367-2630/6/1/035/pdf

Ten questions concerning the large-eddy simulation of turbulent flows

A computational technique is described and demonstrated that can decrease by three orders of magnitude the computer time required to treat detailed chemistry in reactive flow calculations. The method is based on the in situ adaptive tabulation (ISAT) of the accessed region of the composition space - the adaptation being to control the tabulation errors. Test calculations are performed for non-premixed methane - air combustion in a statistically-homogeneous turbulent reactor, using a kinetic mechanism with 16 species and 41 reactions. The results show excellent control of the tabulation errors with respect to a specified error tolerance; and a speed-up factor of about 1000 is obtained compared to the direct approach of numerically integrating the reaction equations. In the context of PDF methods, the ISAT technique makes feasible the use of detailed kinetic mechanisms in calculations of turbulent combustion. The technique can also be used with reduced mechanisms, and in other approaches for calculating rea...

Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation

A discussion of the applicability of an effective-viscosity approach to turbulent flow suggests that there are flow situations where the approach is valid and yet present hypotheses fail. The general form of an effective-viscosity formulation is shown to be a finite tensor polynomial. For two-dimensional flows, the coefficients of this polynomial are evaluated from the modelled Reynolds-stress equations of Launder, Reece & Rodi (1975). The advantage of the proposed effective-viscosity formulation, equation (4.3), over isotropie-viscosity hypotheses is that the whole velocity-gradient tensor affects the predicted Reynolds stresses. Two notable consequences of this are that (i) the complete Reynolds-stress tensor is realistically modelled and (ii) the influence of streamline curvature on the Reynolds stresses is incorporated.

/pdf/a-more-general-effective-viscosity-hypothesis-yt4uaa1s2h.pdf

Stephen B. Pope

Papers

PDF methods for turbulent reactive flows

Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space

Ten questions concerning the large-eddy simulation of turbulent flows

Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation

A more general effective-viscosity hypothesis