Principles And Practice Of Physics 2nd Edition

[Lisa Nevilles]

Thisbook is an introduction to the theory, practice, and implementation of the Lattice Boltzmann (LB) method, a powerful computational fluid dynamics method that is steadily gaining attention due to its simplicity, scalability, extensibility, and simple handling of complex geometries. The book contains chapters on the method's background, fundamental theory, advanced extensions, and implementation.

Timm Krger is a Chancellor's Fellow at the School of Engineering, University of Edinburgh, UK. He obtained his PhD in Physics from Bochum University in 2011. His research interests include suspensions, interfacial phenomena, microfluidics and biophysical applications of blood flow.

Halim Kusumaatmaja is a Lecturer (Assistant Professor) at the Department of Physics, Durham University, UK. He obtained his PhD in Theoretical Condensed Matter Physics from the University of Oxford. He has a broad range of interests in Soft Matter and Biophysics, including wetting phenomena, membrane biophysics, liquid crystals and colloidal systems.

Alexandr Kuzmin is a Thermal/CFD Software Engineer at Maya Heat Transfer Technologies. He holds a PhD in CFD from the University of Calgary (Mechanical Engineering Department). He has a broad experience in computational geometry, heat and mass transfer, and numerical methods applied to industrial and research problems.

Orest Shardt is a postdoctoral research fellow in Mechanical and Aerospace Engineering at Princeton University. He graduated from the University of Alberta in 2014 with a PhD in chemical engineering. His main research interests are high performance computing and interfacial and electrokinetic phenomena in multiphase flows.

Goncalo Silva is a postdoctoral researcher in Mechanical Engineering at IDMEC/IST, University of Lisbon, from where he graduated in 2013 with a PhD in Mechanical Engineering. Between 2013 and 2016, he developed his postdoctoral research at IRSTEA, Antony, France. His main research interests are in the field of microfluidics and flows in porous media.

Erlend Magnus Viggen is a research scientist at SINTEF. He has a Master's in Applied Physics (2009) and a PhD in Acoustics (2014), both from the Norwegian University of Science and Technology. His main research interests are physical and computational acoustics.

Topics: Numerical and Computational Physics, Simulation, Fluid- and Aerodynamics, Complex Systems, Engineering Fluid Dynamics, Computational Mathematics and Numerical Analysis, Statistical Physics and Dynamical Systems

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Introductory physics is taught to several hundred thousand university students every year. It is seen as especially difficult by many and the failure rate is often high. A relevant question is whether one can increase the success rate among the weaker students? Retrieval practice is an established learning strategy with large benefits. However, as pointed out last year in this journal, hardly any systematic research has been done on retrieval practice in physics. Here we present a novel tool for retrieval practice in physics called the hierarchical principle structure for mechanics (HPSM). HPSM hierarchically organizes the essential principles, equations, and definitions for translational, rotational, and fluid mechanics, to emphasize meaningful connections. We investigated HPSM in a two-phase study. First, we present a randomized controlled experiment showing that 70 min of retrieval practice of HPSM had a very large effect on a declarative factual test compared to 70 min of problem study, d=1.42. In the second phase, which was carried out the following year, we implemented distributed retrieval practice of HPSM in the first 15 min of 16 lectures. Although difficult to disentangle the effect from the lectures it was embedded in, distributed retrieval practice of HPSM seems to promote factual knowledge (r=0.44) and better exam results for the weaker students (significant main and interaction effects).

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This edition includes 142 entries ranging from Aberrations to X-rays. All entries are arranged in an A to Z order, making it easy to find the topic of interest. Each entry includes related fields of study to illustrate the connections between the various branches of physics, including acoustics, high energy physics, psychophysics, quantum electrodynamics, and nanotechnology; a brief, concrete summary of the topic and how the entry was organized; principal terms that are fundamental to the discussion and to understanding the concepts presented; illustrations that clarify difficult concepts via models, diagrams, and charts of such key topics as blackbody radiation, Bernouilli's principle, and Higgs boson; equations that demonstrate how to determine mechanical advantage, understand the ideal gas law, the fundamentals of quantum mechanics, and Einstein's famous mass-energy equation-E=mc2; photographs of significant contributors to the study of physics; sample problems that further demonstrate the concept, law or constant presented; and biography lists that relates to the entry. Entries range from one to five pages in length.

This new resource is a helpful tool for students and researchers who are just beginning their study of physics and need a solid background of the key terms and elements in the field. A must for all high school and undergraduate science programs.

Diagram of the MDTF framework evolving under the current phase of development. The framework manages a set of process-oriented diagnostics modules (PODs) contributed by a variety of diagnostic development teams. Coordinated standards facilitate the inclusion/exchange of metrics and diagnostics with other parts of the U.S. diagnostics community.

Examples from the precipitation diurnal cycle POD showing phase-amplitude diagrams from (a) observational reference (using Tropical Rainfall Measuring Mission 3B42 dataset), (b) Community Atmospheric Model (CAM6), and (c) the earlier version, CAM5. Colors indicate the local hour of maximum precipitation with color intensity indicating diurnal cycle amplitude.

2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

In the quest to improve climate and weather model simulations, process-oriented diagnostics (PODs) have been advocated as a means of providing more information to model developers beyond performance-based metrics. A POD (Eyring et al. 2005; Sperber and Waliser 2008; Maloney et al. 2014; Kim et al. 2014; Eyring et al. 2019; Maloney et al. 2019) characterizes a physical process that is hypothesized to be related to the ability of a model to simulate an observed phenomenon. Evaluating a candidate model version against observations analyzed with such a POD can, in principle, give insight into whether a particular process is being well represented, focus model improvement on specific processes, and identify gaps in the understanding of phenomena. However, moving from principles to practice is a nontrivial step that requires thoughtful implementation. Here we draw on experience with the NOAA Model Diagnostics Task Force (MDTF) to provide best-practice recommendations for entraining diagnostics from a broad scientific community to make them available to model developers.

Developing a suitably comprehensive and useful package of diagnostics to enable effective climate model validation is a challenge. The set of phenomena that a numerical weather, climate, or Earth system model (ESM) is expected to capture is continually expanding, and the observational datasets to which the model can be compared continue to be expanded and improved (Teixeira et al. 2014; Eyring et al. 2016a). A model development team normally has a set of diagnostics from prior phases of model development but has limited resources for maintenance and further development of those diagnostics. Legacy diagnostics packages can quickly become outdated as new observational datasets are developed and are often associated with a particular developer who may have moved on from the organization. It can thus be highly advantageous to have a mechanism by which diagnostics development in a wider set of research groups can be brought into a coherent framework for use by the model development group.

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