SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments

Pressure and momentum field investigation of a centrifugal pump through dynamic loading of a semi-open impeller

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references: p. 58-59.Issued also on microfiche from Lange Micrographics.A study was performed to investigate the variation in the forces and moments acting on the front and back sides of a semi-open impeller. Three rotational speeds and five volumetric flow rates for each speed were identified as the operating conditions for the pump after generating performance curves. The pressure distribution inside the pump housing was measured through pressure taps drilled in the front and back housing. These pressure measurements were obtained for distinct geometric configurations comprising of varying positions of the impeller and the front housing. Pressure contour plots were generated for all the operating conditions and an asymmetric pressure distribution was observed in the pump housing. Higher pressures were witnessed near the volute tongue. Forces acting on the impeller were calculated by integrating the pressures acting on it as measured by the pressure taps. A net axial thrust on the impeller, trying to push it towards the suction side, was observed, which decreased in magnitude with increasing back clearance for a certain fixed value of the front clearance. Moments acting on the front and back sides of the impeller were noticed to approach smaller magnitudes for higher volumetric flow rates. A geometric configuration was identified for better overall performance of the pump

i-PI 2.0: a universal force engine for advanced molecular simulations

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born–Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code (Ceriotti et al., 2014) was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives. Program summary: Program Title: i-PI Program Files doi: http://dx.doi.org/10.17632/x792grbm9g.1 Licensing provisions: GPLv3, MIT Programming language: Python External routines/libraries: NumPy Nature of problem: Lowering the implementation barrier to bring state-of-the-art sampling and atomistic modeling techniques to ab initio and empirical potentials programs. Solution method: Advanced sampling methods, including path-integral molecular dynamics techniques, are implemented in a Python interface. Any electronic structure code can be patched to receive the atomic coordinates from the Python interface, and to return the forces and energy that are used to integrate the equations of motion, optimize atomic geometries, etc. Restrictions: This code does not compute interatomic potentials, although the distribution includes sample driver codes that can be used to test different techniques using a few simple model force fields