A computer program for the design and analysis of low-speed airfoils

A conformal mapping method for the design of airfoils with prescribed velocity distribution characteristics, a panel method for the analysis of the potential flow about given airfoils, and a boundary layer method have been combined. With this combined method, airfoils with prescribed boundary layer characteristics can be designed and airfoils with prescribed shapes can be analyzed. All three methods are described briefly. The program and its input options are described. A complete listing is given as an appendix

A computer program for the design and analysis of low-speed airfoils, supplement

Three new options were incorporated into an existing computer program for the design and analysis of low speed airfoils. These options permit the analysis of airfoils having variable chord (variable geometry), a boundary layer displacement iteration, and the analysis of the effect of single roughness elements. All three options are described in detail and are included in the FORTRAN IV computer program

Low speed airfoil design and analysis

A low speed airfoil design and analysis program was developed which contains several unique features. In the design mode, the velocity distribution is not specified for one but many different angles of attack. Several iteration options are included which allow the trailing edge angle to be specified while other parameters are iterated. For airfoil analysis, a panel method is available which uses third-order panels having parabolic vorticity distributions. The flow condition is satisfied at the end points of the panels. Both sharp and blunt trailing edges can be analyzed. The integral boundary layer method with its laminar separation bubble analog, empirical transition criterion, and precise turbulent boundary layer equations compares very favorably with other methods, both integral and finite difference. Comparisons with experiment for several airfoils over a very wide Reynolds number range are discussed. Applications to high lift airfoil design are also demonstrated

Aerodynamic Design of a Propeller for High-Altitude Balloon Trajectory Control

The aerodynamic design of a propeller for the trajectory control of a high-altitude, scientific balloon has been performed using theoretical methods developed especially for such applications. The methods are described. Optimum, nonlinear chord and twist distributions have been developed in conjunction with the design of a family of airfoils, the SE403, SE404, and SE405, for the propeller. The very low Reynolds numbers along the propeller blade fall in a range that has yet to be rigorously investigated, either experimentally or theoretically

Interviews with the Apollo lunar surface astronauts in support of planning for EVA systems design

Focused interviews were conducted with the Apollo astronauts who landed on the moon. The purpose of these interviews was to help define extravehicular activity (EVA) system requirements for future lunar and planetary missions.RTOP 199-06-1

PyNEST: A Convenient Interface to the NEST Simulator

The neural simulation tool NEST (http://www.nest-initiative.org) is a simulator for heterogeneous networks of point neurons or neurons with a small number of compartments. It aims at simulations of large neural systems with more than 104 neurons and 107 to 109 synapses. NEST is implemented in C++ and can be used on a large range of architectures from single-core laptops over multi-core desktop computers to super-computers with thousands of processor cores. Python (http://www.python.org) is a modern programming language that has recently received considerable attention in Computational Neuroscience. Python is easy to learn and has many extension modules for scientific computing (e.g. http://www.scipy.org). In this contribution we describe PyNEST, the new user interface to NEST. PyNEST combines NEST's efficient simulation kernel with the simplicity and flexibility of Python. Compared to NEST's native simulation language SLI, PyNEST makes it easier to set up simulations, generate stimuli, and analyze simulation results. We describe how PyNEST connects NEST and Python and how it is implemented. With a number of examples, we illustrate how it is used

PyNN: A Common Interface for Neuronal Network Simulators

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN

SLS-Derived Lab- Precursor to Deep Space Human Exploration

Plans to send humans to Mars are in the works and the launch system is being built. Are we ready? Transportation, entry, landing, and surface operations have been successfully demonstrated for robotic missions. However, for human missions, there are significant, potentially show-stopping issues. These issues, called Strategic Knowledge Gaps (SKGs), are the unanswered questions concerning long duration exploration Beyond low Earth Orbit (BEO). The gaps represent a risk of loss of life or mission and because they require extended exposure to the weightless environment outside of earth's protective geo-magnetic field, they cannot be resolved on Earth or on the International Space Station (ISS). Placing a laboratory at a relatively close and stable lunar Distant Retrograde Orbit (DRO) provides an accessible location with the requisite environmental conditions for conducting SKG research and testing mitigation solutions. Configurations comprised of multiple 3 m and 4.3 m diameter modules have been studied but the most attractive solution uses elements of the human Mars launch vehicle or Space Launch System (SLS) for a Mars proving ground laboratory. A shortened version of an SLS hydrogen propellant tank creates a Skylab-like pressure vessel that flies fully outfitted on a single launch. This not only offers significant savings by incorporating SLS pressure vessel development costs but avoids the expensive ISS approach using many launches with substantial on-orbit assembly before becoming operational. One of the most challenging SKGs is crew radiation protection; this is why SKG laboratory research is combined with Mars transit habitat systems development. Fundamentally, the two cannot be divorced because using the habitat systems for protection requires actual hardware geometry and material properties intended to contribute to shielding effectiveness. The SKGs are difficult problems. The solutions to these problems are not obvious; they require integrated, iterative, and multi-disciplinary development. A lunar DRO lab built from SLS elements enables an early and representative transit habitat test bed necessary for closing gaps before sending humans on a 1,000-day Mars mission