TRW space radiator-condenser design and performance analysis computer programs Users' manual
Space radiator-condenser design and performance analysis computer programs - users manual
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Space radiator-condenser design and performance analysis computer programs - users manual
Computer acquired performance map of etched-rhenium, niobium planar diode
Computer processed performance data for thermionic converter with etched rhenium emitter and niobium collector
Computer acquired performance mapping of fixed spaced planar diode with etched Re emitter and Nb collector over wide operating temperature range
SHABERTH computer program developed to predict steady-state and transient thermal performance of multi-bearing shaft system operating with either wet or dry friction. Calculates loads, torques, temperatures, and fatigue lives for ball and/or roller bearings on single shaft. Enables study of many causes of instabilities in bearings. Also provides for analysis of reaction of system to termination of supply of lubricant to bearings and other lubricated mechanical elements. Valuable software tool in design and analysis of shaft bearing systems. Written in FORTRAN IV.
Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.
Computer acquired performance data from physically vapor deposited tungsten-niobium thermoinic converter
A guidance, navigation, and control system, the Simulated Shuttle Flight Test System (SS-FTS), when interfaced with existing aircraft systems, provides a research facility for studying concepts for landing the space shuttle orbiter and conventional jet aircraft. The SS-FTS, which includes a general-purpose computer, performs all computations for precisely following a prescribed approach trajectory while properly managing the vehicle energy to allow safe arrival at the runway and landing within prescribed dispersions. The system contains hardware and software provisions for navigation with several combinations of possible navigation aids that have been suggested for the shuttle. The SS-FTS can be reconfigured to study different guidance and navigation concepts by changing only the computer software, and adapted to receive different radio navigation information through minimum hardware changes. All control laws, logic, and mode interlocks reside solely in the computer software.
Conference poster using previously disclosed techniques and methods (see ARC 16210 & 16212). We present simulations of grid-fin control surfaces to demonstrate geometric complexity and numerical robustness. These results have relevance to high-performance computing and performance of grid-fin-based control systems.
The Computational Aerosciences (CAS) Project is part of NASA's High Performance Computing and Communications Program. Its primary goal is to accelerate the availability of high-performance computing technology to the US aerospace community-thus providing the US aerospace community with key tools necessary to reduce design cycle times and increase fidelity in order to improve safety, efficiency and capability of future aerospace vehicles. A complementary goal is to hasten the emergence of a viable commercial market within the aerospace community for the advantage of the domestic computer hardware and software industry. The CAS Project selects representative aerospace problems (especially design) and uses them to focus efforts on advancing aerospace algorithms and applications, systems software, and computing machinery to demonstrate vast improvements in system performance and capability over the life of the program. Recent demonstrations have served to assess the benefits of possible performance improvements while reducing the risk of adopting high-performance computing technology. This talk will discuss past accomplishments in providing technology to the aerospace community, present efforts, and future goals. For example, the times to do full combustor and compressor simulations (of aircraft engines) have been reduced by factors of 320:1 and 400:1 respectively. While this has enabled new capabilities in engine simulation, the goal of an overnight, dynamic, multi-disciplinary, 3-dimensional simulation of an aircraft engine is still years away and will require new generations of high-end technology.
The degradation of antenna performance during hypersonic re-entry is a well known phenomenon that can lead to complete radio blackout. Recent additions to the Empire code establish it as a tool for the study and analysis of the problem. Coupling to the Sandia Parallel Aerodynamics and Reentry Code (SPARC) enables the electromagnetic analysis of realistic re-entry plasma profiles. The geometric flexibility afforded by both Empire and SPARC allow the consideration of arbitrary vehicle and antenna configurations. We have used this tool to study antenna performance during re-entry when the boundary layer becomes turbulent. A concise description of line-of-sight transmissions, which employs advanced statistical methods, was developed. New insights into the low altitude reflectometer readings of RAM-C2 are offered. Techniques for the reconstruction of the re-entry plasma profile from reflectometer data were explored.
Computer program plots flight test data /stored on magnetic tape during the flight/ with comparative data from other tapes /design and post-flight predictions/. Information as to which measurements are on each tape, the order in which they appear, and the exact time span is supplied by the source of the data.
The design of interactive systems, including levels of automation, displays, and controls, is usually based on design guidelines and iterative empirical prototyping. A complementary approach is to use computational human performance models to evaluate designs. An integrated strategy of model-based and empirical test and evaluation activities is particularly attractive as a methodology for verification and validation of human-rated systems for commercial space. This talk will review several computational human performance modeling approaches and their applicability to design of display and control requirements.
The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is essential for addressing the increasingly complex national workforce demands stemming from the growth of computational science and engineering challenges. Computational science and engineering (CSE) takes a multidisciplinary approach that utilizes scientific computing to tackle practical problems and provide technical tools across the spectrum of scientific discovery. The DOE CSGF specifically highlights high-performance computing (HPC) as a critical enabling technology in CSE, driving advancements in science and engineering that are vital to both the DOE and the broader economy. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines have been transformed through the augmentation of scientific observation via HPC. At government laboratories, academic institutions, and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, quantum information systems, fusion-reactor design, machine learning, additive manufacturing, nano materials for next-generation batteries and transistors, and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing. Computational biology, machine learning, and quantum computing are among the subjects that began to swell in the ranks of CSGF applicants before the labs were hiring as high a percentage of employees in these categories.” The explosion of scientific and technological data has heightened the demand for advanced high-performance computing (HPC) to transform these data into meaningful scientific insights. As access to vast amounts of data increases, the fields of Machine Learning and Artificial Intelligence are experiencing a resurgence, enhancing the established practices of computational modeling and simulation. In its September 2020 subcommittee report on "AI/ML, Data Intensive Science, and High-Performance Computing," the DOE Advanced Scientific Computing Advisory Committee (ASCAC) specifically called for a fellowship program to train computational and data scientists to address exascale and data-intensive computing challenges. This integration of empirical and theoretical modeling will increasingly guide federal policymakers in making decisions that impact American society and future generations. It demands a workforce of highly skilled and intellectually agile computational scientists capable of navigating the rapid advancements in scientific computing within the DOE National Laboratory research environment. The DOE CSGF program has consistently addressed this critical need.
This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey.
Computer program developed for analyzing thermodynamic characteristics of kinematic Stirling engine. Computes time-varying piston positions, pressures, and gas temperatures in each of gas-control volumes into which engine working space is divided. Engine performance characterized by calculations of power and efficiency (both indicated and brake). Inputs to code are engine geometrical parameter, engine-operating conditions, and indexes that specify various options available.
Developed to give a quick and accurate predictions HISPER, a simplification of the TRNSYS program, achieves its computational speed by not simulating detailed system operations or performing detailed load computations. In order to validate the HISPER computer for air systems the simulation was compared to the actual performance of an operational test site. Solar insolation, ambient temperature, water usage rate, and water main temperatures from the data tapes for an office building in Huntsville, Alabama were used as input. The HISPER program was found to predict the heating loads and solar fraction of the loads with errors of less than ten percent. Good correlation was found on both a seasonal basis and a monthly basis. Several parameters (such as infiltration rate and the outside ambient temperature above which heating is not required) were found to require careful selection for accurate simulation.
On behalf of the High Performance Computing and Modernization Program (HPCMP) and NASA Advanced Supercomputing Division (NAS) a study is conducted to assess the role of supercomputers on computational aeroelasticity of aerospace vehicles. The study is mostly based on the responses to a web based questionnaire that was designed to capture the nuances of high performance computational aeroelasticity, particularly on parallel computers. A procedure is presented to assign a fidelity-complexity index to each application. Case studies based on major applications using HPCMP resources are presented.