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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The application of computer techniques for solving Navier-Stokes equations in support of wind tunnel tests is discussed. The ILLIAC IV computer is considered for this purpose and its limitations are analyzed. The author states that improved computers will make it possible to solve many aerodynamic problems and reduce the amount of wind tunnel testing required for adequate data processing.
To date the computation of the total circulation, or strength of a vortex has required detailed measurements of the velocity field within the vortex. In this paper a method is described in which the kinematics of the vortical flow field is exploited to calculate the strength of a vortex from relatively simple flow visualization measurements. There are several advantages in the technique, the most important being the newly acquired ability to calculate the transient changes in strength of a single vortex as it evolves. The method is applied to the study of vortex rings, although the development can be carried over directly to study vortex pairs, and it is expected that it can be generalized to other flows which contain regions of concentrated vorticity. The accuracy of the method as applied to vortex rings, assessed in part by comparing with the laser Doppler velocimeter (LDV) measurements of Sullivan et al., is shown to be excellent.
Growing interest in quantum computing and simulations have created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. While quantum computing is considered as a paradigm shift in our basic understanding of physical computation, effective implementation of quantum computing in energy applications also depends on progress and development in the dimensions of both quantum computing hardware and quantum computing algorithms. To fully address the status and future challenges of quantum information science (QIS) applied within the energy sector, in this presentation, we firstly summarize recent advancements on the applications of quantum computing to energy infrastructure and materials, complex energy system processes, advanced manufacturing, and energy system security. Then, we will demonstrate the results of quantum computing both on a simulator and a quantum device accessing from OLCF. Our first example is to use the variational quantum eigensolver (VQE) with a unitary coupled cluster with singles and doubles (UCCSD) ansatz to simulate a series of LixHyq molecules (q=-1, 0, +1). The obtained results showed that the quantum computing VQE-UCCSD is comparable to classical CCSD for small systems like LiH with respect to full configuration interaction (FCI). Targeting on CO2 capture application, our second example is to use VQE to quantify molecular vibrational energies and reaction pathways between CO2 and a simplified amine-based solvent model—NH3 to form H2NCOOH. This research showcases quantum computing applications in the study of CO2 capture reactions.
In this paper the concept for a software environment for developing engineering application systems for multiprocessor hardware (MIMD) is presented. The philosophy employed is to solve the largest problems possible in a reasonable amount of time, rather than solve existing problems faster. In the proposed environment most of the problems concerning parallel computation and handling of large distributed data spaces are hidden from the application program developer, thereby facilitating the development of large-scale software applications. Applications developed under the environment can be executed on a variety of MIMD hardware; it protects the application software from the effects of a rapidly changing MIMD hardware technology.
Some results for the bivariate normal distribution analysis are presented. Computer programs for conditional normal probabilities, marginal probabilities, as well as joint probabilities for rectangular regions are given: routines for computing fractile points and distribution functions are also presented. Some examples from a closed circuit television experiment are included.
Presented is a compilation of the history of a two-month project concerned with a survey, evaluation, and specification of a new computer system for the Applications Software Branch of the Software and Data Management Division of Information and Electronic Systems Laboratory of Marshall Space Flight Center, NASA. Information gathering consisted of discussions and surveys of branch activities, evaluation of computer manufacturer literature, and presentations by vendors. Information gathering was followed by evaluation of their systems. The criteria of the latter were: the (tentative) architecture selected for the new system, type of network architecture supported, software tools, and to some extent the price. The information received from the vendors, as well as additional research, lead to detailed design of a suitable system. This design included considerations of hardware and software environments as well as personnel issues such as training. Design of the system culminated in a recommendation for a new computing system for the Branch.
INL's High Performance Computing (HPC) accounts administration tool is a web application that that allows an administrator to change user information, create an HPC account, create or modify an LDAP group or project, and modify the members in a group. An administrator can search for a user, obtain the relevant information about the user, and update their account information. To create or modify an LDAP group or project, the administrator can filter a list of groups or projects according to the name, owner, or administrator that created the project or group, choose a name accordingly, and specify the group or project owner.
The computer program described calculates the design-point characteristics of a gas generator or a turbojet lift engine for V/STOL applications. The program computes the dimensions and mass, as well as the thermodynamic performance of the model engine and its components. The program was written in FORTRAN 4 language. Provision has been made so that the program accepts input values in either SI Units or U.S. Customary Units. Each engine design-point calculation requires less than 0.5 second of 7094 computer time.
This article details the implementation and application of the glass-specific computational constitutive model by Holmquist and Johnson [1] to simulate the dynamic response of soda-lime glass under high rate and high pressure shock conditions. The predictive capabilities of this model are assessed through comparison of experimental data with numerical results from computations using the CTH shock physics code. The formulation of this glass model is reviewed in the context of its implementation within CTH. Using a variety of experimental data compiled from the open literature, a complete parameterization of the model describing the observed behavior of soda-lime glass is developed. Simulation results using the calibrated soda-lime glass model are compared to flyer plate and Taylor rod impact experimental data covering a range of impact and failure conditions spanning an order of magnitude in velocity and pressure. In conclusion, the complex behavior observed in the experimental testing is captured well in the computations, demonstrating the capability of the glass model within CTH.
Patient-specific hemodynamic simulations have the possibility to greatly improve both the diagnosis and treatment of a variety of vascular diseases. Portability will enable wider adoption of computational fluid dynamics (CFD) applications in the biomedical research community and targeting to platforms ideally suited to different vascular regions. Here, we introduce a case study in performance portability that assesses (1) the ease of porting an MPI application optimized for one specific architecture to new platforms using variants of hybrid MPI+X programming models; (2) performance portability seen when simulating blood flow in three different vascular regions on diverse heterogeneous architectures; (3) model-based performance prediction for future architectures; and (4) performance scaling of the hybrid MPI+X programming on parallel heterogeneous systems. We report on the lessons learned in porting HARVEY, a massively parallel CFD application, from traditional multicore CPUs to diverse heterogeneous architectures ranging from NVIDIA/AMD GPUs to Intel MICs and Altera FPGAs.
Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.
Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.
Digital computer program for analysis and weight optimization of thermoelectric generators for space applications
This paper provides an overview of computer simulation, the Lockheed developed STS Processing Model, and the application of computer simulation to a wide range of processes. The STS Processing Model is an icon driven model that uses commercial off the shelf software and a Macintosh personal computer. While it usually takes one year to process and launch 8 space shuttles, with the STS Processing Model this process is computer simulated in about 5 minutes. Facilities, orbiters, or ground support equipment can be added or deleted and the impact on launch rate, facility utilization, or other factors measured as desired. This same computer simulation technology can be used to simulate manufacturing, engineering, commercial, or business processes. The technology does not require an 'army' of software engineers to develop and operate, but instead can be used by the layman with only a minimal amount of training. Instead of making changes to a process and realizing the results after the fact, with computer simulation, changes can be made and processes perfected before they are implemented.
The paper talks about applications in distribued quantum computing networks.
This paper describes a blueprint that can be used for developing a distributed computing environment (DCE) for NASA in general, and the Kennedy Space Center (KSC) in particular. A comprehensive, open, secure, integrated, and multi-vendor DCE such as OSF DCE has been suggested. Design issues, as well as recommendations for each component have been given. Where necessary, modifications were suggested to fit the needs of KSC. This was done in the areas of security and directory services. Readers requiring a more comprehensive coverage are encouraged to refer to the eight-chapter document prepared for this work.
The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. Along with hierarchical-heterogeneous memory, the system typically has a high-performing network and a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecture supports the convergence of the Big-Compute and Big-Data, the programming models have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. In this work, we propose and develop the programming abstraction called SHARed data-structure centric Programming abstraction (SharP) to address both of these goals, i.e., provide (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications. To evaluate SharP, we implement a Stencil benchmark using SharP, port QMCPack, a petascale-capable application, and adapt Memcached ecosystem, a popular Big-Data framework, to use SharP, and quantify the performance and productivity advantages. Additionally, we demonstrate the simplicity of using SharP on different memories including DRAM, High-bandwidth Memory (HBM), and non-volatile random access memory (NVRAM).
The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. along with hierarchical-heterogeneous memory, the system typically has a high-performing network ad a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also for running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecture supports the convergence of the Big-Compute and Big-Data, the programming models and software layer have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. A programming abstraction to address this problem. The programming abstraction is implemented as a software library and runs on pre-exascale and exascale systems supporting current and emerging system architecture. Using distributed data-structures as a central concept, it provides (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications.