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At least 91 records · Page 5

Reconfigurable modular computer networks for spacecraft on-board processing

The core electronics subsystems on unmanned spacecraft, which have been sent over the last 20 years to investigate the moon, Mars, Venus, and Mercury, have progressed through an evolution from simple fixed controllers and analog computers in the 1960's to general-purpose digital computers in current designs. This evolution is now moving in the direction of distributed computer networks. Current Voyager spacecraft already use three on-board computers. One is used to store commands and provide overall spacecraft management. Another is used for instrument control and telemetry collection, and the third computer is used for attitude control and scientific instrument pointing. An examination of the control logic in the instruments shows that, for many, it is cost-effective to replace the sequencing logic with a microcomputer. The Unified Data System architecture considered consists of a set of standard microcomputers connected by several redundant buses. A typical self-checking computer module will contain 23 RAMs, two microprocessors, one memory interface, three bus interfaces, and one core building block.

Rennels, D. A.↗

Preserving and Curating the Moon: Adventures in Lunar Core Processing

The lunar crust is the most easily accessible part of the Moon to both remote sensing and sample analyses and provides an archive of information about planetary formation, crustal evolution, and contains a wealth of information about the origin of the Earth-Moon system [e.g., 1-5]. The Apollo mission returned 382 kg of rocks, soil and core samples. Studies of these lunar samples are crucial for our understanding of the Moon’s formation and geological evolution, and for the past 50 years these returned samples have provided the foundation for lunar science [5]. The returned samples are stored and cared for in the lunar curation facility at NASA’s Johnson Space Center. This facility is comprised of a large suite of clean rooms, sample vaults for pristine and return samples, thin section labs, core and saw rooms, storage and working areas, and ancillary labs all designed to minimize contamination from the environment and other samples. Some of the returned samples were intentionally set aside and left unopened. Recently, the Apollo Next Generation Sample Analysis (ANGSA) initiative was designed to examine these pristine samples so the next generation of lunar scientists can further our insight into the Moon’s history. Here, we present the meticulous process that involves preparing for, and ultimately opening, one of the unopened core samples: Apollo 17 drive tube 73002,0,which was collected on the Moon from a landslide deposit near Lara Crater by astronauts Gene Cernan and Jack Schmitt. In order to open, examine, and curate 73002,0withminimalpotential contamination, great care had to be taken prior to opening its container. Beginning18 months before extrusion of the sample, all core processing equipment was pulled out of storage, identified, sorted, cleaned, and purged with nitrogen gas. However, limited institutional memory has made this step challenging as most of the former core processors from the Apollo area have retired or passed away. Twelvemonths prior to extrusion, table-top rehearsals were initiated to identify equipment and learn how it fits together and operates. Five months before extruding the real core, preparations further evolved to include the extrusion and dissection of a lunar core simulant. In addition, a mock-up glovebox was designed and built to allow for a more realistic practice environment. One month prior to extrusion, the actual core cabinet was prepared for use, which included fitting it with lights, a webcam, and power. The tool and equipment cleaning procedure was also modified to include increased cleanliness and sterility requirements. While still sealed, the core was CT scanned at the University of Texas at Austin to maximize its scientific return. Days before the extrusion, witness plates and foil were deployed inside the core cabinet to monitor potential particle and organic contamination within the cabinet. On Nov. 5th, 2019, core sample 73002,0 was successfully opened and extruded(Fig.1). Dissection of 73002,0 began immediately afterwards and is still under way. Processing this sample will help us prepare for future sampling missions and core extrusions and will enable new scientific discoveries about the Moon.

C H Krysher↗

Parallel-vector out-of-core equation solver for computational mechanics

A parallel/vector out-of-core equation solver is developed for shared-memory computers, such as the Cray Y-MP machine. The input/ output (I/O) time is reduced by using the a synchronous BUFFER IN and BUFFER OUT, which can be executed simultaneously with the CPU instructions. The parallel and vector capability provided by the supercomputers is also exploited to enhance the performance. Numerical applications in large-scale structural analysis are given to demonstrate the efficiency of the present out-of-core solver.

Qin, J.↗

Computer memory access technique

Computer memory access commutator and steering gate configuration produces bipolar current pulses while still employing only the diodes and magnetic cores of the classic commutator, thereby appreciably reducing the complexity of the memory assembly.

Zottarelli, L. J.↗

A study of application of remote sensing to river forecasting. Volume 2: Detailed technical report, NASA-IBM streamflow forecast model user's guide

The Model is described along with data preparation, determining model parameters, initializing and optimizing parameters (calibration) selecting control options and interpreting results. Some background information is included, and appendices contain a dictionary of variables, a source program listing, and flow charts. The model was operated on an IBM System/360 Model 44, using a model 2250 keyboard/graphics terminal for interactive operation. The model can be set up and operated in a batch processing mode on any System/360 or 370 that has the memory capacity. The model requires 210K bytes of core storage, and the optimization program, OPSET (which was used previous to but not in this study), requires 240K bytes. The data band for one small watershed requires approximately 32 tracks of disk storage.

Source record↗

Navier-Stokes Simulation of Homogeneous Turbulence on the CYBER 205

A computer code which solves the Navier-Stokes equations for three dimensional, time-dependent, homogenous turbulence has been written for the CYBER 205. The code has options for both 64-bit and 32-bit arithmetic. With 32-bit computation, mesh sizes up to 64 (3) are contained within core of a 2 million 64-bit word memory. Computer speed timing runs were made for various vector lengths up to 6144. With this code, speeds a little over 100 Mflops have been achieved on a 2-pipe CYBER 205. Several problems encountered in the coding are discussed.

Wu, C. T.↗

Out-of-Core Streamline Visualization on Large Unstructured Meshes

It's advantageous for computational scientists to have the capability to perform interactive visualization on their desktop workstations. For data on large unstructured meshes, this capability is not generally available. In particular, particle tracing on unstructured grids can result in a high percentage of non-contiguous memory accesses and therefore may perform very poorly with virtual memory paging schemes. The alternative of visualizing a lower resolution of the data degrades the original high-resolution calculations. This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that during the streamline construction only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-20 megabytes. Our test results also show that this approach is much more efficient than relying on virtual memory and operating system's paging algorithms.

Ueng, Shyh-Kuang↗

PSA: A program to streamline orbit determination for launch support operations

An interactive, menu driven computer program was written to streamline the orbit determination process during the critical launch support phase of a mission. Residing on a virtual memory minicomputer, this program retains the quantities in-core needed to obtain a least squares estimate of the spacecraft trajectory with interactive displays to assist in rapid radio metric data evaluation. Menu-driven displays allow real time filter and data strategy development. Graphical and tabular displays can be sent to a laser printer for analysis without exiting the program. Products generated by this program feed back to the main orbit determination program in order to further refine the estimate of the trajectory. The final estimate provides a spacecraft ephemeris which is transmitted to the mission control center and used for antenna pointing and frequency predict generation by the Deep Space Network. The development and implementation process of this program differs from that used for most other navigation software by allowing the users to check important operating features during development and have changes made as needed.

Legerton, V. N.↗

Computational mechanics analysis tools for parallel-vector supercomputers

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigensolution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization search analysis and domain decomposition. The source code for many of these algorithms is available.

Storaasli, Olaf O.↗

Computational mechanics analysis tools for parallel-vector supercomputers

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigen-solution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization algorithm and domain decomposition. The source code for many of these algorithms is available from NASA Langley.

Storaasli, O. O.↗

A Simple GPU-Accelerated Two-Dimensional MUSCL-Hancock Solver for Ideal Magnetohydrodynamics

We describe our experience using NVIDIA's CUDA (Compute Unified Device Architecture) C programming environment to implement a two-dimensional second-order MUSCL-Hancock ideal magnetohydrodynamics (MHD) solver on a GTX 480 Graphics Processing Unit (GPU). Taking a simple approach in which the MHD variables are stored exclusively in the global memory of the GTX 480 and accessed in a cache-friendly manner (without further optimizing memory access by, for example, staging data in the GPU's faster shared memory), we achieved a maximum speed-up of approx. = 126 for a sq 1024 grid relative to the sequential C code running on a single Intel Nehalem (2.8 GHz) core. This speedup is consistent with simple estimates based on the known floating point performance, memory throughput and parallel processing capacity of the GTX 480.

graphics processing units↗

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 kin or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed- shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗

On Limits

In the last 3 decades or so, the size of systems we have been able to verify formally with automated tools has increased dramatically. At each point in this development, we encountered a different set of limits -- many of which we were eventually able to overcome. Today, we may have reached some limits that may be much harder to conquer. The problem I will discuss is the following: given a hypothetical machine with infinite memory that is seamlessly shared among infinitely many CPUs (or CPU cores), what is the largest problem size that we could solve?

Holzmann, Gerard J.↗

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 km or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed-shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Lin, Shian-Jiann↗

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Detect-And-Avoid Systems

Java Architecture for Detect-And-Avoid (DAA) Extensibility and Modeling (JADEM) was developed at NASA Ames Research Center as a research and modeling tool for Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS). UAS will be required to have DAA systems in order to fulfill the regulatory requirement to remain well clear'' of other traffic. JADEM supports research on technological requirements and Minimum Operational Performance Standards (MOPS) for UAS DAA systems by providing a flexible and extensible software platform that includes models and algorithms for all major DAA functions. This paper describes one of these algorithms, the Generic Resolution Advisor and Conflict Evaluator (GRACE). GRACE supports two core DAA functions: threat evaluation and guidance. GRACE is generic in the sense that it is designed to work with any aircraft or sensor type (both cooperative and non-cooperative), and to be used in various applications and DAA guidance concepts, thus supporting evolving MOPS requirements and research. GRACE combines flexibility, robustness, and computational efficiency. It has modest memory requirements and can handle multiple cooperative and noncooperative intruders. GRACE has been used as a core JADEM component in several real-time and fast-time experiments, including human-in-the-loop simulations and live flight tests.

detect and avoid↗