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Results for “accelerated computation”

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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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Comparison Between THOR Anthropomorphic Test Device and THOR Finite Element Model

Extended time spent in reduced gravity can cause physiologic deconditioning of astronauts, reducing their ability to sustain excessive forces during dynamic phases of spaceflight such as landing. To make certain that the crew is safe during these phases, NASA must take caution when determining what types of landings are acceptable based on the accelerations applied to the astronaut. In order to test acceptable landings, various trials have been run accelerating humans, cadavers, and Anthropomorphic Test Devices (ATDs), or crash test dummies, at different acceleration and velocity rates on a sled testing platform. Using these tests, risks of injury will be created and metrics will be developed for the likelihood of injuries due to the acceleration. A finite element model (FEM) of the Test Device for Human Occupant Restraint (THOR) ATD has been developed that can simulate these test trials and others (Putnam, 2014), reducing the need for human and ATD testing. Additionally, this will give researchers a more effective way to test the accelerations and orientations encountered during spaceflight landings during design of new space vehicles for crewed missions. However, the FEM has not been proven and must be validated by comparing the forces, accelerations, and other measurements of all parts of the body between the physical tests already completed and computer simulated trials. The purpose of my research was to validate the FEM for the ATD using previously run trials with the physical THOR ATD.

Moore, Erik↗
NASA NTRS2022

Design of a Low-Cost, Submersible, Digital Holographic Microscope for in Situ Microbial Imaging

The methodologies for studying marine microbiology typically consist of utilizing instrumentation within a laboratory. This typically requires extracting a sample from its place of origin prior to examination, which may be days after collection. Oftentimes, the solution is to bring the lab to the ocean, which may be costly and provide further limitations for a sterile and stable laboratory environment. Here we present a low-cost, submersible, digital holographic microscope (DHM) designed to image marine microorganisms (such as bacteria and plankton) in their natural underwater environment. Our instrument eliminates the need to transport samples and allows for instantaneous data collection of microbes in-situ. The DHM achieves sub-micron spatial resolution and is paired with artificial intelligence for the detection and tracking of specimens to reduce the overall collected data. This instrument also aims to reduce the cost of manufacturing and field expenses relative to marine microbiological research. “Off the shelf” components were selected in the design process of this instrument which allows us to achieve precise results without sacrificing data quality. The DHM itself costs under one thousand dollars and features a low-cost high-resolution camera, the Arducam MT9J001. Included in the design were five main subsystems: optical, mechanical, electrical, power, and machine learning. Our on board computer and artificial intelligence consist of a Raspberry Pi 4 (8 Gb) and Google Coral USB Tensorflow accelerator. Instrument testing has successfully proven our abilities of data acquisition for at least two hours in depths of at least forty meters below sea level. Additionally, our artificial intelligence system is currently capable of tracking up to ten areas of interest in a fraction of a second with over ninety percent confidence via the neural net driven by the tensor cores on the Google Coral. Furthermore, we have demonstrated the versatility of our instrument by mounting it on an ocean-going ROV, the BlueRov2 by BlueRobotics. Our tests on the BlueRov2 exemplified the cost-effective nature of a submersible and reusable instrument that can be implemented in moderate environments and on most vessels.

Wallace, James Kent↗
DOE OSTI2022

DenKv: Addressing Design Trade-offs of Key-value Stores for Scientific Applications

High-performance computing (HPC) facilities have employed flash-based storage tier near to compute nodes to absorb high I/O demand by HPC applications during periodic system-level checkpoints. To accelerate these checkpoints, proxy-based distributed key-value stores (PD-KVS) gained particular attention for their flexibility to support multiple backends and different network configurations. PD-KVS rely internally on monolithic KVS, such as LevelDB or RocksDB, to exploit the KV interface and query support. However, PD-KVS are unaware of the high redundancy factor in checkpoint data, which can be up to GBs to TBs, and therefore, tend to generate high write and space amplification on these storage layers. In this paper, we propose DenKv which is deduplication-extended node-local LSM-tree-based KVS. DenKv employs asynchronous partially inline dedup (APID) and aims to maintain the performance characteristics of LSM-tree-based KVS while reducing the write and space amplification problems. We implemented DenKv atop BlobDB and showed that our proposed solution maintains performance while reducing write amplification up to 2× and space amplification by 4× on average.

Khan, Awais↗
DOE OSTI2021

A Hybrid Climate Modeling System Using AI-assisted Process Emulators

This white paper addresses Focus Area II. We advocate developing a hybrid modeling system to improve the understanding of decadal- and longer-scale predictability of high impact water cycle components. This hybrid model combines a partial differential equation (PDE)-based dynamic core with AI/ML based emulators to represent many of the computationally expensive processes in Earth’s climate models. The hybrid modeling system has the potential to exploit emerging graphics processing unit (GPU)-accelerated architectures and allows for the generation of large ensemble (~1000’s) simulations to better characterize the model uncertainty and understand predictability.

58 GEOSCIENCES↗
DOE OSTI2022

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physics-based information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.

Ziabari, Amir↗
NASA NTRS1979

Two dimensional aerodynamic interference effects on oscillating airfoils with flaps in ventilated subsonic wind tunnels

The numerical computation of unsteady airloads acting upon thin airfoils with multiple leading and trailing-edge controls in two-dimensional ventilated subsonic wind tunnels is studied. The foundation of the computational method is strengthened with a new and more powerful mathematical existence and convergence theory for solving Cauchy singular integral equations of the first kind, and the method of convergence acceleration by extrapolation to the limit is introduced to analyze airfoils with flaps. New results are presented for steady and unsteady flow, including the effect of acoustic resonance between ventilated wind-tunnel walls and airfoils with oscillating flaps. The computer program TWODI is available for general use and a complete set of instructions is provided.

Fromme, J.↗
NASA NTRS1992

Design tradeoffs on engine-integrated hypersonic vehicles

Two classes of airbreathing hypersonic vehicle concepts, one for primarily cruise missions and the other for accelerator type missions, are presented. Both are designed with waverider airframes and hydrogen-fueled scramjet engine modules. Cruise configurations are optimized for the product of I(sp) and L/D while matching lift to weight and thrust to drag at some equivalence ratio. Accelerator configurations are optimized for effective specific impulse while matching lift to weight at an equivalence ratio of one. The method and computer code developed to optimize the configurations are discussed. The features and design tradeoffs for each class of vehicles are described. Recently available weight estimates for all-body waveriders have had a significant impact on the integrated configurations. Mach 8 vehicles at 40 km altitude optimized with the cruise objective function have L/Ds of 2.55 to 2.92 and I(sp)s of 2850 to 2940 sec. A Mach 14 vehicle at 40-km altitude optimized with the accelerator objective function has an I(sp) sub eff of 189 sec, and a Mach 10 vehicle an I(sp) sub eff of 880 sec.

O'Neill, Mary Kae L.↗
DOE OSTI2023

Artificial Intelligence-Enhanced, Multi-Level, Modular System Design

As Moore’s Law and Dennard Scaling come to an end, it is becoming increasingly important to develop non-von Neumann computing architectures that can perform low-power computing in the domains of scientific computing, artificial intelligence, embedded systems, and edge computing. Next-generation computing technologies, such as neuromorphic computing and quantum computing, have the potential to revolutionize computing. However, in order to make progress in these fields, it is necessary to fundamentally change the current computing paradigm by codesigning systems across all system level, from materials to software. Because skilled labor is limited in the field of next-generation computing, we are developing artificial intelligence-enhanced tools to automate the codesign and co-discovery of next-generation computers. Here, we develop a method called Modular and Multi-level MAchine Learning (MAMMAL) which is able to perform analog codesign and co-discovery across multiple system levels, spanning devices to circuits. We prototype MAMMAL by using it to design simple passive analog low-pass filters. We also explore methods to incorporate uncertainty quantification into MAMMAL and to accelerate MAMMAL by using emerging technologies, such as crossbar arrays. Ultimately, we believe that MAMMAL will enable rapid progress in developing next-generation computers by automating the codesign and co-discovery of electronic systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Spectrum formation in supernovae - Numerical techniques

The study combines several novel techniques for spectrum simulation in the Eddington computer program which solves the comoving frame equation of transfer coupled with the statistical and radiative equilibrium equations. One of these is a generalization of the accelerated lambda iteration (ALI) scheme to include an approximate frequency-derivative operator. This greatly enhances the convergence rate of ALI in optically thick, high-velocity shear flows. Another is a partial linearization technique which is capable of efficiently solving a very large number of rate equations on a moderately sized computer. An expansion opacity and emissivity approximation is derived which makes it possible to determine the effect on the transfer and statistical equilibrium of a very large number of lines not explicitly represented in the frequency grid and additionally to treat line-blanketing from species not explicitly included in the rate equations. The utility of these techniques is illustrated with models of two supernovae.

Eastman, Ronald G.↗
NASA NTRS1989

Numerical simulation of vortical flows on flexible wings

A procedure to simultaneously solve the Navier-Stokes equations and modal structural equations of motion is presented for computing aeroelastic responses of wings. The Navier-Stokes flow equations are solved by a finite-difference scheme with dynamic grids. The coupled aeroelastic equations of motion are solved using the linear-acceleration method. The aeroelastic, configuration-adaptive dynamic grids are time-accurately generated using the aeroelastically deformed shape of the wing. The unsteady flow calculations are validated with the experiment. Present development is demonstrated for computing vortical flows over flexible wings.

Guruswamy, Guru P.↗
NASA NTRS1988

Simulation of electron distributions within auroral acceleration regions

Plasma data from the Dynamics Explorer 1 satellite are used to investigate the electron distributions found within regions of parallel potential drops. A computer simulation was developed to predict major features of the observed electron distributions found within these regions. The simulation results are discussed and compared with observations. Good agreement is observed only when the potential distribution along the field line is split into separate potential drops, one above and one below the satellite. The simulated electron distribution exhibits a trapped particle region, the formation of the electron hole, and the evacuation of the loss cone.

Gurgiolo, C.↗
NASA NTRSDate not supplied

Quantum-Accelerated Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

We present two algorithms in the Quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability; One for producing an approximately optimal Steiner Tree, and one for producing an exact Minimum Directed Spanning tree. These use O(n1/4) rounds of communication and O(n9/4) messages, leading to a quantum speedup in round and message complexity compared to any known algorithms in the classical CONGEST-CLIQUE model (vs O(n1/3) and O(n7/3)). At a high level, we achieve these results by combining classical algorithms with fast quantum subroutines. Further, these problems can not be sped up in the CONGEST (non-clique) setting, and we characterize the constants involved.

Phillip Kerger↗
NASA NTRSDate not supplied

Quantum-Accelerated Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

We present two algorithms in the Quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability; one for producing an approximately optimal Steiner Tree, and one for producing an exact spanning arborescence of minimum weight, the analog of a Minimum Spanning Tree in a directed graph, each of which uses O~(n^(1/4)) rounds of communication and O~(n^(9/4)) messages, achieving a lower round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. The CONGEST distributed computational model allows limited-sized messages to be transmitted within a network described by a communication graph of size n in a series of rounds to address a computational problem. The size limitation for such messages isO(log(n)) bits at each edge of the communication graph per round. The communication graph in the CONGEST-CLIQUE model is fully connected. In the Quantum CONGEST-CLIQUE model, at most O(log(n)) classical and quantum bits (qubits) can be communicated across each edge of the communication graph per round. At a high level, we achieve these results by combining classical algorithms with fast quantum subroutines. These speedups further contribute to understanding what problems can be solved more efficiently when we allow quantum communication in this CONGEST-CLIQUE model of distributed computation.

quantum distributed algorithms↗
DOE OSTI2020

A fast implicit solver for semiconductor models in one space dimension

Several different approaches are proposed for solving fully implicit discretizations of a simplified Boltzmann-Poisson system with a linear relaxation-type collision kernel. This system models the evolution of free electrons in semiconductor devices under a low-density assumption. At each implicit time step, the discretized system is formulated as a fixed-point problem, which can then be solved with a variety of methods. A key algorithmic component in all the approaches considered here is a recently developed sweeping algorithm for Vlasov-Poisson systems. A synthetic acceleration scheme has been implemented to accelerate the convergence of iterative solvers by using the solution to a drift-diffusion equation as a preconditioner. The performance of four iterative solvers and their accelerated variants has been compared on problems modeling semiconductor devices with various electron mean-free-path.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Holistic Algorithmic Approach to Improving Accuracy, Robustness, and Computational Efficiency for Atmospheric Dynamics

Atmospheric weather and climate models must perform simulations very quickly to be useful. Therefore, modelers have traditionally focused on reducing computations as much as possible. However, in our new era of increasingly compute-capable hardware, data movement is now the prohibiting expense. This study examines the computational benefits of a new algorithmic approach to modeling atmospheric dynamics on scales relevant to weather and climate simulation. Rather than minimizing computations, this new approach considers the larger problem more holistically, including spatial accuracy, temporal accuracy, robustness (i.e., oscillations), on-node efficiency, and internode data transfers together at once. Numerical experiments demonstrate how computations can be strategically increased to simultaneously address each of these constraints while reducing data movement to adapt to modern accelerated hardware. The new algorithm can achieve at times up to 80% peak floating point throughput in single precision on the Nvidia Tesla V100 GPU, where the traditional approach is shown to only achieve single-digit floating point efficiency. Further, the new algorithm is twice as fast as a standard Runge--Kutta time integrator, and high-order accuracy with Weighted Essentially Non-Oscillatory (WENO) limiting came at less than 30% additional runtime cost on a GPU, thus increasing the accuracy per degree of freedom.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Predictions of cardiovascular responses during STS reentry using mathematical models

The physiological adaptation to weightless exposure includes cardiovascular deconditioning arising in part from a loss of total circulating blood volume and resulting in a reduction of orthostatic tolerance. The crew of the Shuttle orbiter are less tolerant to acceleration forces in the head-to-foot direction during the reentry phase of the flight at a time they must function at a high level of performance. The factors that contribute to orthostatic intolerance during and following reentry and to predict the likelihood of impaired crew performance are evaluated. A computer simulation approach employing a mathematical model of the cardiovascular system is employed. It is shown that depending on the severity of blood volume loss, the reentry acceleration stress may be detrimental to physiologic function and may place the physiologic status of the crew near the borderline of some type of impairment. They are in agreement with conclusions from early ground-based experiments and from observations of early Shuttle flights.

Leonard, J. I.↗
NASA NTRS1990

Preconditioned upwind methods to solve 3-D incompressible Navier-Stokes equations for viscous flows

A computational method for calculating low-speed viscous flowfields is developed. The method uses the implicit upwind-relaxation finite-difference algorithm with a nonsingular eigensystem to solve the preconditioned, three-dimensional, incompressible Navier-Stokes equations in curvilinear coordinates. The technique of local time stepping is incorporated to accelerate the rate of convergence to a steady-state solution. An extensive study of optimizing the preconditioned system is carried out for two viscous flow problems. Computed results are compared with analytical solutions and experimental data.

Hsu, C.-H.↗
DOE OSTI2022

Accelerating shared file checkpoint with local burst buffers

A data management system and method for accelerating shared file checkpointing. Written application data is aggregated in an application data file created in a local burst buffer memory at a compute node, and an associated data mapping built index to maintain information related to the offsets into a shared file at which segments of the application data is to be stored in a parallel file system, and where in the buffer those segments are located. The node asynchronously transfers a data file containing the application data and the associated data mapping index to a file server for shared file storage. The data management system and method further accelerates shared file checkpointing in which a shared file, together with a map file that specifies how the shared file is to be distributed, is asynchronously transferred to local burst buffer memories at the nodes to accelerate reading of the shared file.

Gooding, Thomas↗