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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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At least 1,441 records · Page 80

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NASA NTRS1991

An improved distributed arithmetic architecture

Speed requirements have been, and will continue to be, a major consideration in the design of hardware to implement digital signal processing functions like digital filters and transforms like the DFT and DCT. The conventional approach is to increase speed by adding hardware and increasing chip area. The real challenge is to save chip area while still maintaining high speed performance. The approach we propose is based on the distributed arithmetic implementation (DA) of digital filters. The improvement is based on two observations. Firstly, a single memory element can replace several identical memory elements in a fully parallel DA implementation. Secondly, truncation or rounding may be introduced into the computation at strategic points without increasing error unduly. Both of these approaches can be used to attain area savings without impairing speed of operation.

Guo, X.↗

DOE OSTI2020

PLEXUS: A Pattern-Oriented Runtime System Architecture for Resilient Extreme-Scale High-Performance Computing Systems

For high-performance computing (HPC) system designers and users, meeting the myriad challenges of next-generation exascale supercomputing systems requires rethinking their approach to application and system software design. Among these challenges, providing resiliency and stability to the scientific applications in the presence of high fault rates requires new approaches to software architecture and design. As HPC systems become increasingly complex, they require intricate solutions for detection and mitigation for various modes of faults and errors that occur in these large-scale systems, as well as solutions for failure recovery. These resiliency solutions often interact with and affect other system properties, including application scalability, power and energy efficiency. Therefore, resilience solutions for HPC systems must be thoughtfully engineered and deployed.In previous work, we developed the concept of resilience design patterns, which consist of templated solutions based on well-established techniques for detection, mitigation and recovery. In this paper, we use these patterns as the foundation to propose new approaches to designing runtime systems for HPC systems. The instantiation of these patterns within a runtime system enables flexible and adaptable end-to-end resiliency solutions for HPC environments. The paper describes the architecture of the runtime system, named Plexus, and the strategies for dynamically composing and adapting pattern instances under runtime control. This runtime-based approach enables actively balancing the cost-benefit trade-off between performance overhead and protection coverage of the resilience solutions. Based on a prototype implementation of PLEXUS, we demonstrate the resiliency and performance gains achieved by the pattern-based runtime system for a parallel linear solver application.

Hukerikar, Saurabh↗

DOE OSTI2022

Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Report for the ASCR Workshop on the Management and Storage of Scientific Data

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Cost Effective Electrochromic Glazing Repositioning For Insulating Glass & Secondary Windows

This project addressed both technical and economic objectives toward significantly increasing the availability of ElectroChromic (EC) dynamic architectural glass at reduced cost. In contrast to the prevailing industry location of the EC device layer on the exterior light of an Insulating Glass (IG) unit, Project Objectives assessed the feasibility of a) repositioning the device layer midway between two IG panes to configure triple glazing, and b) independently framed to enable positioning to the building interior independent of the original glass configured as a dynamic secondary window. Parallel research complementing both EC-IG and EC-WinSert configurations addressed integral Photo Voltaic, and ceiling mounted “broadcast” power to circumvent the significant onsite electrical con tracting normally associated with the installation of commercial EC glazing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area

This work describes the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI) architecture for the co-simulation of distribution and transportation systems to evaluate EV charging impacts on electric distribution systems of a large metropolitan area and the surrounding rural regions with high fidelity. The current co-simulation is applied to Oakland and Alameda, California, and in future work will be extended to the full San Francisco Bay Area. It uses the HELICS co-simulation framework to enable parallel instances of vetted grid and transportation software programs to interact at every model timestep, allowing high-fidelity simulations at a large scale. This enables not only the impacts of electrified transportation systems across a larger interconnected collection of distribution feeders to be evaluated, but also the feedbacks between the two systems, such as through control systems, to be captured and compared. The findings are that with moderate passenger EV adoption rates, inverter controls combined with some distribution system hardware upgrades can maintain grid voltages within ANSI C.84 range A limits of 0.95 to 1.05 p.u. without smart charging. However, EV charging control may be required for higher levels of charging or to reduce grid upgrades, and this will be explored in future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1992

Domain specific software design for decision aiding

McDonnell Aircraft Company (MCAIR) is involved in many large multi-discipline design and development efforts of tactical aircraft. These involve a number of design disciplines that must be coordinated to produce an integrated design and a successful product. Our interpretation of a domain specific software design (DSSD) is that of a representation or framework that is specialized to support a limited problem domain. A DSSD is an abstract software design that is shaped by the problem characteristics. This parallels the theme of object-oriented analysis and design of letting the problem model directly drive the design. The DSSD concept extends the notion of software reusability to include representations or frameworks. It supports the entire software life cycle and specifically leads to improved prototyping capability, supports system integration, and promotes reuse of software designs and supporting frameworks. The example presented in this paper is the task network architecture or design which was developed for the MCAIR Pilot's Associate program. The task network concept supported both module development and system integration within the domain of operator decision aiding. It is presented as an instance where a software design exhibited many of the attributes associated with DSSD concept.

Keller, Kirby↗

NASA NTRS2016

Refined AFC-Enabled High-Lift System Integration Study

A prior trade study established the effectiveness of using Active Flow Control (AFC) for reducing the mechanical complexities associated with a modern high-lift system without sacrificing aerodynamic performance at low-speed flight conditions representative of takeoff and landing. The current technical report expands on this prior work in two ways: (1) a refined conventional high-lift system based on the NASA Common Research Model (CRM) is presented that is more representative of modern commercial transport aircraft in terms of stall characteristics and maximum Lift/Drag (L/D) ratios at takeoff and landing-approach flight conditions; and (2) the design trade space for AFC-enabled high-lift systems is expanded to explore a wider range of options for improving their efficiency. The refined conventional high-lift CRM (HL-CRM) concept features leading edge slats and slotted trailing edge flaps with Fowler motion. For the current AFC-enhanced high lift system trade study, the refined conventional high-lift system is simplified by substituting simply-hinged trailing edge flaps for the slotted single-element flaps with Fowler motion. The high-lift performance of these two high-lift CRM variants is established using Computational Fluid Dynamics (CFD) solutions to the Reynolds-Averaged Navier-Stokes (RANS) equations. These CFD assessments identify the high-lift performance that needs to be recovered through AFC to have the CRM variant with the lighter and mechanically simpler high-lift system match the performance of the conventional high-lift system. In parallel to the conventional high-lift concept development, parametric studies using CFD guided the development of an effective and efficient AFC-enabled simplified high-lift system. This included parametric trailing edge flap geometry studies addressing the effects of flap chord length and flap deflection. As for the AFC implementation, scaling effects (i.e., wind-tunnel versus full-scale flight conditions) are addressed, as are AFC architecture aspects such as AFC unit placement, number AFC units, operating pressures, mass flow rates, and steady versus unsteady AFC applications. These efforts led to the development of a novel traversing AFC actuation concept which is efficient in that it reduces the AFC mass flow requirements by as much as an order of magnitude compared to previous AFC technologies, and it is predicted to be effective in driving the aerodynamic performance of a mechanical simplified high-lift system close to that of the reference conventional high-lift system. Conceptual system integration studies were conducted for the AFC-enhanced high-lift concept applied to a NASA Environmentally Responsible Aircraft (ERA) reference configuration, the so-called ERA-0003 concept. The results from these design integration assessments identify overall system performance improvement opportunities over conventional high-lift systems that suggest the viability of further technology maturation efforts for AFC-enabled high lift flap systems. To that end, technical challenges are identified associated with the application of AFC-enabled high-lift systems to modern transonic commercial transports for future technology maturation efforts.

Hartwich, Peter M.↗

DOE OSTI2020

Impact of acid site speciation and spatial gradients on zeolite catalysis

This mini-review provides an overview of the current state of acid site control in zeolite catalysts, including methods of synthesis, advanced characterization, and measured effects of acid properties (speciation, concentration, proximity, siting, and spatial distribution) on a variety of commerciallyrelevant reactions. The diversity of aluminum species is described with respect to their location at specific sites in zeolite crystals as well as mesoscopic gradients in elemental composition that give rise to zoned or core-shell architectures. Challenges in the identification of acid siting are highlighted within the context of trial-and-error synthesis methods for a range of aluminosilicate frameworks, which hinder a priori design of catalysts, and limitations in techniques to suitably characterize active sites. Emphasis is also placed on knowledge gaps in zeolite catalysis wherein broad development of structure-performance relationships relies on future advancement of synthesis and analytical methods in parallel with atomistic modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

PythonFOAM: In-situ data analyses with OpenFOAM and Python

Here, we outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindings to data analysis libraries in Python. Double precision data in OpenFOAM is cast to a NumPy array using the NumPy C-API and Python modules may then be used for arbitrary data analysis and manipulation on flow-field information. We highlight how the proposed wrapper may be used for an in-situ online singular value decomposition (SVD) implemented in Python and accessed from the OpenFOAM solver PimpleFOAM. Here, 'in-situ' refers to a programming paradigm that allows for a concurrent computation of the data analysis on the same computational resources utilized for the partial differential equation solver. In addition, to demonstrate parallel deployments, we deploy a distributed SVD, which collects snapshot data across the ranks of a distributed simulation to compute the global left singular vectors. Crucially, both OpenFOAM and Python share the same message passing interface (MPI) communicator for this deployment which allows Python objects and functions to exchange NumPy arrays across ranks. Subsequently, we provide scaling assessments of this distributed SVD on multiple nodes of Intel Broadwell and KNL architectures for canonical test cases such as the large eddy simulations of a backward facing step and a channel flow at friction Reynolds number of 395. Finally, we demonstrate the deployment of a deep neural network for compressing the flow-field information using an autoencoder to demonstrate an ability to use state-of-the-art machine learning tools in the Python ecosystem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Heterogeneous techniques for rescaling energy deposits in the CMS Phase-2 endcap calorimeter

We present the porting to heterogeneous architectures of the algorithm used for applying linear transformations of raw energy deposits in the CMS High Granularity Calorimeter (HGCAL). This is the first heterogeneous algorithm to be fully integrated with HGCAL’s reconstruction chain. After introducing the latter and giving a brief description of the structural components of HGCAL relevant for this work, the role of the linear transformations in the calibration is reviewed. The many ways in which parallelization is achieved are described, and the successful validation of the heterogeneous algorithm is covered. Detailed performance measurements are presented, including throughput and execution time for both CPU and GPU algorithms, therefore establishing the corresponding speedup. We finally discuss the interplay between this work and the porting of other algorithms in the existing reconstruction chain, as well as integrating algorithms previously ported but not yet integrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Molecular underpinnings of ssDNA specificity by Rep HUH-endonucleases and implications for HUH-tag multiplexing and engineering

Abstract Replication initiator proteins (Reps) from the HUH-endonuclease superfamily process specific single-stranded DNA (ssDNA) sequences to initiate rolling circle/hairpin replication in viruses, such as crop ravaging geminiviruses and human disease causing parvoviruses. In biotechnology contexts, Reps are the basis for HUH-tag bioconjugation and a critical adeno-associated virus genome integration tool. We solved the first co-crystal structures of Reps complexed to ssDNA, revealing a key motif for conferring sequence specificity and for anchoring a bent DNA architecture. In combination, we developed a deep sequencing cleavage assay, termed HUH-seq, to interrogate subtleties in Rep specificity and demonstrate how differences can be exploited for multiplexed HUH-tagging. Together, our insights allowed engineering of only four amino acids in a Rep chimera to predictably alter sequence specificity. These results have important implications for modulating viral infections, developing Rep-based genomic integration tools, and enabling massively parallel HUH-tag barcoding and bioconjugation applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗

DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗

NASA NTRS1996

A Framework for Performing V&V within Reuse-Based Software Engineering

Verification and validation (V&V) is performed during application development for many systems, especially safety-critical and mission-critical systems. The V&V process is intended to discover errors, especially errors related to critical processing, as early as possible during the development process. Early discovery is important in order to minimize the cost and other impacts of correcting these errors. In order to provide early detection of errors, V&V is conducted in parallel with system development, often beginning with the concept phase. In reuse-based software engineering, however, decisions on the requirements, design and even implementation of domain assets can be made prior to beginning development of a specific system. In this case, V&V must be performed during domain engineering in order to have an impact on system development. This paper describes a framework for performing V&V within architecture-centric, reuse-based software engineering. This framework includes the activities of traditional application-level V&V, and extends these activities into domain engineering and into the transition between domain engineering and application engineering. The framework includes descriptions of the types of activities to be performed during each of the life-cycle phases, and provides motivation for the activities.

Addy, Edward A.↗

NASA NTRS2006

Using Existing NASA Satellites as Orbiting Testbeds to Accelerate Technology Infusion into Future Missions

One of the shared problems for new space mission developers is that it is extremely difficult to infuse new technology into new missions unless that technology has been flight validated. Therefore, the issue is that new technology is required to fly on a successful mission for flight validation. We have been experimenting with new technology on existing satellites by retrofitting primarily the flight software while the missions are on-orbit to experiment with new operations concepts. Experiments have been using Earth Observing 1 (EO-1), which is part of the New Millennium Program at NASA. EO-1 finished its prime mission one year after its launch on November 21,2000. From November 21,2001 until the present, EO-1 has been used in parallel with additional science data gathering to test out various sensor web concepts. Similarly, the Cosmic Hot Interstellar Plasma Spectrometer (CHIPS) satellite was also a one year mission flown by the University of Berkeley, sponsored by NASA and whose prime mission ended August 30,2005. Presently, CHIPS is being used to experiment with a seamless space to ground interface by installing Core Flight System (cFS), a "plug-and-play" architecture developed by the Flight Software Branch at NASA/GSFC on top of the existing space-to-ground Internet Protocol (IP) interface that CHIPS implemented. For example, one targeted experiment is to connect CHIPS to a rover via this interface and the Internet, and trigger autonomous actions on CHIPS, the rover or both. Thus far, having satellites to experiment with new concepts has turned out to be an inexpensive way to infuse new technology for future missions. Relevant experiences thus far and future plans will be discussed in this presentation.

Mandl, Daniel↗

DOE OSTI2026

Efficient Routing of Quantum LDPC Codes on Programmable 2D Toric Architectures

Quantum low-density parity-check codes are promising candidates towards scalable fault-tolerant quantum computation. Among these, bivariate bicycle (BB) codes offer superior encoding rates and large code distance compared to surface codes. However, their requirement on long-range stabilizer measurements poses significant challenges for implementation on realistic hardware with limited connectivity, such as superconducting circuit platforms. In this work, we introduce a novel hardware-software co-design that leverages a programmable communication network architecture to address these limitations. Our approach utilizes a 2D toric network of oscillators as a flexible communication fabric linking qubits at each site. Such architecture significantly reduces the number of long-range couplers required from O ( n ) to O (√ n ). Dual-rail qubits, along with native gates including Swap-Wait-Swap gates and beamsplitter SWAPs, ensure that long-range two-qubit gates can be executed with high fidelity and low latency. To further enhance performance, our qubit layout and routing algorithm utilize symmetries of the codes and enable maximum parallelism for long-range two-qubit gates, maintaining a low syndrome extraction cycle duration and scalability over the code length. We perform circuit-level simulation with realistic noise modeling based on experimental hardware parameters, observing an logical error rate per logical qubit per cycle of 3.06% for [[18,4,4]] BB code, 2.6× less than the existing experimental result. These findings provide a practical roadmap and identify key technological advancements needed to achieve low-overhead fault-tolerant quantum computing at scale.

Liu, Kun [Yale Univ., New Haven, CT (United States↗

NASA NTRS1985

Solution of partial differential equations on vector and parallel computers

The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed.

Ortega, J. M.↗