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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 379 records · Page 21

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NASA NTRS1988

A visual programming environment for the Navier-Stokes computer

The Navier-Stokes computer is a high-performance, reconfigurable, pipelined machine designed to solve large computational fluid dynamics problems. Due to the complexity of the architecture, development of effective, high-level language compilers for the system appears to be a very difficult task. Consequently, a visual programming methodology has been developed which allows users to program the system at an architectural level by constructing diagrams of the pipeline configuration. These schematic program representations can then be checked for validity and automatically translated into machine code. The visual environment is illustrated by using a prototype graphical editor to program an example problem.

Tomboulian, Sherryl↗

NASA NTRSDate not supplied

Turbine Electrified Energy Management with Model Predictive Control

Affordability, sustainability, and efficiency are primary motivators driving the future of NASA aeronautics research. These factors are realized, in part, through the development and implementation of new technologies and strategies enabling efficient, affordable, and safe hybrid-electric aircraft. Research supporting electrified aircraft propulsion control systems exemplifies such new methodologies, offering varied opportunities to integrate electric machines with gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures seek to introduce energy storage and exploit electrical power system components to assist gas-based system components. Capitalizing on the electric machines in hybridized engines, Turbine Electrified Energy Management (TEEM) is a control approach that enhances transient operability to improve overall propulsion and vehicle efficiency by injecting or extracting power from engine shafts. Traditionally implemented with proportional-integral (PI) control, this study expands the application of TEEM by presenting model predictive control (MPC) schemes to execute the TEEM concept. Via cost function design and constraint selection, the transient operability goals for TEEM are considered in the controller designs. The proposed MPCs are simulated on a nonlinear turbofan engine model at two environmental conditions, with comparisons drawn to a baseline PI. Performance is evaluated using compressor maps and two TEEM-specific metrics: transient stack usage and transient excursion integral. Simulation results reveal the developed schemes perform comparably to the benchmark controller and can be implemented in two distinct configurations. Potential modifications for future investigations include cost function measures that optimize energy use, additional performance effectiveness measures, and battery storage capabilities.

Elyse D. Hill↗

NASA NTRS2002

Memory-Intensive Benchmarks: IRAM vs. Cache-Based Machines

The increasing gap between processor and memory performance has lead to new architectural models for memory-intensive applications. In this paper, we explore the performance of a set of memory-intensive benchmarks and use them to compare the performance of conventional cache-based microprocessors to a mixed logic and DRAM processor called VIRAM. The benchmarks are based on problem statements, rather than specific implementations, and in each case we explore the fundamental hardware requirements of the problem, as well as alternative algorithms and data structures that can help expose fine-grained parallelism or simplify memory access patterns. The benchmarks are characterized by their memory access patterns, their basic control structures, and the ratio of computation to memory operation.

Biswas, Rupak↗

NASA NTRS2017

Generating Real-Time Robotics Control Software from SysML

In this paper, we outline an approach for autogenerating real-time robotics control code from hierarchical state machines and hardware configurations encoded in Systems Modeling Language (SysML). We propose a software architecture that provides an abstract SysML layer with access to device state information and a set of primitive device commands, such as move actuator and release brake, allowing a user to build up a complete functional state machine directly in SysML. The SysML diagram is then exported to a standard SCXML file format and subsequently used to auto-generate hardware control code. Once this architecture is in place, the only explicit code elements that need to be written are the primitive device commands, which can be easily unit tested and reused across different systems. The motivation for this work was the need for a test bed that enables the rapid prototyping of mechanisms and control algorithms for a spacecraft that could ultimately be used for preparing Martian rock samples for their return to Earth. To this end, our software system was also designed to allow for the run-time specification of the hardware layout in SysML, with the hardware-level control functions kept agnostic to the specific parameters or communication bus of any particular device. Further, we outline a system for specifying both the state machine and hardware configuration in the MagicDraw IDE in such a way that the system can be simulated before any code is generated. The resultant software system is easy to debug, understand, and allows users to choose how much information is encoded as a visual or text-based representation.

Godart, Peter↗

NASA NTRS1992

Future directions in computing and CFD

In recent years CFD on massively parallel machines has become a reality. This paper summarizes some recent trends both in high performance computing, and in CFD using parallel machines. The long term computational requirements for accomplishing some of the large scale problems in computational aerosciences, and current hardware and architecture trends are discussed. Performance results obtained from the implementation of some CFD applications on the Connection Machine CM-2 and the Intel iPSC/860 at NASA Ames Research Center are presented. It is argued that only massively parallel machines will be able to meet these grand challenge requirements.

Bailey, F. R.↗

NASA NTRS2006

Distributed Antenna-Coupled Transition Edge Sensors

We describe progress toward realizing a new architecture for focal plane arrays for the Submillimeter and Far- Infrared (FIR) bands. This architecture is based on a detector design utilizing distributed hot{electron transition edge sensors (TES) coupled to slot antenna elements. Arrays utilizing this type of detector can be considerably easier to manufacture than membrane-isolated TES arrays, because the need for micro-machining is eliminated. We present background and rationale for this new array architecture and details of a new antenna design for an imaging polarimeter, which yields greater bandwidth than past designs. In addition, we describe a cryogenic facility for testing these arrays.

bolometer↗

DOE OSTI2023

Determining the twist angle of stacked MoS 2 layers using machine learning-assisted low-frequency interlayer Raman fingerprints

The investigation of twisted stacked few-layer MoS 2 has revealed novel electronic, optical, and vibrational properties over an extended period. For the successful integration of twisted stacked few-layer MoS 2 into a wide range of applications, it is crucial to employ a noninvasive, versatile technique for characterizing the layered architecture of these complex structures. Here, in this work, we introduce a machine learning-assisted low-frequency Raman spectroscopy method to characterize the twist angle of few-layer stacked MoS 2 samples. A feedforward neural network (FNN) is utilized to analyze the low-frequency breathing mode as a function of the twist angle. Moreover, using finite difference method (FDM) and density functional theory (DFT) calculations, we show that the low-frequency Raman spectra of MoS 2 are mainly influenced by the effect of the nearest and second nearest layers. A new improved linear chain model (TA-LCM) with taking the twist angle into the consideration is developed to understand the interlayer breathing modes of stacked few-layer MoS 2 . This approach can be extended to other 2D materials systems and provides an intelligent way to investigate naturally stacked and twisted interlayer interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation

Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such large architectures lead to increased accuracy, this is usually accompanied by a larger increase in computation and memory requirements during inference. While this is a non-issue in traditional machine learning (ML) pipelines, the recent confluence of machine learning and fields like the Internet of Things (IoT) has rendered such large architectures infeasible for execution in low-resource settings. For some datasets, large monolithic pipelines may be overkill for simpler inputs. To address this problem, previous efforts have proposed decision cascades where inputs are passed through models of increasing complexity until the desired performance is achieved. However, we argue that cascaded prediction leads to sub-optimal throughput and increased computational cost due to wasteful intermediate computations. To address this, we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a non-cascading, cost-aware learning pipeline as an efficient alternative to cascaded decision architectures. Through experimental evaluation on both real-world and standard datasets, we demonstrate that PaSeR achieves better accuracy while minimizing computational cost relative to cascaded models. Further, we introduce a new metric IoU/GigaFlop to evaluate the balance between cost and performance. On the real-world task of battery material phase segmentation, PaSeR yields 179% improvement over SOTA MatPhase model and a 196% improvement over IDK Cascades under the IoU/GigaFlop metric. We also demonstrate PaSeR’s adaptability to complementary models trained on a noisy MNIST dataset, where it outperforms all baselines on IoU/GigaFlop by an average of 44%.

Srikshan, Bharat↗

NASA NTRSDate not supplied

Communication Network Awareness Machine System Phase I Development: The Intelligent Party-Line Schema

As NextGen continues toward the full implementation of a Net-Centric Architecture (N-CA)it will inherently provide a continuous increase to the Three-Vs components (Volume, Velocity, and Variety) of big data . This will create an insurmountable environment for direct-action aviation personnel (DAAP)as the DAAP’s natural abilities to manage and process data into actionable information will be overmatched by the Three-Vs. Therefore, conducting operations within a N-CA requires that new tools and applications be researched and developed to aid the DAAP’s ability to understand and manage data, mitigate non-normals, create contingency plans and actions. This paper will describe a research area at NASA Langley Research Center known as the Intelligent Party-Line (IPL).

Intelligent Party-Line↗

DOE OSTI2025

Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions

Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO 2 -to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO 2 .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys

Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. Furthermore, we demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.

density functional theory↗

NASA NTRS2019

Evaluation of Electrical System Requirements for Implementing Turbine Electrified Energy Management

Turbine Electrified Energy Management (TEEM) is a concept concerned with the management of energy in an electrified propulsion system. The management of energy in the hybrid-electric architecture has potential to benefit the turbomachinery and the aircraft it powers. The concept is particularly useful for improving operability during transient operation and could be leveraged to design a better performing engine. The concept utilizes electric machines coupled to the engine shafts and an electric power distribution system that includes energy storage. A controller is used to decide when and how energy is moved around the electrified propulsion system, particularly when considering energy conversion between mechanical and electrical forms. Prior work has shown that the electric machines can be used to supply/or extract supplemental power to/from the engine shafts to improve their operability and achieve or enable propulsion efficiency and performance benefits. However, the previous studies did not consider the practical constraints of the electrical machines and energy storage devices that are required for implementing the TEEM system architecture concept. This paper presents an integrated engine and electrical system model that is used to evaluate the electrical system requirements. The model captures the physics of the conceptual, Advanced Geared Turbofan 30,000lbf (AGTF30) engine, which features advanced technologies such as a compact gas turbine and a variable area fan nozzle. For this work, the engine is augmented with electrical system components that allow for the implementation of the TEEM concept. The evaluation presented suggests the potential of the TEEM concept to provide performance benefits for a turbofan engine.

electrified↗

NASA NTRS2019

Evaluation of Electrical System Requirements for Implementing Turbine Electrified Energy Management

hybrid electric propulsion↗

DOE OSTI2026

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗

DOE OSTI2021

Did the GPU obfuscate the load imbalance in my MPI simulation?

The current proliferation of GPU-based HPC systems necessitates a method for assessing the performance of simulations on heterogeneous machines. The addition of GPUs to a system adds multiple hierarchical levels of parallelism to the node architecture. In this paper, we demonstrate that the traditional load imbalance metric is insufficient for capturing the load imbalance on GPU-based machines, since it treats the GPU as a monolithic entity and ignores the internal parallelism. We propose a new hierarchical metric that improves the correlation of measured performance and application workload by up to 20.61%. Using our metric for determining application load instead of the traditional metric as the input for the load balancing algorithm reduces the residual load imbalance by up to 4× in our application.

Eberius, David↗

DOE OSTI2020

Generic Spiking Architecture (GenSA)

Neuromorphic devices are a rapidly growing area of interest in industry, with machines in production by IBM and Intel, among others. These devices promise to reduce size, weight and power (SWaP) costs while increasing resilience and facilitating high- performance computing (HPC). Each device will favor some set of algorithms, but this relationship has not been thoroughly studied. The field of neuromorphic computing is so new that existing devices were designed with merely estimated use-cases in mind. To better understand the fit between neuromorphic algorithms and machines, a simulated machine can be configured to any point in the design space. This will identify better choices of devices, and perhaps guide the market in new directions. The design of a generic spiking machine generalizes existing examples while also looking forward to devices that haven't been built yet. Each parameter is specified, along the approach/mechanism by which the relevant component is implemented in the simulator.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗

NASA NTRS1988

System architectures for PROLOG execution

The principal features of PROLOG are reviewed with particular reference to the use of the language in the development of expert systems. Attention is given to the language syntax, data structures and recursion, constructs that alter control, predicates that modify the database, predicates for input and output, and metalogical predicates. The parts of the Warren Abstract Machine are then described, and it is shown how it executes programs. Parallel architectures proposed for expert systems are examined, and the principal varieties of parallelism available in PROLOG and examples of architectures exploiting them are discussed.

Gooley, Markian M.↗