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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Two Serial Data to Pulse Code Modulation System Interfaces

Two pulse code modulation (PCM) system interfaces for asynchronous serial data are described. One interface is for global positioning system (GPS) data on the NASA Dryden Flight Research Center (DFRC) F-15B (McDonnell Douglas Corporation, St. Louis, Missouri) airplane, tail number 836 (F-15B/836). The other is for flight control computer data on the duPont Aerospace (La Jolla, California) DP-1, a 53-percent scale model of the duPont Aerospace DP-2.

Hamory, Phil↗
DOE OSTI2018

COOLR: A New System for Dynamic Thermal-Aware Computing

Extreme-scale systems need to walk a fine line between the amount of cooling they receive and the thermal-induced performance and reliability degradation they can sustain. System managers are extremely motivated to battle cooling-energy cost: the largest line item in their total operating cost. On the other hand, pushing the nodes to peak performance in tightened cooling regimes places the burden on the dynamic thermal management (DTM) to protect the hardware. DTM schemes throttle performance to relieve heat accumulation within nodes when cooling cannot mitigate the problem. These interventions prevent fatal failures but introduce inevitable performance degradations and variations. Such variations can have drastic consequences on the performance of extreme-scale systems. Furthermore, distribution of heat across different system nodes depends not only on the amount of workload. Even if a strictly equal share of computational load is assigned to all nodes, there are physical attributes and topological features of each system that inherently cause uneven accumulation of heat. These can cause one subcomponent to trigger DTM prematurely and penalize the overall system. Our goal in this project is to create a holistic thermal-aware view of the system, capturing both inherent physical attributes and dynamic system state. We developed COOLR, a dynamic system with the ability to evaluate the interplay between management of computation, data, power dissipation, and thermal state. Thereby, COOLR will achieve higher overall performance at the same energy and cooling cost. First, we performed a systematic power and thermal modeling and of high performance computing architectures. Analytical and empirical techniques were blended to generate a light-weight thermal model of target systems. Second, we developed a thermal-aware OS and runtime system. Policies for managing resources and schedules for processes has been designed to co-optimize the thermal state and performance of the system. The main outcome of our proposed thermal-aware dynamic computing system are scheduling and allocation of processes and memory accesses leading to an appropriately “skewed” distribution of activity across the system. The ultimate result of this new arrangement resulted in reduced occurrences of hotspots and reduced episodes of DTM intervention, leading to improvements in performance. The overall thermal characterization and modeling methodology (thermal instrumentation techniques, model building, and systematic model reduction) resulting from this project can be applied to systems beyond our immediate scope and will be applicable to future scaling. The thermal-aware dynamic computing paradigm will impact the efficiency of extreme-scale systems. The gained “thermal slack” can be given back to further tighten the cooling budget, benefiting the management cost of next generation extreme-scale systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

AAH Cage Out-Link and In-Link Antenna Characterization

This final report encapsulates the accomplishments of the third year of work on an Advanced Biotelemetry System (ABTS). Overall MU/ABTS project objectives are to provide a biotelemetry system that can collect data from and send commands to an implanted biotransceiver. This system will provide for studies of rodent development in space. The system must be capable of operating in a metal animal cage environment. An important goal is the development of a small, "smart", micropower, implantable biotransceiver with eight-channel data output and single channel command input capabilities with the flexibility for easy customization for a variety of physiologic investigations. The NASA Ames/Marquette University Joint Research work has been devoted to the system design of such a new state of the art biotelemetry system, having multiple physiologic inputs, and bi-directional data transfer capabilities. This work has provided a successful prototype system that connects, by two-way radio links, an addressable biotelemetry system that provides communication between an animal biotelemeter prototype and a personal computer. The operational features of the prototype system are: (1) two-way PCM communication with implanted biotelemeter; (2) microcontroller based biotelemeter; (3) out-link: wideband FSK (60 kBaud); (4) in-link: OOK (2.4 kbaud); (5) septum antenna arrays (In/Out-Links); and (6) personal computer data interface. The important requirement of this third year's work, to demonstrate two-way communication with transmit and receive antennas inside the metal animal cage, has been successfully accomplished. The advances discussed in this report demonstrate that the AAH cage antenna system can provide Out-link and In-link capability for the ABTS bi-directional telemetry system, and can serve as a benchmark for project status. Additions and enhancements to the most recent (April 1997) prototype cage and antenna have been implemented. The implementation, testing, and documentation was accomplished at the Biotelemetry Laboratory at Marquette University with Out-Link (slot) antenna design assistance was provided.

Jeutter, Dean C.↗
NASA NTRS1998

AAH Cage Out-Link and In-Link Antenna Characterization

This final report encapsulates the accomplishments of the third year of work on an Advanced Biotelemetry System (ABTS). Overall MU/ABTS project objectives are to provide a biotelemetry system that can collect data from and send commands to an implanted biotransceiver. This system will provide for studies of rodent development in space. The system must be capable of operating in a metal animal cage environment. An important goal is the development of a small, "smart", micropower, -channel data output and single channel command implantable biotransceiver with eight input capabilities with the flexibility for easy customization for a variety of physiologic investigations. The NASA Ames/Marquette University Joint Research work has been devoted to the system design of such a new state of the art biotelemetry system, having multiple physiologic inputs, and bi-directional data transfer capabilities. This work has provided a successful prototype system that connects, by two-way radio links, an addressable biotelemetry system that provides communication between an animal biotelemeter prototype and a personal computer. The operational features of the prototype system are listed below: Two-Way PCM Communication with Implanted Biotelemeter Microcontroller Based Biotelemeter Out-Link: Wideband FSK (60 kbaud) In-Link: OOK (2.4 kbaud) Septum Antenna Arrays (In/Out-Links) Personal Computer Data Interface The important requirement of this third year's work, to demonstrate two-way communication with transmit and receive antennas inside the metal animal cage, has been successfully accomplished. The advances discussed in this report demonstrate that the AAH cage antenna system can provide Out-link and In-link capability for the ABTS bi-directional telemetry system, and can serve as a benchmark for project status.

Jeutter, Dean C.↗
DOE OSTI2018

Cyber-Infrastructure for Data-Intensive Geospatial Computing

With the recent advent of heterogeneous High-performance Computing (HPC) to handle EO “Big Data” workloads, there is a need for a unified Cyber-infrastructure (CI) platform that can bridge the best of many HPC worlds. In this chapter, we discuss such a CI platform being developed at Geographic Information Science and Technology (GIST) group using novel and innovative techniques, and emerging technologies that are scalable to large-scale supercomputers. The CI platform utilizes a wide variety of computing such as GPGPU, distributed, real-time and cluster computing, which are being brought together architecturally to enable data-driven analysis, scientific understanding of earth system models, and research collaboration. This development addresses the need for close integration of EO and other geospatial information in the face of growing volumes of the data, and facilitates spatio-temporal analysis of disparate and dynamic data streams. Horizontal scalability and linear throughput are supported in the heart of the platform itself. It is being used to support very broad application areas, ranging from high-resolution settlement mapping, national bioenergy infrastructure to urban information and mobility systems. The platform provides spatio-temporal decision support capabilities in planning, policy and operational missions for US federal agencies. Also, the platform is designed to be functionally and technologically sustainable for continued support of the US energy and environment mission for the coming decades.

Karthik, Rajasekar↗
DOE OSTI2021

Deep potential generation scheme and simulation protocol for the Li 10 GeP 2 S 12 -type superionic conductors

We report solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here, we implement the deep potential generator to set up an efficient protocol to automatically generate interatomic potentials for Li 10 GeP 2 S 12 -type solid-state electrolyte materials (Li 10 GeP 2 S 12 , Li 10 SiP 2 S 12 , and Li 10 SnP 2 S 12 ). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of the diffusion process to a wide temperature range (300 K–1000 K) and systems with large size (~1000 atoms). Important technical aspects such as the statistical error and size effect are carefully investigated, and benchmark tests including the effect of density functional, thermal expansion, and configurational disorder are performed. The computed data that consider these factors agree well with the experimental results, and we find that the three structures show different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Energy-efficient scientific computing using chemical reservoirs

The rapid growth of computing demands driven by scientific computing, data analytics, and artificial intelligence (AI) advancements has exposed the limitations of traditional digital processing systems. These systems are nearing physical energy barriers, making significant gains in energy efficiency increasingly unattainable. As we advance toward post-exascale computing, disruptive approaches are critical to overcoming these limitations. Among emerging analog solutions, biochemical computing offers a transformative path for achieving orders-of-magnitude improvements in energy efficiency. By leveraging the natural optimization capabilities of chemical reaction networks (CRNs), biochemical systems have the potential to meet high-performance computing needs through natural scalability. However, numerous challenges remain, including theoretical limitations in mapping computational problems to CRNs and practical barriers in implementing biochemical computing devices. In this paper, we present a framework for chemical computation using biochemical systems and introduce key components of our approach for energy-efficient scientific computing. We showcase the feasibility of this framework by solving a system of ordinary differential equations by emulating a chemical reservoir device, demonstrating its potential for addressing modern computing challenges. This work lays a foundational step toward harnessing the computational power of chemistry to design energy-efficient, scalable, high-performance next-generation computing systems.

Johnson, Connah G. M. [Pacific Northwest National ↗
NASA NTRS1985

Image processing of aerodynamic data

The use of digital image processing techniques in analyzing and evaluating aerodynamic data is discussed. An image processing system that converts images derived from digital data or from transparent film into black and white, full color, or false color pictures is described. Applications to black and white images of a model wing with a NACA 64-210 section in simulated rain and to computed low properties for transonic flow past a NACA 0012 airfoil are presented. Image processing techniques are used to visualize the variations of water film thicknesses on the wing model and to illustrate the contours of computed Mach numbers for the flow past the NACA 0012 airfoil. Since the computed data for the NACA 0012 airfoil are available only at discrete spatial locations, an interpolation method is used to provide values of the Mach number over the entire field.

Faulcon, N. D.↗
NASA NTRS2019

Additional Findings from the Common Research Model Natural Laminar Flow Wind Tunnel Test

An experimental investigation of the Common Research Model with Natural Laminar Flow (CRM-NLF) took place in the National Transonic Facility (NTF) at the NASA Langley Research Center in 2018. The 5.2% scale semispan model was designed using a new natural laminar flow design method, Crossflow Attenuated NLF (CATNLF). CATNLF enables laminar flow on typical transport wings with high sweep and Reynolds number by reshaping the wing airfoils to obtain specific pressure distribution characteristics that control the crossflow growth near the leading edge. The CATNLF method also addresses Tollmien- Schlichting transition, attachment line transition, and Görtler vortices. During the wind tunnel test, data were acquired to address three primary test objectives: validate the CATNLF design method, characterize the NTF laminar flow testing capabilities, and establish best practices for laminar flow wind tunnel testing. The present paper provides both experimental and computational data to understand the CRM-NLF laminar flow characteristics, as well as address the three primary test objectives. The effects of angle of attack and Reynolds number on the CRM-NLF laminar flow extent are studied, and the dominant transition mechanism is evaluated at a variety of test conditions. Critical N-factors are calculated for the NTF environment, and a discussion on best practices for laminar flow wind tunnel testing is provided. The CRM-NLF in the NTF provided initial confirmation of the ability of the CATNLF method to suppress crossflow growth and enable significant extents of laminar flow on transport wings with high sweep and Reynolds numbers.

Lynde, Michelle N.↗
DOE OSTI2026

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems

The unprecedented growth in artificial intelligence (AI) workloads, recently dominated by large language models (LLMs) and vision-language models (VLMs), has intensified power and cooling demands in data centers. This study benchmarks LLMs and VLMs on two HGX nodes, each with 8× NVIDIA H100 graphics processing units (GPUs), using liquid and air cooling. Leveraging GPU Burn, Weights & Biases, and IPMItool, we collect detailed thermal, power, and computation data. Results show that the liquid-cooled systems maintain GPU temperatures between 41-50$^\circ$C, while the air-cooled counterparts fluctuate between 54-72$^\circ$C under load. This thermal stability of liquid-cooled systems yields 17% higher performance (54 TFLOPs/ GPU vs. 46 TFLOPs/GPU), performance-per-watt, reduced energy overhead, and greater system efficiency than the air-cooled counterparts. These findings underscore the energy and sustainability benefits of liquid cooling, offering a compelling path forward for hyperscale data centers seeking to optimize AI infrastructure. https://github.com/iscaas/Cooling-Matters.

Latif, Imran↗
NASA NTRS1998

Two-Dimensional High-Lift Aerodynamic Optimization Using Neural Networks

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. The 'pressure difference rule,' which states that the maximum lift condition corresponds to a certain pressure difference between the peak suction pressure and the pressure at the trailing edge of the element, was applied and verified with experimental observations for this configuration. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural nets were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 44% compared with traditional gradient-based optimization procedures for multiple optimization runs.

Greenman, Roxana M.↗
NASA NTRS1998

High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.

Greenman, Roxana M.↗
DOE OSTI2024

HPC-FAIR: A Framework Managing Data and AI Models for Analyzing and Optimizing Scientific Applications

The increasing reliance on machine learning (ML) to analyze and optimize large-scale scientific applications on supercomputers faces a significant bottleneck: the lack of readily available, high-quality training datasets and the difficulty in reusing existing AI models. This project was motivated by the urgent need to address the “FAIR” principles (Findability, Accessibility, Interoperability, Reusability) for both training datasets and AI models in the high-performance computing (HPC) domain. The project developed HPC-FAIR, a high-performance computing data management framework designed to centralize HPC-related datasets and AI models within a unified hub. To ensure interoperability, the framework established a standardized representation and vocabulary (ontology) for both data and models. HPC-FAIR also implemented automated workflows to streamline data processing, model access, and benchmarking. Additionally, the project focused on optimizing data harnessing efficiency through advanced techniques like deep reuse and compression-based analytics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Numerical investigation of an internal layer in turbulent flow over a curved hill

The development of an internal layer in a turbulent boundary layer flow over a curved hill is investigated numerically. The turbulence field of the boundary layer flow over the curved hill is compared with that of a turbulent flow over a symmetric airfoil (which has the same geometry as the curved hill except that the leading and trailing edge plates were removed) to study the influence of the strongly curved surface on the turbulence field. The turbulent flow equations are solved by a control-volume based finite difference method. The turbulence is described by a multiple-time-scale turbulence model supplemented with a near-wall turbulence model. Computational results for the mean flow field (pressure distributions on the walls, wall shearing stresses and mean velocity profiles), the turbulence structure (Reynolds stress and turbulent kinetic energy profiles), and the integral parameters (displacement and momentum thicknesses) compared favorably with the measured data. Computational results show that the internal layer is a strong turbulence field which is developed beneath the external boundary layer and is located very close to the wall. Development of the internal layer was more obviously observed in the Reynolds stress profiles and in the turbulent kinetic energy profiles than in the mean velocity profiles. In this regard, the internal layers is significantly different from wall-bounded simple shear layers in which the mean velocity profile characterizes the boundary layer most distinguishably. Development of such an internal layer, characterized by an intense turbulence field, is attributed to the enormous mean flow strain rate caused by the streamline curvature and the strong pressure gradient. In the turbulent flow over the curved hill, the internal layer begin to form near the forward corner of the hill, merges with the external boundary layer, and develops into a new fully turbulent boundary layer as the fluid flows in the downstream direction. For the flow over the symmetric airfoil, the boundary layer began to form from almost the same location as that of the curved hill, grew in its strength, and formed a fully turbulent boundary layer from mid-part of the airfoil and in the downstream region. Computational results also show that the detailed turbulence structure in the region very close to the wall of the curved hill is almost the same as that of the airfoil in most of the curved regions except near the leading edge. Thus the internal layer of the curved hill and the boundary layer of the airfoil were also almost the same. Development of the wall shearing stress and separation of the boundary layer at the rear end of the curved hill mostly depends on the internal layer and is only slightly influenced by the external boundary layer flow.

Kim, S-W.↗
NASA NTRS2011

Combustor Computations for CO2-Neutral Aviation

Knowing the pure component C(sub p)(sup 0) or mixture C(sub p) (sup 0) as computed by a flexible code such as NIST-STRAPP or McBride-Gordon, one can, within reasonable accuracy, determine the thermophysical properties necessary to predict the combustion characteristics when there are no tabulated or computed data for those fluid mixtures 3or limited results for lower temperatures. (Note: C(sub p) (sup 0) is molar heat capacity at constant pressure.) The method can be used in the determination of synthetic and biological fuels and blends using the NIST code to compute the C(sub p) (sup 0) of the mixture. In this work, the values of the heat capacity were set at zero pressure, which provided the basis for integration to determine the required combustor properties from the injector to the combustor exit plane. The McBride-Gordon code was used to determine the heat capacity at zero pressure over a wide range of temperatures (room to 6,000 K). The selected fluids were Jet-A, 224TMP (octane), and C12. It was found that each heat capacity loci were form-similar. It was then determined that the results [near 400 to 3,000 K] could be represented to within acceptable engineering accuracy with the simplified equation C(sub p) (sup 0) = A/T + B, where A and B are fluid-dependent constants and T is temperature (K).

Hendricks, Robert C.↗