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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Establishing a Robotic, LEO-to-GEO Satellite Servicing Infrastructure as an Economic Foundation for Exploration

The strategy for accomplishing civilian exploration goals and objectives is in the process of a fundamental shift towards a potential new approach called Flexible Path. This paper suggests that a government-industry or public-private partnership in the commercial development of low Earth orbit to geostationary orbit (LEO-to-GEO (LTG)) space, following or in parallel with the commercialization of Earth-to-LEO and International Space Station (ISS) operations, could serve as a necessary, logical step that can be incorporated into the flexible path approach. A LTG satellite-servicing infrastructure and architecture concept is discussed within this new strategic context. The concept consists of a space harbor that serves as a transport facility for a fleet of specialized, fully- or semi-autonomous robotic servicing spacecraft. The baseline, conceptual system architecture is composed of a space harbor equipped with specialized servicer spacecraft; a satellite command, communication, and control system; a parts station; a fuel station or depot; and a fuel/parts replenishment transport. The commercial servicer fleet would consist of several types of spacecraft, each designed with specialized robotic manipulation subsystems to provide services such as refueling, upgrade, repair, inspection, relocation, and removal. The space harbor is conceptualized as an ISS-type, octagonal truss structure equipped with radiation tolerant subsystems. This space harbor would be primarily capable of serving as an operational platform for various commercially owned and operated servicer spacecraft positioned and docked symmetrically on four of the eight sides. Several aspects of this concept are discussed, such as: system-level feasibility in terms of ISS-truss-type infrastructure and subsystems emplacement and maintenance between LEO and GEO; infrastructure components assembly in LEO, derived from ISS assembly experience, and transfer to various higher orbital locations; the evolving Earth-to-orbit (ETO) capability to deliver humans and cargo to LEO for assembly purposes; system architectural definition, optimal orbital parameters, mass estimations, delta velocity ( V) estimations, power and propulsion options, and assessments of various critical technologies. Large-scale, robotic, LTG satellite servicing is considered as an essential economic pre-condition and next parallel or sequential step on the road toward exploration beyond LEO. Such a step might produce the necessary pre-requisite economic value that can be used by future decision makers to justify further investment in exploration beyond LEO.

Horsham, Gary A. P.↗
NASA NTRS1994

Large-Scale Numerical Simulations of Human Motion

This paper examines the feasibility of using massively-parallel and vector-processing supercomputers to solve large-scale optimal control problems for human movement. Specifically, we compare the computational expense of determining the optimal controls for the single support phase of walking using a conventional serial machine (a Silicon Graphics Personal Iris 4D25 workstation), a MIMD parallel machine (an Intel iPSC/860 comprising 128 processors), and a parallel-vector-processing machine (a Cray Y-MP 8/864). With the human body modeled as a 14 degree-of-freedom linkage actuated by 46 musculotendinous units, computation of the optimal controls for walking could take up to 3 months of CPU time on the Iris. Both the Cray Y-MP and the Intel iPSC/860 are able to reduce this time to practical levels. The optimal control solution for walking can be found with about 77 hours of CPU time on the Cray, and with about 88 hours of CPU time on the Intel. Although the overall speeds of the Cray and the Intel were found to be similar, the unique capabilities of each machine are best suited to different parts of the optimal control algorithm used. The Intel performed best in the calculation of the derivatives of the performance criterion and the constraints. In contrast, the Cray performed best during parameter optimization of the controls. These results suggest that the ideal computer architecture for solving very large-scale optimal control problems is a hybrid system in which a vector-processing machine is integrated into the communication network of a MIMD parallel machine.

Anderson, Frank C.↗
DOE OSTI2026

Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

09 BIOMASS FUELS↗
DOE OSTI2017

Experiments and Analyses of Data Transfers Over Wide-Area Dedicated Connections

Dedicated wide-area network connections are increasingly employed in high-performance computing and big data scenarios. One might expect the performance and dynamics of data transfers over such connections to be easy to analyze due to the lack of competing traffic. However, non-linear transport dynamics and end-system complexities (e.g., multi-core hosts and distributed filesystems) can in fact make analysis surprisingly challenging. We present extensive measurements of memory-to-memory and disk-to-disk file transfers over 10 Gbps physical and emulated connections with 0–366 ms round trip times (RTTs). For memory-to-memory transfers, profiles of both TCP and UDT throughput as a function of RTT show concave and convex regions; large buffer sizes and more parallel flows lead to wider concave regions, which are highly desirable. TCP and UDT both also display complex throughput dynamics, as indicated by their Poincare maps and Lyapunov exponents. For disk-to-disk transfers, we determine that high throughput can be achieved via a combination of parallel I/O threads, parallel network threads, and direct I/O mode. Our measurements also show that Lustre filesystems can be mounted over long-haul connections using LNet routers, although challenges remain in jointly optimizing file I/O and transport method parameters to achieve peak throughput.

Rao, Nageswara S.↗
NASA NTRSDate not supplied

Advanced Parametric Lobe Mixer Concepts for Internally Mixed Nozzles

A continuation of efforts to optimize a dual-stream lobe mixer for supersonic commercial flight are detailed, adding scarfing and scalloping features to a baseline lobe mixer previously optimized within a baseline parametric space. These "advanced features" are parameterized by four quantities: scarfing angle 𝜁, scallop height ℎ 𝑠 , scallop depth 𝑑 𝑠 , and scallop conic focal parameter 𝜌 𝑠 . Two baseline mixers, one parallel and one vortical, form the basis for an eighteen-case study to investigate the effects of each parameter in single-axis fashion. Aerodynamic performance is evaluated through velocity coefficient 𝐶 𝑉 . Moderate performance increases are seen with increasing scallop height and depth on the parallel mixer, and with increasing scarfing angle on the vortical mixer. Flowfield analysis is presented through visualization of axial and cross-stream vorticity development through the duct, as well as density variation. A correlation between axial vorticity and density variation decay was found, with earlier introduction of axial vorticity found to result in improved aerodynamic performance and potential noise suppression.

Lobe Mixer↗
DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1976

Maximum likelihood identifier refinements for F-8C adaptive control

Design refinement is described for baseline F-8C adaptive flight test control laws which consist of simplified quadratic-optimal model-following control structures in the pitch and lateral directional axes. The variable gains of both axes are adjusted adaptively by an explicit on-line maximum likelihood parameter identifier. The identifier, consisting of five parallel computing channels which evaluate the likelihood functions at discrete points in parameter space, operates in pitch only, using pitch rate, normal acceleration, and elevator position measurements.

Stein, G.↗
DOE OSTI2018

Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems

Parallel I/O hardware and software infrastructure is a key contributor to performance variability for applications running on large-scale HPC systems. This variability confounds efforts to predict application performance for characterization, modeling, optimization, and job scheduling. We propose a modeling approach that improves predictive ability by explicitly treating the variability and by leveraging the sensitivity of application parameters on performance to group applications with similar characteristics. We develop a Gaussian process-based machine learning algorithm to model I/O performance and its variability as a function of application and file system characteristics. We demonstrate the effectiveness of the proposed approach using data collected from the Edison system at the National Energy Research Scientific Computing Center. The results show that the proposed sensitivity-based models are better at prediction when compared with application-partitioned or unpartitioned models. We highlight modeling techniques that are robust to the outliers that can occur in production parallel file systems. Using the developed metrics and modeling approach, we provide insights into the file system metrics that have a significant impact on I/O performance.

I/O performance variability↗
DOE OSTI2021

HYPPO v1.0.0

HYPPO is a hyperparameter optimization (HPO) software that uses surrogate modeling and uncertainty quantification to provide reliable neural network architectures. Similar state-of-the-art HPO technologies do not use such features and mostly perform random searches to explore the parameter space, which can easily become computationally expensive. Using HYPPO, the user is able to reduce by an order of magnitude the number of evaluations necessary to identify the most optimal region in the hyperparameter space. Finally, using asynchronous nested parallelism, we are able to decrease by two orders of magnitude the amount of time required to complete the HPO process.

Dumont, Vincent↗
DOE OSTI2024

High Throughput Source-less Plasma Deposition of Structured Silicon Anodes for Lithium-Ion Batteries

Amprius developed a manufacturing solution for silicon nanowire anode that relies on an inexpensive, high throughput, and high gas precursor utilization plasma deposition method that uses the anode foils as electrodes for plasma generation. The capacitively couple plasma (CCP) method is used in semiconductor and photovoltaic industry and Amprius modified existing high throughput equipment to use anode foils and to deposit amorphous silicon. The equipment was installed ahead of the program at Amprius site. The rest of the tasks included foil handling and process development. The equipment passed site acceptance tests (SAT) and the process parameter mapping was completed, indicating that the target process window limits produce output materials at the rate and with yield and specifications that meet the manufacturing target criteria. Amprius has hired supporting personnel to optimize processes and run the equipment. A parallel task verified the baseline performance of the silicon anode material, to be used as reference for the new manufacturing method.

25 ENERGY STORAGE↗
NASA NTRS1982

Conversion and matched filter approximations for serial minimum-shift keyed modulation

Serial minimum-shift keyed (MSK) modulation, a technique for generating and detecting MSK using series filtering, is ideally suited for high data rate applications provided the required conversion and matched filters can be closely approximated. Low-pass implementations of these filters as parallel inphase- and quadrature-mixer structures are characterized in this paper in terms of signal-to-noise ratio (SNR) degradation from ideal and envelope deviation. Several hardware implementation techniques utilizing microwave devices or lumped elements are presented. Optimization of parameter values results in realizations whose SNR degradation is less than 0.5 dB at error probabilities of .000001.

Ziemer, R. E.↗
DOE OSTI2019

An Innovative Nb3Sn Film Approach and Its Potential for SRF Applications

A novel electro-chemical technique to produce Nb3Sn films on Nb substrates was developed and optimized at Fermilab. The Nb3Sn phase is obtained in a two-electrode cell, by electrodeposition from aqueous solutions of Sn layers and Cu intermediate layers onto Nb substrates. Subsequent thermal treatments in inert atmosphere are realized at a maximum temperature of 700°C to obtain the Nb3Sn superconducting phase. Several superconduct-ing Nb3Sn films were obtained on Nb substrates by study-ing and optimizing most parameters of the electro-plating process. Samples were characterized at Fermilab, NIMS, KEK and JLAB, including EPMA analyses, DC and in-ductive tests of critical temperature Tc0, and lower critical field Hc1(4.2 K) by SQUID. In parallel to sample devel-opment and fabrication at FNAL, at JLAB and KEK effort was put into etching and electro-polishing techniques adequate to remove the Cu and bronze phases from the samples’ outer surface. This is necessary prior to meas-urements at JLAB of the surface impedance of flat sam-ples in a setup that make use of an RF host cavity.

Barzi, E. Z.↗
DOE OSTI2023

Investigation on Tuning Power–Frequency Droop for Improved Grid–Forming Inverter and Synchronous Generator Transient Load Sharing

Poor transient load sharing has been observed during the parallel operation of synchronous generators (SGs) and droop-controlled grid-forming (GFM) inverter-based resources (IBRs) in islanded microgrids. This can result in overcurrent protections, causing the inverters to trip, which leads to the collapse of voltage and frequency. In this article, small-signal analysis supported by electromagnetic transients (EMTs) simulation is performed on a detailed model of a microgrid containing IBRs and an SG to identify underdamped modes and their sensitivities to the power–frequency (P-F) droop parameter. Further EMT simulation of a more complex microgrid model with multiple GFM IBRs and SGs is performed to determine whether trends identified from the modal analysis extend to cases where the number of SGs in operation may vary. The objective is to investigate the effectiveness of tuning this parameter to reduce the likelihood of overcurrents in response to load and generation disturbances. The work uncovers both fast and slow modes of concern. Primarily, the findings show that increasing the P-F droop value improves the damping of the slower mode while degrading the damping of the faster mode, leading to an upper limit on the damping improvement of the GFM IBR output current. In EMT simulations of the more complex microgrid, the greatest damping improvement still exhibits significant maximum overshoot. Furthermore, the optimal value of the P-F droop parameter is sensitive to the number of SGs in operation. The conclusion is that tuning the P-F droop parameter is not an effective means to avoid the overcurrent tripping of GFM IBRs during response to large load or generation disturbances when operating in parallel to SGs in islanded microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes

Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well-known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for “tiling” the domain in two and three dimensions (2-D and 3-D), as this type of formal DDC construction is currently limited to 1-D. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-D for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-D parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to around 2700 cores, reducing a 5 h simulation to 8 s, while the 3-D algorithm maintains appreciable speedup up to 1700 cores.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On Concavity and Utilization Analytics of Wide-Area Network Transport Protocols

Wide-area data transfers in high-performance computing infrastructures and big data applications are increasingly carried over dynamically provisioned dedicated network connections, which provide high capacities with no competing traffic. Throughput of TCP and UDT over wide-area connections depends, often in a complex non-linear way, on the congestion control mechanism, buffer sizes, and the number of parallel streams. In addition, our extensive TCP and UDT throughput measurements and time traces over a suite of physical and emulated 10 Gbps connections with 0-366 ms round-trip times (RTTs) show significant statistical and temporal variations. Consequently, parameter selection and optimization for transport methods require data analytics to complement analytical throughput models. We present analytics based on the concavity-convexity of throughput profiles which provide insights into peak throughput and superior/inferior trend compared to linear interpolations based on RTT. In particular, we propose the utilization-concavity coefficient, a scalar metric that incorporates both utilization and concavity profile to characterize the overall performance of a transport protocol. These measurements-based anaytics enable us to select a transport protocol and its parameters for a given host and connection configuration to achieve high throughput with statistical guarantees.

Rao, Nageswara↗
DOE OSTI2019

Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). In this work, the proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1989

Single-mode projection filters for modal parameter identification for flexible structures

Single-mode projection filters are developed for eigensystem parameter identification from both analytical results and test data. Explicit formulations of these projection filters are derived using the orthogonal matrices of the controllability and observability matrices in the general sense. A global minimum optimization algorithm is applied to update the filter parameters by using the interval analysis method. The updated modal parameters represent the characteristics of the test data. For illustration of this new approach, a numerical simulation for the MAST beam structure is shown by using a one-dimensional global optimization algorithm to identify modal frequencies and damping. The projection filters are practical for parallel processing implementation.

Huang, Jen-Kuang↗