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Results for “acceleration factor”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Influence of maneuverability on helicopter combat effectiveness

A computational procedure employing a stochastic learning method in conjunction with dynamic simulation of helicopter flight and weapon system operation was used to derive helicopter maneuvering strategies. The derived strategies maximize either survival or kill probability and are in the form of a feedback control based upon threat visual or warning system cues. Maneuverability parameters implicit in the strategy development include maximum longitudinal acceleration and deceleration, maximum sustained and transient load factor turn rate at forward speed, and maximum pedal turn rate and lateral acceleration at hover. Results are presented in terms of probability of skill for all combat initial conditions for two threat categories.

Falco, M.↗
NASA NTRS2001

Microgravity Science Glovebox Investigations SUBSA

Solidification Using a Baffle in Sealed Ampoules (SUBSA) is a Microgravity Science Glovebox Investigation manifested for the UF2 flight, on the U.S. Orbiter 111, to the International Space Station (ISS). SUBSA complements the "parent" flight investigation CG13 (Space-and Groundbased Crystal Growth Using a Baffle). During directional solidification, the disk-shaped baffle acts as a partition, creating a small melt zone at the solid-liquid interface. As a result, the level of buoyancy-driven convection at the interface is significantly reduced. In space, the baffle will reduce convection driven by residual micro acceleration. The baffle reduces the Rayleigh number (Ra) of the melt by a factor of 103. The combined effect of the baffle and microgravity will yield a reduction in Ra by a factor of 107 to 109 approaching effectively the acceleration conditions in "nanogravity". The results of ground based tests and numerical modeling will be presented. The furnace for directional solidification (flight hardware and the ground unit) was developed by Tec-Masters Inc. The flight ampoules were produced jointly at Rensselaer, Tec-Masters Inc. and Crystallod Inc.

Ostrogorsky, A.↗
DOE OSTI2023

Commissioning of a two-target DC cylindrical magnetron sputter coater for depositing Nb 3 Sn film on Nb superconducting radiofrequency cavities

A DC cylindrical magnetron sputter coater was commissioned and used to coat Nb 2.6 GHz superconducting radiofrequency (SRF) cavity with Nb 3 Sn. The sputter coater has two identical cylindrical magnetrons that can move, with a controlled speed, along the axis of the SRF cavity to coat the inside surface of the cavity. The design of the sputter coater is discussed, along with its performance. Initially, a sample holder that allows coating on flat substrates at positions similar to the equator and beam tubes of a 2.6 GHz SRF cavity was used to test conditions for fabricating Nb 3 Sn layers. Multilayers of Nb and Sn were sequentially sputtered on flat Nb and sapphire substrates mounted on the equivalent positions of the cavity's beam tubes and the equator using the two identical cylindrical magnetrons. Then, the Nb/Sn multilayers were annealed at 950 °C for 3 h. Here, the ~1.2 μm thick Nb 3 Sn film did not show any other Nb–Sn compounds and had a superconducting transition temperature of 17.61–17.76 K. The 2.6 GHz SRF cavity was coated using similar conditions as flat samples. Cryogenic RF testing of the Nb 3 Sn-coated cavity demonstrated a quality factor of 3.2 × 10 8 at an accelerating gradient of 5 MV/m at 4.4 K.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Alkali Metal Alkoxides in Noyori-Type Hydrogenations

Chemical modification of the catalyst via the formation of the amidate HM-N-M a + complexes and/or physical activation of the substrate through C=O…M a + non-covalent interactions are two factors responsible for the rate acceleration in Noyori-type hydrogenations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The Onco-cardiologist Dilemma: to Implant, to Defer, or to Avoid Transcatheter Aortic Valve Replacement in Cancer Patients with Aortic Stenosis?

Purpose of Review: Aging is associated with an increased prevalence of both cancer and heart disease. The progression of aortic valve calcification to aortic stenosis may be accelerated by both cardiovascular risk factors and cancer treatments, such as radiotherapy with mediastinal involvement. Symptomatic aortic stenosis is occasionally diagnosed in cancer patients undergoing cardiovascular evaluation; likewise, cancer is often recognized during assessment preceding aortic valve interventions. In these complex cases, physicians face difficult treatment decisions. Due to a myriad of clinical presentations of cancer and valve disease, specific guidelines for this patient population are not currently in place. Management is currently based on clinical judgment, on an individual basis. Recent Findings: Patients with cancer in remission or with a favorable prognosis should be treated according to current cardiovascular guidelines. In these patients, aortic valve replacement can be performed either by surgery or transcatheter. Significant challenges arise in patients with active cancer, especially those receiving anti-cancer treatment. Recent data suggests that these patients can be offered aortic valve replacement, with a trend of favoring the transcatheter route in order to minimize perioperative risk and complications associated with major surgery. Patients with advanced cancer and severe aortic stenosis should be offered palliative care and can benefit from aortic balloon valvuloplasty if indicated. Modern cancer treatments associated with improved long-term prognosis may allow the appropriate cure of aortic stenosis. Summary: We discuss the protocol, outcomes, and evolving recommendations of aortic valve replacement in cancer patients with aortic stenosis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Predicting the morphologies of γ' precipitates in cobalt-based superalloys

Cobalt-based alloys with γ/γ' microstructures have the potential to become the next generation of superalloys, but alloy compositions and processing steps must be optimized to improve coarsening, creep, and rafting behavior. While these behaviors are different than in nickel-based superalloys, alloy development can be accelerated by understanding the thermodynamic factors influencing microstructure evolution. In this work, we develop a phase field model informed by first-principles density functional theory and experimental data to predict the equilibrium shapes of Co-Al-W γ' precipitates. Three-dimensional simulations of single and multiple precipitates are performed to understand the effect of elastic and interfacial energy on coarsened and rafted microstructures; the elastic energy is dependent on the elastic stiffnesses, misfit strain, precipitate size, applied stress, and precipitate spatial distribution. We observe characteristic microstructures dependent on the type of applied stress that have the same γ' morphology and orientation seen in experiments, indicating that the elastic stresses arising from coherent γ/γ' interfaces are important for morphological evolution during creep. Here, the results also indicate that the narrow γ channels between γ' precipitates are energetically favored, and provide an explanation for the experimentally observed directional coarsening that occurs without any applied stress.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Direct observation of pitting corrosion evolutions on carbon steel surfaces at the nano-to-micro- scales

The Cl¯-induced corrosion of metals and alloys is of relevance to a wide range of engineered materials, structures, and systems. Because of the challenges in studying pitting corrosion in a quantitative and statistically significant manner, its kinetics remain poorly understood. Herein, by direct, nano- to micro-scale observations using vertical scanning interferometry (VSI), we examine the temporal evolution of pitting corrosion on AISI 1045 carbon steel over large surface areas in Cl¯-free, and Cl¯-enriched solutions. Special focus is paid to examine the nucleation and growth of pits, and the associated formation of roughened regions on steel surfaces. By statistical analysis of hundreds of individual pits, three stages of pitting corrosion, namely, induction, propagation, and saturation, are quantitatively distinguished. By quantifying the kinetics of these processes, we contextualize our current understanding of electrochemical corrosion within a framework that considers spatial dynamics and morphology evolutions. In the presence of Cl¯ ions, corrosion is highly accelerated due to multiple autocatalytic factors including destabilization of protective surface oxide films and preservation of aggressive microenvironments within the pits, both of which promote continued pit nucleation and growth. Furthermore these findings offer new insights into predicting and modeling steel corrosion processes in mid-pH aqueous environments

36 MATERIALS SCIENCE↗
DOE OSTI2021

Efficiency analysis of helium-cooled MAS DNP: case studies of surface-modified nanoparticles and homogeneous small-molecule solutions

Despite the growing number of successful applications of dynamic nuclear polarization (DNP)-enhanced magic-angle spinning (MAS) NMR in structural biology and materials science, the nuclear polarizations achieved by current MAS DNP instrumentation are still considerably lower than the theoretical maximum. Here, the method could be significantly strengthened if experiments were performed at temperatures much lower than those currently widely used (~100 K). Recently, the prospects of helium (He)-cooled MAS DNP have been increased with the instrumental developments in MAS technology that uses cold helium gas for sample cooling. Despite the additional gains in sensitivity that have been observed with He-cooled MAS DNP, the performance of the technique has not been evaluated in the case of surfaces and interfaces that benefit the most from DNP. Herein, we studied the efficiency of DNP at temperatures between ~30 K and ~100 K for organically functionalized silica material and a homogeneous solution of small organic molecules at a magnetic field B 0 = 16.4 T. We recorded the changes in signal enhancement, paramagnet-induced quenching and depolarization effects, DNP build-up rate, and Boltzmann polarization. For these samples, the increases in MAS-induced depolarization and DNP build-up times at around 30 K were not as severe as anticipated. In the case of the surface species, we determined that MAS DNP at 30 K provided ~10 times higher sensitivity than MAS DNP at 90 K, which corresponds to the acceleration of experiments by multiplicative factors of up to 100.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Recent progress with the top to bottom approach to vectorization in GeantV

SIMD acceleration can potentially boost by factors the application throughput. Achieving efficient SIMD vectorization for scalar code with complex data flow and branching logic, goes however way beyond breaking some loop dependencies and relying on the compiler. Since the refactoring effort scales with the number of lines of code, it is important to understand what kind of performance gains can be expected in such complex cases. We started to investigate a couple of years ago a top to bottom vectorization approach to particle transport simulation. Percolating vector data to algorithms was mandatory since not all the components can internally vectorize. Vectorizing low-level algorithms is certainly necessary, but not sufficient to achieve relevant SIMD gains. In addition, the overheads for maintaining the concurrent vector data flow and copy data have to be minimized. In the context of a vectorization R&D for simulation we developed a framework to allow different categories of scalar and vectorized components to co-exist, dealing with data flow management and real-time heuristic optimizations. The paper describes our approach on coordinating SIMD vectorization at framework level, making a detailed quantitative analysis of the SIMD gain versus overheads, with a breakdown by components in terms of geometry, physics and magnetic field propagation. We also present the more general context of this R&D work and goals for 2018.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Accelerating multilevel Markov Chain Monte Carlo using machine learning models

Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We suggest a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118-and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

MAGNETRON SPUTTERING OF Nb3Sn FOR SRF CAVITIES

Nb3Sn is a potential candidate for surface material of SRF cavities since it can enable the cavity to operate at higher temperatures with high quality factor and at an increased accelerating gradient. Nb-Sn films were deposited using magnetron sputtering of individual Nb and Sn targets onto Nb and sapphire substrates. The as-deposited films were annealed at 1200 °C for 3 hours. The films were characterized for their structure by X-ray Diffraction (XRD), morphology by Field Emission Scanning Electron Microscopy (FESEM), and composition by Energy Dispersive X-ray Spectroscopy (EDS) and Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS). The surface resistivity was measured down to cryogenic temperature to determine the superconducting transition temperature and its width. The composition of the multilayered films was controlled by varying the thickness of the Nb and Sn layers. The films showed crystalline Nb3Sn phases with Tc up to 17.6 K.

Eremeev, G.↗
DOE OSTI2017

Cavity Processing and Preparation of 650 MHz Elliptical Cell Cavities for PIP-II

The PIP-II project at Fermilab requires fifteen 650 MHz SRF cryomodules as part of the 800 MeV LINAC that will provide a high intensity proton beam to the Fermilab neutrino program. A total of fifty-seven high-performance SRF cavities will populate the cryomodules and will operate in both pulsed and continuous wave modes. These cavities will be processed and prepared for performance testing utilizing adapted cavity processing infrastructure already in place at Fermilab and Argonne. The processing recipes implemented for these structures will incorporate state-of-the art processing and cleaning techniques developed for 1.3 GHz SRF cavities for the ILC, XFEL, and LCLS-II projects. This paper describes the details of the processing recipes and associated chemistry, heat treatment, and cleanroom processes at the Fermilab and Argonne cavity processing facilities. This paper also presents single and multi-cell cavity test results with quality factors above 5·10¹⁰ and accelerating gradients above 30 MV/m.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Design, Fabrication and Concept for the Surface Treatment of the SRF Cavity Prototype for the CLIC Damping Rings

The Compact Linear Collider (CLIC) Damping Rings (DRs) need to generate ultra-low emittance bunches to achieve high luminosity in CLIC. This requires many wiggler magnets with big energy loss which is compensated by the Radio Frequency (RF) system. The resulting strong beam loading transients lead to a challenging design for the RF system. A novel SRF cavity at 2 GHz with an ultra-low R/Q parameter of below 1 Ω is proposed to minimize the transient beam loading effects below acceptable level. The design and fabrication of the bulk Nb prototype based on turning from a single piece of Nb and EB welding is presented. Moreover, conceptual study of the system for cavity surface treatment to achieve the highest surface magnetic field which is the main goal of the prototype cold test is described as well. To enable excellent performance in this cavity, we plan to apply the 75/120C modified low temperature bake in combination with the cold electropolishing process. This surface treatment approach has been shown to consistently deliver high accelerating gradients and improved quality factors in TESLA-shaped 1.3 GHz SRF cavities. By adapting and implementing this process for the 2 GHz ultra-low R/Q design, we aim to maximize the achievable surface magnetic field while minimizing residual resistance and field emission. This treatment strategy will be critical for demonstrating that the cavity can meet the demanding performance requirements of the CLIC damping ring RF system under high beam loading conditions.

Bafia, Daniel P. [Fermilab]↗
DOE OSTI2021

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

alternating direction method of multipliers↗
DOE OSTI2022

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1977

Anomalous solar rotation in the early 17th century

The character of solar rotation has been examined for two periods in the early 17th century for which detailed sunspot drawings are available: A.D. 1625 through 1626 and 1642 through 1644. The first period occurred 20 years before the start of the Maunder sunspot minimum, 1645 through 1715; the second occurred just at its commencement. Solar rotation in the earlier period was much like that of today. In the later period, the equatorial velocity of the sun was faster by 3 to 5 percent and the differential rotation was enhanced by a factor of 3. The equatorial acceleration with declining solar activity is in the same sense as that found in recent Doppler data. It seems likely that the change in rotation of the solar surface between 1625 and 1645 was associated with the onset of the Maunder Minimum.

Eddy, J. A.↗