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At least 1,117 records · Page 62

All sources NASA NTRS DOE OSTI

DOE OSTI2024

High-energy, low-jitter, narrowband ps probe laser for kHz-rate fs/ps coherent anti-Stokes Raman scattering

Hybrid fs/ps coherent anti-Stokes Raman scattering (CARS) thermometry often utilizes ps probe pulses derived from pulse shaping or spectrally filtering the primary laser source or by synchronization with a low repetition rate external laser. This results in limited energy, spectral resolution, and/or repetition rate of the ps probe. In this work, a master-oscillator power-amplifier (MOPA) laser was synchronized to the oscillator of a Ti:sapphire regenerative amplifier to achieve high-energy (600 µJ), narrowband (58 ps) probe pulses at kHz repetition rates. Furthermore, temporal filtering allows the pulse characteristics to be adjusted for each application. At 25 Torr, relevant to high-speed flows, the kHz-rate MOPA system generated signal-to-noise ratios 3× higher in nitrogen and had improved precision relative to a 10 ps probe derived from spectral filtering and the power-amplifier. The MOPA system also enabled single-shot ro-vibrational hybrid fs/ps CARS thermometry in 650 K heated air.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

A Cartesian-diffusion Langevin method for hybrid kinetic-fluid Coulomb scattering in particle-in-cell plasma simulations

A novel, drag-diffusion Langevin method of hybrid, kinetic-fluid Coulomb scattering in plasmas is presented. Unlike previous methods, the frictional drag is always applied in the simulation frame of reference. The velocity-space diffusion is performed in the stationary-fluid frame of reference when anisotropic, and in the laboratory frame when isotropic. While the general method is mass-ratio independent, we focus on interactions of kinetic-ions and fluid-electrons to show first-order modifications to the electron velocity distribution function that are an important correction for the accurate calculation of electric resistivity. Inclusion of sub-cycling and a limit to the maximum collision frequency is shown to allow for arbitrarily large timesteps without numerical failure. Here the Langevin method is compared with a grid-based binary method and found to require a much less restrictive timestep in cases of ion–electron slowing and temperature equilibration; this finding differs from previous work and is dependent on the mass ratio.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Hybrid height and slope figuring method for grazing-incidence reflective optics

Grazing-incidence reflective optics are commonly used in synchrotron radiation and free-electron laser facilities to transport and focus the emitted X-ray beams. To preserve the imaging capability at the diffraction limit, the fabrication of these optics requires precise control of both the residual height and slope errors. However, all the surface figuring methods are height based, lacking the explicit control of surface slopes. Although our preliminary work demonstrated a one-dimensional (1D) slope-based figuring model, its 2D extension is not straightforward. In this study, a novel 2D slope-based figuring method is proposed, which employs an alternating objective optimization on the slopes in the x - and y -directions directly. An analytical simulation revealed that the slope-based method achieved smaller residual slope errors than the height-based method, while the height-based method achieved smaller residual height errors than the slope-based method. Therefore, a hybrid height and slope figuring method was proposed to further enable explicit control of both the height and slopes according to the final mirror specifications. An experiment to finish an elliptical-cylindrical mirror using the hybrid method with ion beam figuring was then performed. Both the residual height and slope errors converged below the specified threshold values, which verified the feasibility and effectiveness of the proposed ideas.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Implicit fast sweeping method for hyperbolic systems of conservation laws

Implicit time-accurate methods are often used to integrate stiff problems where explicit schemes impose severe time step restrictions. This paper presents an efficient numerical framework based on the Fast Sweeping Method (FSM) for solving linear and nonlinear hyperbolic systems of conservation laws. The solution at each discrete location is computed by sweeping the numerical domain in several predetermined directions that follow the causality of the characteristic families. The use of a fractional step strategy eliminates the need for a solution selection criterion while one-sided stencils limit the number of sweeps to at most 2 d for d space dimensions. This work focuses on the first-order implicit upwind method since it constitutes the building block for high-order conservative schemes. For problems where the degree of stiffness evolves over time, implicit-explicit hybridization can be accomplished with the same algorithm by simply switching the stencil at each time level. As opposed to traditional implicit solvers, the sweeping method does not require a local time linearization of the fluxes thereby preserving the nonlinear stability properties of the original implicit scheme. It also avoids the large computational and memory requirements associated with solving large block-diagonal systems of equations. Here, a series of one- and two-dimensional test cases are presented for the inviscid Burgers' equation and the reactive Euler equations. The results indicate that the implicit FSM can allow a major reduction in the number of time steps even in the presence of discontinuous solution profiles.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Three-Dimensional Full-Core BEAVRS Using OpenMOC with Transport Equivalence

Using an optimized implementation of the three-dimensional (3D) method of characteristics for neutron transport, along with a novel equivalence method for transport calculations that was designed to correct self-shielding errors from neglecting the angular dependence of resonant group absorption, a 3D full-core light water reactor hybrid stochastic-deterministic eigenvalue calculation was achieved. This paper presents the optimizations developed and compares the transport solutions obtained. For the statepoint, run times near 10 000 CPU hours are achieved—improving on previous works by an order of magnitude—with near 1% error on pin fission to 238 U capture ratios and a few dozen pcms on the eigenvalue.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Identifying the fingerprints of topological states by tuning magnetoresistance in a semimetal: The case of topological half-Heusler Pt 1 - x Au x LuSb

We report that topological materials often exhibit remarkably linear nonsaturating magnetoresistance (LMR), which is both of scientific and technological importance. However, the role of topologically nontrivial states in the emergence of such a behavior has eluded clear demonstration in experiments. Here, by reducing the coupling between the topological surface states (TSS) and the bulk carriers, we controllably tune the LMR behavior in Pt 1-x Au x LuSb into distinct plateaus in Hall resistance, which we show arise from a quantum Hall phase. This allowed us to reveal how smearing of the Landau levels, which otherwise gives rise to a quantum Hall phase, results in an LMR behavior due to strong interaction between the TSS with a positive g factor and the bulk carriers. We establish that controlling the coupling strength between the surface and the bulk carriers in topological materials can bring about dramatic changes in their magnetotransport behavior. In addition, our work outlines a strategy to reveal macroscopic physical observables of TSS in compounds with a semimetallic bulk band structure, as is the case in multifunctional Heusler compounds, thereby opening up opportunities for their utilization in hybrid quantum structures.

36 MATERIALS SCIENCE↗

DOE OSTI2020

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer paral- lelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation.

Wang, Tianqi↗

DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗

DOE OSTI2021

Lean-Stratified Combustion System with Miller Cycle for Downsized Boosted Application - Part I

Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio, and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. A high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the first to two papers describing the development of a combustion system combining lean-stratified combustion with Miller cycle for downsized boosted applications. The work was completed under a multi-year US DOE project. The goal was to define a light-duty engine package capable of achieving a 35% fuel economy improvement at US Tier 3 emission standards over a naturally aspirated stoichiometric baseline vehicle. A multi-mode combustion system was developed enabling highly efficient lean-stratified operation at light-load and stoichiometric Miller-cycle operation at mid- to highloads. Some of the unique challenges in synergistically combining the two concepts are highlighted in this paper. Here, a central direct-injection four-valve layout was designed with high-tumble ports and a bowl-in-piston capable of stable operation under highly dilute (lean plus EGR) mixtures when properly matched to fuel spray characteristics, a multiple-injection strategy, and a high-energy ignition system. Other challenges specific to meeting exhaust emissions requirements with a high-efficiency engine are also explored in this study. In this Part 1 of the paper, the single-cylinder combustion system design, analysis, and testing is described. Multicylinder engine system design, analysis and development is described in Part 2 of the paper.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1992

Electron-ion hybrid instabilities driven by velocity shear in a magnetized plasma

The stability of a magnetized plasma is investigated in which a sheared electron flow channel is present. The flow's peak velocity and shear scale length are denoted by V and L, respectively. If the velocity channel is perpendicular to the confining magnetic field and L is less than the ion Larmor radius, an electrostatic instability develops whose frequency is on the order of the lower hybrid frequency. It is concluded that velocity shear is the only source of free energy. Further, it is shown that density gradients do not stabilize this mode. It follows that the mode presented in this work can not be identified with the well-known modified two-stream instability. If the velocity channel is parallel to the confining magnetic field and the plasma is weakly magnetized, an instability driven by velocity shear is shown to exist.

Romero, H.↗

NASA NTRS1996

Fundamental Phenomena on Fuel Decomposition and Boundary-Layer Combustion Precesses with Applications to Hybrid Rocket Motors: Experimental Investigation - Part 1

This final report summarizes the major findings on the subject of 'Fundamental Phenomena on Fuel Decomposition and Boundary-Layer Combustion Processes with Applications to Hybrid Rocket Motors', performed from 1 April 1994 to 30 June 1996. Both experimental results from Task 1 and theoretical/numerical results from Task 2 are reported here in two parts. Part 1 covers the experimental work performed and describes the test facility setup, data reduction techniques employed, and results of the test firings, including effects of operating conditions and fuel additives on solid fuel regression rate and thermal profiles of the condensed phase. Part 2 concerns the theoretical/numerical work. It covers physical modeling of the combustion processes including gas/surface coupling, and radiation effect on regression rate. The numerical solution of the flowfield structure and condensed phase regression behavior are presented. Experimental data from the test firings were used for numerical model validation.

Kuo, Kenneth K.↗

NASA NTRS2005

High Energy Cutting and Stripping Utilizing Liquid Nitrogen

The Aerospace Industry has endeavored for decades to develop hybrid materials that withstand the rigors of mechanized flight both within our atmosphere and beyond. The development of these high performance materials has led to the need for environmentally friendly technologies for material re-work and removal. The NitroJet(TM) is a fluid jet technology that represents an evolution of the widely used, large-scale water jet fluid jet technology. It involves the amalgamation of fluid jet technology and cryogenics technology to create a new capability that is applicable where water jet or abrasive jet (water jet plus entrained abrasive) are not suitable or acceptable because of technical constraints such as process or materials compatibility, environmental concerns and aesthetic or legal requirements. The NitroJet(TM) uses ultra high-pressure nitrogen to cut materials, strip numerous types of coatings such as paint or powder coating, clean surfaces and profile metals. Liquid nitrogen (LN2) is used as the feed stream and is pressurized in two stages. The first stage pressurizes sub cooled LN2 to an intermediate pressure of between 15,000 and 20,000 psi at which point the temperature of the LN2 is about -250 F. The discharge from this stage is then introduced as feed to a dual intensifier system, which boosts the pressure from 15,000 - 20,000 psi up to the maximum operating pressure of 55,000 psi. A temperature of about -220 F is achieved at which point the nitrogen is supercritical. In this condition the nitrogen cuts, strips and abrades much like ultra high-pressure water would but without any residual liquid to collect, remove or be contaminated. Once the nitrogen has performed its function it harmlessly flashes back into the atmosphere as pure nitrogen gas. The system uses heat exchangers to control and modify the temperature of the various intake and discharge nitrogen streams. Since the system is hydraulically operated, discharge pressures can be easily varied over a very wide range providing considerable flexibility for various operations. The NitroJet(TM) is an advance on the nitrogen fluid jet technology initially developed at the Idaho National Engineering Laboratory in Idaho Falls, Idaho. NitroCision(R) first introduced the NitroJet(TM) into a commercial setting in 2003 and there has been considerable interest from many diverse sectors of government and industry since then. While the current system is an industrial system with the size and mass normally associated with industrial applications, a smaller system that is much more compact is being contemplated for those applications that do not need the full capabilities of the larger system. The NitroJet(TM) can be deployed as a fixed or mobile system with multiple end effectors capable of cutting, stripping, cleaning, and surface profiling either in robotic or manual applications.

Hume, Howard↗

NASA NTRS2016

Environmentally Friendly Coating Technology for Autonomous Corrosion Control

Coatings↗

NASA NTRS2016

Corrosion-Activated Micro-Containers for Environmentally Friendly Corrosion Protective Coatings

This work concerns the development of environmentally friendly encapsulation technology, specifically designed to incorporate corrosion indicators, inhibitors, and self-healing agents into a coating, in such a way that the delivery of the indicators and inhibitors is triggered by the corrosion process, and the delivery of self-healing agents is triggered by mechanical damage to the coating. Encapsulation of the active corrosion control ingredients allows the incorporation of desired autonomous corrosion control functions such as: early corrosion detection, hidden corrosion detection, corrosion inhibition, and self-healing of mechanical damage into a coating. The technology offers the versatility needed to include one or several corrosion control functions into the same coating.The development of the encapsulation technology has progressed from the initial proof-of-concept work, in which a corrosion indicator was encapsulated into an oil-core (hydrophobic) microcapsule and shown to be delivered autonomously, under simulated corrosion conditions, to a sophisticated portfolio of micro carriers (organic, inorganic, and hybrid) that can be used to deliver a wide range of active corrosion ingredients at a rate that can be adjusted to offer immediate as well as long-term corrosion control. The micro carriers have been incorporated into different coating formulas to test and optimize the autonomous corrosion detection, inhibition, and self-healing functions of the coatings. This paper provides an overview of progress made to date and highlights recent technical developments, such as improved corrosion detection sensitivity, inhibitor test results in various types of coatings, and highly effective self-healing coatings based on green chemistry. The NASA Kennedy Space Centers Corrosion Technology Lab at the Kennedy Space Center in Florida, U.S.A. has been developing multifunctional smart coatings based on the microencapsulation of environmentally friendly corrosion indicators, inhibitors and self-healing agents. This allows the incorporation of autonomous corrosion control functionalities, such as corrosion detection and inhibition as well as the self-healing of mechanical damage, into coatings. This paper presents technical details on the characterization of inhibitor-containing particles and their corrosion inhibitive effects using electrochemical and mass loss methods.Three organic environmentally friendly corrosion inhibitors were encapsulated in organic microparticles that are compatible with desired coatings. The release of the inhibitors from the microparticles in basic solution was studied. Fast release, for immediate corrosion protection, as well as long-term release for continued protection, was observed.The inhibition efficacy of the inhibitors, incorporated directly and in microparticles, on carbon steel was evaluated. Polarization curves and mass loss measurements showed that, in the case of 2MBT, its corrosion inhibition effectiveness was greater when it was delivered from microparticles.

Corrosion Control↗

NASA NTRS2016

Hybrid Propulsion In-Situ Resource Utilization Test Facility Results for Performance Characterization

Hybrid propulsion presents a promising alternative to conventional systems for in-space propulsion applications using In-Situ Propellant Production (ISPP). A hybrid propulsion test facility has been built at the NASA Jet Propulsion Laboratory (JPL) with the capability to evaluate ISPP oxidizers (gaseous mixtures of O2 and CO2) and modify fuel type and chamber geometry. This work is the result of a continuing effort at JPL to characterize the burn characteristics and performance of different propellant combinations and motor configurations in order to refine and support preliminary designs. Twelve tests using gaseous oxygen and two paraffin based fuels (Black Paraffin and SP1X) from the 2015 campaign are presented. Different reduction techniques are compared in an effort to determine the uncertainty associated with deriving a regression rate law and evaluating c* efficiency with the collected data.

Mechentel, F. S.↗

DOE OSTI2022

Double-Bilayer polar nanoregions and Mn antisites in (Ca, Sr)3Mn2O7

Abstract The layered perovskite Ca 3 Mn 2 O 7 (CMO) is a hybrid improper ferroelectric candidate proposed for room temperature multiferroicity, which also displays negative thermal expansion behavior due to a competition between coexisting polar and nonpolar phases. However, little is known about the atomic-scale structure of the polar/nonpolar phase coexistence or the underlying physics of its formation and transition. In this work, we report the direct observation of double bilayer polar nanoregions (db-PNRs) in Ca 2.9 Sr 0.1 Mn 2 O 7 using aberration-corrected scanning transmission electron microscopy (S/TEM). In-situ TEM heating experiments show that the db-PNRs can exist up to 650 °C. Electron energy loss spectroscopy (EELS) studies coupled with first-principles calculations demonstrate that the stabilization mechanism of the db-PNRs is directly related to an Mn oxidation state change (from 4+ to 2+), which is linked to the presence of Mn antisite defects. These findings open the door to manipulating phase coexistence and achieving exotic properties in hybrid improper ferroelectric.

36 MATERIALS SCIENCE↗