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At least 127 records · Page 7

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DOE OSTI2025

Soft x-ray high-harmonic generation in an anti-resonant hollow core fiber driven by a 3 μ m ultrafast laser

High-harmonic upconversion driven by a mid-infrared femtosecond laser can generate coherent soft x-ray beams in a tabletop-scale setup. Here, we report on a compact ytterbium-pumped optical parametric chirped pulse amplifier (OPCPA) laser system seeded by an all-fiber front-end and employing periodically poled lithium niobate (PPLN) nonlinear media operated near the pulse fluence limits of current commercially available PPLN crystals. The OPCPA delivers 3 µm wavelength pulses with 775 µJ energy at 1 kHz repetition rate, with transform-limited 120 fs pulse duration, diffraction-limited beam quality, and ultrahigh 0.33% rms energy stability over >18 h. Using this laser, we generate soft x-ray high harmonics (HHG) in argon gas by focusing into a low-loss, high-pressure gas-filled anti-resonant hollow core fiber (ARHCF), generating coherent light at photon energies up to the argon L-edge (250 eV) and carbon K-edge (284 eV), with high beam quality and ∼1% rms energy stability. This work demonstrates soft x-ray HHG in a high-efficiency guided-wave phase matched geometry, overcoming the high losses inherent to mid-IR propagation in unstructured waveguides, or the short interaction lengths of gas cells or jets. The ARHCF can operate in the long term without damage and with the repetition rate, stability, and robustness required for demanding applications in spectromicroscopy and imaging. Finally, we discuss routes for further optimizing the soft x-ray HHG flux by driving He at higher laser intensities using either the signal (1.5 μm) or idler wavelengths (3 μm).

Femtosecond lasers↗

DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗

DOE OSTI2025

Does provable absence of barren plateaus imply classical simulability?

A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly addressed: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically? We collect evidence-on a case-by-case basis-that many commonly used models whose loss landscapes avoid barren plateaus can also admit classical simulation, provided that one can collect some classical data from quantum devices during an initial data acquisition phase. This follows from the observation that barren plateaus result from a curse of dimensionality, and that current approaches for solving them end up encoding the problem into some small, classically simulable, subspaces. Thus, while stressing that quantum computers can be essential for collecting data, our analysis sheds doubt on the information processing capabilities of many parametrized quantum circuits with provably barren plateau-free landscapes. We end by discussing the (many) caveats in our arguments including the limitations of average case arguments, the role of smart initializations, models that fall outside our assumptions, the potential for provably superpolynomial advantages and the possibility that, once larger devices become available, parametrized quantum circuits could heuristically outperform our analytic expectations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Quantum Imaging with X-rays

Quantum imaging encompasses a broad range of methods that exploit the quantum properties of light to capture information about an object. One such approach involves using a two-photon quantum state, where only one photon interacts with the object being imaged while its entangled partner carries spatial or temporal information. To implement this technique, it is necessary to generate specific quantum states of light and detect photons at the single-photon level. While this method has been successfully demonstrated in the visible electromagnetic spectrum, extending it to X-rays has faced significant challenges due to the difficulties in producing a sufficient rate of X-ray photon pairs and detecting them with adequate resolution. Here, we demonstrate record high rates of correlated X-ray photon pairs produced via a spontaneous parametric down-conversion process and we employ these photons to perform quantum correlation imaging of several objects, including a biological sample (E. cardamomum seedpod). Notably, we report an unprecedented detection rate of about 6,300 pairs per hour and the observation of energy anti-correlation for the X-ray photon pairs. We also present a detailed analysis of the properties of the down-converted X-ray photons, as well as a comprehensive study of the correlation imaging formation, including a study of distortions and corrections. These results mark a substantial advancement in X-ray quantum imaging, expanding the possibilities of X-ray quantum optical technologies, and illustrating the pathway towards enhancing biological imaging with reduced radiation doses.

Gofron, Kaz [ORNL] (ORCID:0000000314415736)↗

NASA NTRS1975

Theory of type 3b solar radio bursts

During the initial space-time evolution of an electron beam injected into the corona, the strong beam-plasma interaction occurs at the head of the beam, leading to the amplification of a quasi-monochromatic large-amplitude plasma wave that stabilizes by trapping the beam particles. Oscillation of the trapped particles in the wave troughs amplifies sideband electrostatic waves. The sidebands and the main wave subsequently decay to observable transverse electromagnetic waves through the parametric decay instability. This process gives rise to the elementary striation bursts. Owing to velocity dispersion in the beam and the density gradient of the corona, the entire process may repeat at a finite number of discrete plasma levels, producing chains of elementary bursts. All the properties of the type IIIb bursts are accounted for in the context of the theory.

Smith, R. A.↗

NASA NTRS1976

Theory of type IIIb solar radio bursts

Smith, R. A.↗

NASA NTRS1984

Design and analysis techniques for trajectory correction maneuvers

Selecting a spacecraft maneuver strategy usually involves tradeoffs of many competing factors. This paper describes such a tradeoff process. The method uses parametric data to cope with targeting requirements, thruster configuration and performance, hardware constraints, operational considerations, propellant optimization demands, and expected execution and orbit determination errors, while remaining flexible to react to new conditions.

Hintz, G. R.↗

NASA NTRS1989

High performance alloy electroforming

Electroformed copper and nickel are used in structural applications for advanced propellant combustion chambers. An improved process has been developed by Bell Aerospace Textron, Inc. wherein electroformed nickel-manganese alloy has demonstrated superior mechanical and thermal stability when compared to previously reported deposits from known nickel plating processes. Solution chemistry and parametric operating procedures are now established and material property data is established for deposition of thick, large complex shapes such as the Space Shuttle Main Engine. The critical operating variables are those governing the ratio of codeposited nickel and manganese. The deposition uniformity which in turn affects the manganese concentration distribution is affected by solution resistance and geometric effects as well as solution agitation. The manganese concentration in the deposit must be between 2000 and 3000 ppm for optimum physical properties to be realized. The study also includes data regarding deposition procedures for achieving excellent bond strength at an interface with copper, nickel-manganese or INCONEL 718. Applications for this electroformed material include fabrication of complex or re-entry shapes which would be difficult or impossible to form from high strength alloys such as INCONEL 718.

Malone, G. A.↗

NASA NTRS1993

Mission activities planning for a Hermes mission by means of AI-technology

Mission Activities Planning is a complex task to be performed by mission control centers. AI technology can offer attractive solutions to the planning problem. This paper presents the use of a new AI-based Mission Planning System for crew activity planning. Based on a HERMES servicing mission to the COLUMBUS Man Tended Free Flyer (MTFF) with complex time and resource constraints, approximately 2000 activities with 50 different resources have been generated, processed, and planned with parametric variation of operationally sensitive parameters. The architecture, as well as the performance of the mission planning system, is discussed. An outlook to future planning scenarios, the requirements, and how a system like MARS can fulfill those requirements is given.

Pape, U.↗

NASA NTRS1997

Emulsion Droplet Combustion in Microgravity: Water/Heptane Emulsions

This presentation reviews a series of experiments to further examine parametric effects on sooting processes of droplet flames in microgravity. The particular focus is on a fuel droplet emulsified with water, specifically emulsions of n-heptane as the fuel-phase and water as the dispersed phase. Water was selected as the additive because of its anticipated effect on soot formation, and the heptane fuel phase was chosen to theoretically reduce the likelihood of microexplosions because its boiling point is nearly the same as that of water: 100 C for water and 98 C for heptane. The water content was varied while the initial droplet diameter was kept within a small range. The experiments were carried out in microgravity to reduce the effects of buoyancy and to promote spherical symmetry in the burning process. Spherically symmetric droplet burning is a convenient starting point for analysis, but experimental data are difficult to obtain for this situation as evidenced by the fact that no quantitative data have been reported on unsupported emulsion droplet combustion in a convection-free environment. The present study improves upon past work carried out on emulsion droplet combustion in microgravity which employed emulsion droplets suspended from a fiber. The fiber can be instrusive to the emulsion droplet burning process as it can promote coalescence of the dispersed water phase and heterogeneous nucleation on the fiber. Prior work has shown that the presence of water in liquid hydrocarbons can have both beneficial and detrimental effects on the combustion process. Water is known to reduce soot formation and radiation heat transfer to combustor walls Gollahalli (1979) reduce flame temperatures and thereby NOx emissions, and encourage secondary droplet atomization or microexplosion. Water also tends to retard ignition and and promote early extinction. The former effect restricted the range of water volume fractions as discussed below.

Avedisian, C. Thomas↗

NASA NTRS2002

Plasma-enhanced chemical vapor deposition of multiwalled carbon nanofibers

Plasma-enhanced chemical vapor deposition is used to grow vertically aligned multiwalled carbon nanofibers (MWNFs). The graphite basal planes in these nanofibers are not parallel as in nanotubes; instead they exhibit a small angle resembling a stacked cone arrangement. A parametric study with varying process parameters such as growth temperature, feedstock composition, and substrate power has been conducted, and these parameters are found to influence the growth rate, diameter, and morphology. The well-aligned MWNFs are suitable for fabricating electrode systems in sensor and device development.

Nanotechnology/instrumentation/methods↗

NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗

NASA NTRSDate not supplied

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts.1,2 The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners.1 However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen M Middleton↗

DOE OSTI2025

X-ray Parametric Down Conversion at an XFEL

Understanding and harnessing X-ray quantum effects could open new frontiers in imaging and quantum optics. In this study, we measured the process of X-ray Parametric Down Conversion, where a single high-energy X-ray photon splits into two lower-energy photons. Using the SACLA X-ray Free Electron Laser in Japan at 9.83 keV, we found clear evidence that pairs of photons were produced along the energy-angle relationship that conserved both energy and momentum, as predicted for down-conversion, and consistent with quantum entanglement of X-ray photons. By matching specific photon pairs for energy and momentum conservation, we observe a signal rate of 1,250 pairs per hour, confirming that correlated photon pairs can be generated and observed in the absence of explicit time correlations. Our results show that with further refinement, the number of entangled photons produced per laser pulse could increase by an order of magnitude. This paves the way for demonstrating quantum-enhanced X-ray imaging, and confirmation of X-ray photon entanglement.

47 OTHER INSTRUMENTATION↗

NASA NTRS2005

Nanomaterials Work at NASA-Johnson Space Center

Nanomaterials activities at NASA-Johnson Space Center focus on single wall carbon nanotube production, characterization and their applications for aerospace. Nanotubes are produced by arc and laser methods and the growth process is monitored by in-situ diagnostics using time resolved passive emission and laser induced fluorescence of the active species. Parametric study of both these processes are conducted to monitor the effect of production parameters including temperature, buffer gas, flow rate, pressure, laser fluence and arc current. Characterization of the nanotube material is performed using the NASA-JSC protocol developed by combining analytical techniques of SEM, TEM, UV-VIS-NIR absorption, Raman, and TGA. Efforts at JSC over the past five years in composites have centered on structural polymernanotube systems. Recent activities broadened this focus to multifunctional materials, supercapacitors, fuel cells, regenerable CO2 absorbers, electromagnetic shielding, radiation dosimetry and thermal management systems of interest for human space flight. Preliminary tests indicate improvement of performance in most of these applications because of the large surface area as well as high conductivity exhibited by SWCNTs.

Arepalli, Sivaram↗

DOE OSTI2026

Design optimization of MAPS-based detectors using a data-driven fast simulation approach

A parametric simulation tool for pixel sensors is presented. A realistic pixel response is simulated purely based on measurement input, without requiring detailed knowledge of the underlying manufacturing process. As such, it provides an efficient alternative to the use of Technology Computer-Aided Design simulations, which typically depend on proprietary process information. Due to its parametric approach, the package is fast and thus particularly useful for larger detector systems and high hit rate environments. This work presents measurements, simulation and its validation for the MALTA2 sensor. It is a small collection electrode monolithic active pixel sensor produced in the Tower 180 nm complementary metal-oxide-semiconductor imaging process. Modifications to the sensor’s periphery, mainly in the hit merger, are studied in order to optimize the performance for tracking and calorimetry. This optimization is of special interest as part of the MALTA3 sensor redesign in the 65 nm Tower Partners Semiconductor Co. process.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1975

A finite-difference analysis of the nozzle starting process in an expansion tunnel

A suitable finite-difference method for computing the quasi-one-dimensional unsteady flow in an expansion tunnel nozzle was identified. The difference equations are presented along with the appropriate stability limits. A parametric study of the starting process in an expansion tunnel nozzle is made, and acceptable operating conditions were determined.

Weilmuenster, K. J.↗

DOE OSTI2024

Exploration with Scalable Gaussian Process Reinforcement Learning

Exploration is a challenging problem in reinforcement learning (RL), especially in environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has been shown to be paramount for successful exploration [Osband et al., 2018]. Bayesian, or approximately Bayesian, methods present a principled means of estimating the parametric uncertainty in RL problems. Gaussian processes, nonparametric Bayesian models, are often impractical due to poor scalability and computational bottlenecks. We introduce a scalable Gaussian process RL (GPRL) method which directly induces sparsity in the covariance matrix to facilitate faster computation. This is a departure from previous GPRL methods which instead rely on data reduction and subsampling. We compare various covariance-based exploration techniques (Thompson sampling, upper confidence bound, and probabilistic maximum variance) which leverage our scalable GP framework in sparse reward environments. Finally, we show favorable comparison against the bootstrapped deep Q-Network.

97 MATHEMATICS AND COMPUTING↗