Fast hybrid particle-in-cell technique for pulsed-power accelerators
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70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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Results for “Hybrid techniques”
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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Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers
The current practice for fusion scientists running first principle simulations on high performance computing plat-forms is to either run their simulations and output their data for post-hoc analysis, or to place in situ analytics into their code. In this paper we examine a complex workflow using XGC fusions simulation run on the Oak Ridge Leadership Computing Facility's supercomputer Summit, which also involve three anal-yses as part of the results necessary for scientific discovery. We discuss the challenges faced when implementing these algorithms and present an original hybrid staging technique to help enable the physicists to make discoveries during the execution of the simulation. By creating this infrastructure, we can examine complicated physics results, which may not have been possible without the infrastructure. For example, our work enables the online visualization of turbulent homoclinic tangle around the magnetic X-point, breaking the last confinement surface. This visualization could help fusion scientists to better understand and improve the turbulence spread of plasma exhaust heat, which is crucial toward realizing plasmas beyond the currently accessible physics regimes of present-day tokamak reactors. The physics of turbulent homoclinic tangle will be reported in a future physics publication, by utilizing the original online analysis/visualization framework presented in this paper.
Suchyta, Eric↗
Joining Techniques for Novel Metal Polymer Hybrid Heat Exchangers
In the United States, over 50% of the unrecovered energy from industrial processes is in the form of low-grade heat (< 220°C). Materials and maintenance costs of common heat exchangers are typically too high to justify their usage. Polymers, though more affordable, are usually unsuitable for HX applications due to their low thermal conductivity (~0.2 W/mK). Here, we show that metal-polymer hybrids may be attractive from both performance and cost perspectives. The use of polymers further increases the resistance to corrosion by sulfuric and carbonic acids often present in flue gases. An ongoing work explores different configurations of layered polyimide-copper macroscale hybrids for heat exchanger applications using numerical simulations. This paper explores a manufacturing pathway for producing such layered hybrid tubes that involves directly rolling and bonding tapes made of polymer and copper foil into tubes. A critical problem in the fabrication process is the bonding of metal and polymers. We explore approaches involving adhesives (epoxy, acrylic and silicone) for metal/polymer interfaces and direct welding (ultrasonic) for metal/metal interfaces that can be integrated into the manufacturing process. We report characterizations of the thermomechanical properties of these joining processes. This work paves the way for realizing cost-effective manufacturing of heat exchangers for low grade waste heat recovery.
Kuntumalla, Gowtham↗
Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Abstract Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.
79 ASTRONOMY AND ASTROPHYSICS↗
Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes
Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.
42 ENGINEERING↗
Ultrasonic Additive Manufacturing
Ultrasonic additive manufacturing (UAM) is a solid-state hybrid manufacturing technique that leverages the principles of ultrasonic welding, mechanized tape layering, and computer numerical control (CNC) machining operations to create three-dimensional metal parts. This article begins with a discussion on the process fundamentals and process parameters of UAM. It then describes metallurgical aspects in UAM. The article provides a detailed description of a wide range of mechanical characterization techniques of UAM, namely tensile testing, peel testing, and pushpin testing. The article ends with information on sensor embedding.
Niyanth S, Niyanth↗
A Multiplexed Hybrid Velocimeter
Optical velocimetry systems play a crucial role in studying the dynamic behavior of materials under extreme conditions. Two prominent systems used at the Nevada National Security Site are the velocity interferometer system for any reflector(VISAR) and the photon Doppler velocimeter (PDV) system. Their ability to accurately measure surface motion ranging from 0 to > 20km=s without interacting with material flow enables experimenters to construct thermodynamic equations of state for materials of interest. A number of modifications to VISAR and PDV have taken place over the past five decades, leading to improved performance, increased recording capacity, and a reduction in operational burden. Prior efforts to develop a hybrid velocimeter combining the advantages of both PDV and VISAR into a single diagnostic did not take into account progress in PDV technology or new optical components that had not been developed. This thesis describes the design and implementation of a multiplexed hybrid velocimeter (MHV) based on time-multiplexing techniques, an optical hybrid, and polarization-maintaining fiber and components. Data from experiments, compared with current velocity systems, serve to demonstrate the hybrid's performance.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Long coir and glass fiber reinforced polypropylene hybrid composites prepared via wet-laid technique
Natural fiber composites offer an advantage in terms of weight saving for many automotive applications; however, many natural fiber composites lack properties to justify substitution for synthetic composites. Hybridizing the natural fiber composites by adding a fraction of synthetic fibers is an innovative approach to provide a balance between composite's performance and weight savings. In this study, coir fiber (40 wt%)-reinforced polypropylene (PP) composites were hybridized by substituting a fraction of coir fiber with glass fiber (0–30 wt%). The composites were prepared using a novel wet-laid technique followed by compression molding, where the fiber length is preserved. The composites prepared by hybridizing PP/coir fibers with glass fibers were light in weight (6–20% lighter compared to 40 wt% glass fiber reinforced PP) with significantly enhanced tensile (strength – 49–182%, modulus – 54–130%), flexural (strength – 41–104%, modulus – 64–193%), and impact properties (157 - 474%) compared to 40 wt% coir fiber reinforced PP composites. Furthermore, the addition of glass fiber (10–30 wt%) to coir fiber reduced the water-absorbing tendency (by 18–74%) of PP/coir fiber composites. All in all, this work has potential applications in automotive, mass transit, and truck applications where natural fiber composites are being investigated as alternatives to metal and/or fully synthetic composites.
36 MATERIALS SCIENCE↗
Techno-Economic Analysis and Market Potential of Geological Thermal Energy Storage (GeoTES) Charged With Solar Thermal and Heat Pumps into Depleted Oil/Gas Reservoirs and Shallow Reservoirs: A Technology Overview: Preprint
Depleted oil/gas reservoirs represent a waste of underground resources and investments of drilling, and also a potential risk to the earth's environment. Geologic thermal energy storage (GeoTES) is proposed as a solution to convert depleted oil/gas reservoirs into long-term seasonal energy storage. GeoTES can be hybridized with other techniques for viable commercial deployment, such as 1) concentrating solar power (CSP) collectors and 2) heat pumps with excess renewable energy. Here, a technology overview is given on the two GeoTES technologies, which includes system overview, techno-economic models, and case study. Both GeoTES technologies show great potential in the economics of individual project deployment and applicability across the US.
concentrating solar power↗
Ultra-Long Distance BOTDA Sensor System Employing Hybrid Amplification and Advanced Noise Reduction Techniques
Brillouin Optical Time Domain Analysis (BOTDA) sensor system play a pivotal role in distributed sensing, which enables precise measurements of strain and temperature across extensive fiber lengths. Nonetheless, challenges emerge as distances grow due to signal attenuation and noise interference resulting in measurement errors. This research offers a comprehensive strategy to extend the sensing range of BOTDA systems beyond 10’s of kilometers while maintaining high spatial resolutions. Such enhanced sensing is realized through the integration of distributed Raman amplification, inline amplification using erbium-doped fiber amplifiers (EDFA), and advanced noise reduction techniques. Leveraging inherent redundancy in measured data as a function of frequency and fiber distance, the non-local means (NLM) filter removes noise while preserving essential physical information. This approach proves particularly advantageous in BOTDA systems, where accurate measurement of Brillouin scattering signals is paramount for long-range sensing, while concurrently safeguarding high spatial resolutions. In summary, this research has shown a holistic exploration of extending BOTDA's distance sensing capabilities up to 150 km with spatial resolutions of 8 meters.
Bhatta, Hari↗
Parametric Estimation of the Local Stress Field: A Hybrid Combinatorial and Machine Learning Based Technique
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58 GEOSCIENCES↗
Ultra-Long Distance BOTDA Sensor System Employing Hybrid Amplification and Advanced Noise Reduction Techniques
Brillouin Optical Time Domain Analysis (BOTDA) sensor systems play a pivotal role in distributed sensing, which enables precise measurements of strain and temperature across extensive fiber lengths. This research offers a comprehensive strategy to extend the sensing range of BOTDA systems beyond tens of kilometers while maintaining high spatial resolutions. Such enhanced sensing is realized through the integration of distributed Raman amplification, inline amplification using erbium-doped fiber amplifiers (EDFA), and advanced noise reduction techniques. Optimizing Raman pump and probe wave profiles ensures the robustness of Brillouin scattering signals, effectively countering attenuation-induced losses. Additionally, enhancing weak Brillouin signals by strategically placing EDFAs at optimal fiber locations keeps sensor sensitivity high. Leveraging inherent redundancy in measured data as a function of frequency and fiber distance, the non-local means (NLM) filter removes noise while preserving essential physical information. In summary, this research has shown a holistic exploration of extending BOTDA’s distance sensing capabilities up to 150 km with spatial resolutions of 8 meters.
Bhatta, Hari↗
Single-particle detection of enhanced polarizability in Au-decorated semiconducting nanorods via scanning dielectric microscopy
Hybrid nanostructures that combine semiconducting and metallic components offer great potential for photothermal therapy, optoelectronics, and sensing, by integrating tunable optical properties with enhanced light absorption and charge transport. Boosting the integrated performance of these hybrid systems demands techniques capable of probing local variations of the physical properties inaccessible to bulk analysis. Here, we report the single-particle dielectric characterization of hybrid, semiconducting bismuth sulfide (Bi 2 S 3 ) nanorods (NR) decorated with metallic Au nanoparticles (NP), employing scanning dielectric microscopy, which uses electrostatic force microscopy in combination with finite-element numerical simulations. We reveal a pronounced enhancement in the local dielectric response of Bi2S3 upon Au decoration, attributed to interfacial polarization and electron transfer from Au to the Bi 2 S 3 matrix, thus suggesting a enhanced metallic-like polarizability at the single-particle level. Numerical simulations show that the response is dominated by the vertical component of the permittivity and that the decorating metallic Au NP produce only moderate shielding of the semiconductor Bi 2 S 3 NR core, indicating that the large increase in the dielectric response originates primarily from intrinsic modifications within the NR. Overall, these findings provide direct insight into structure–property relationships at the single-particle level, supporting the rational design of advanced hybrid nanostructures with tailored electronic functionalities.
36 MATERIALS SCIENCE↗
Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. Further the opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the hybridization techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Use of Gas Adsorption and Inversion Methods for Shale Pore Structure Characterization
The analysis of porosity and pore structure of shale rocks has received special attention in the last decades as unconventional reservoir hydrocarbons have become a larger parcel of the oil and gas market. A variety of techniques are available to provide a satisfactory description of these porous media. Some techniques are based on saturating the porous rock with a fluid to probe the pore structure. In this sense, gases have played an important role in porosity and pore structure characterization, particularly for the analysis of pore size and shapes and storage or intake capacity. In this review, we discuss the use of various gases, with emphasis on N2 and CO2, for characterization of shale pore architecture. We describe the state of the art on the related inversion methods for processing the corresponding isotherms and the procedure to obtain surface area and pore-size distribution. The state of the art is based on the collation of publications in the last 10 years. Limitations of the gas adsorption technique and the associated inversion methods as well as the most suitable scenario for its application are presented in this review. Finally, we discuss the future of gas adsorption for shale characterization, which we believe will rely on hybridization with other techniques to overcome some of the limitations.
04 OIL SHALES AND TAR SANDS↗
Atomic-resolution lithography with an on-chip scanning tunneling microscope
In this work, atomic-resolution lithography with a Microelectromechanical-System (MEMS) based Scanning Tunneling Microscope (STM) is demonstrated for the first time. The microscope consists of a commercial UltraHigh-Vacuum (UHV) STM whose regular tip is replaced with a 1-Degree-of-Freedom (1-DOF) MEMS nanopositioner. This results in a hybrid STM system where XY-plane motions are provided by the piezotube of the original system and Z-axis motion by the MEMS with a higher bandwidth. Sharp tips made of Pt or W are added to the MEMS devices with postfabrication techniques. With this hybrid system, STM-based lithography is demonstrated on an H-passivated Si (100)-2×1 sample under UHV condition. Results prove the capability of the hybrid STM system for atomic-scale lithography. This capability, paired with the small footprint of the MEMS device, makes this approach a candidate for building a high-throughput parallel STM lithography platform by incorporating an array of 1-DOF MEMS devices that perform lithography in parallel.
Alipour, Afshin (ORCID:0000000263252067)↗
Code Development Supporting a Non-Thermal Source of High Fluence Warm X-Ray
A six-month research effort has advanced the hybrid kinetic-fluid modeling capability required for developing non-thermal warm x-ray sources on Z. The three particle treatments of quasi-neutral, multi-fluid, and kinetic are demonstrated in 1D simulations of an Ar gas puff. The simulations determine required resolutions for the advanced implicit solution techniques and debug hybrid particle treatments with equation-of-state and radiation transport. The kinetic treatment is used in preliminary analysis of the non-Maxwellian nature of a gas target. It is also demonstrates the sensitivity of the cyclotron and collision frequencies in determining the transition from thermal to non-thermal particle populations. Finally, a 2D Ar gas puff simulation of a Z shot demonstrates the readiness to proceed with realistic target configurations. The results put us on a very firm footing to proceed to a full LDRD which includes continued development transition criteria and x-ray yield calculation.
36 MATERIALS SCIENCE↗
Performance of hybridized bamboo-carbon fiber reinforced polypropylene composites processed using wet laid technique
The end-of-life vehicles (ELV) regulations motivate hybrid materials usage in automotive industries to optimize properties at reduced cost and increase eco-friendly designs. This research explores hybrid compositions of natural bamboo fiber and synthetic carbon fiber. The goal of hybridization was to synergistically benefit from each constituent– i.e., sustainability, energy absorption and superior damping from bamboo, and high strength and stiffness from carbon fiber. Carbon fibers (CF), bamboo fibers (BF) and polypropylene (PP) fibers were dispersed in water to produce wet-laid hybrid mats. The mats were compression molded into consolidated panels to obtain the hybrid composite(s) (BF-CF-PP). Four formulations with different fiber-resin weight percent were designed and produced including- BF-PP (30/70), BF-PP (50/50), BF-CF-PP (32/8/60), and BF-CF-PP (8/32/60). The effect of (a) fiber length, (b) surface treatment, (c) fiber content, and (d) consolidation pressure on the mechanical properties were examined. The improved mechanical (flexural strength 76.4 MPa, flexural modulus 4.1 GPa, ILSS 12.4 MPa and impact strength 49.9 KJ/m 2 ) and vibrational damping (1.05%) properties showed that the BF-CF-PP (8/32/60) provided higher properties compared to the other variants. The projected properties at various stoichiometric ratios of carbon and bamboo fiber revealed that the properties of hybrid composites could be tailored to produce desirable, cost-effective, and sustainable automotive components.
36 MATERIALS SCIENCE↗