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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Feasibility of main thermal ion heating by ICRF waves using a top launcher in a tokamak with deuterium–tritium plasmas

A scenario of ion cyclotron range of frequency (ICRF) wave injection from a top launcher is proposed as an efficient and direct heating method for thermal deuterium ions in deuterium–tritium tokamak plasmas. Positioned between the tritium cyclotron layer and ion–ion hybrid layer, the top launcher allows effective wave penetration to the ion–ion hybrid layer and enables significant power transfer to thermal deuterium. This is achieved through favorable wave polarization for fundamental cyclotron damping. There is a Doppler broadening around the cyclotron resonance and this overlaps with the ion–ion hybrid layer. Low toroidal mode numbers and ion temperature in the range of 5–20 keV are favorable for enhancing the main ion damping relative to electron damping. In contrast to the neutral beam injection, which penetration strongly depends on machine size and plasma density, the proposed ICRF-based direct ion heating scenario is shown to be scalable and applicable to both larger and smaller tokamak devices within practical constraints.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Large Single Crystals of V 1– x Mo x O 2 from a Two-Step Chemical Vapor Transport Synthesis

This article details the synthesis of single crystals with the chemical formulas V 1-x Mo x O 2 ( 0 ≤ x ≤ 0.6) using the chemical vapor transport method at high temperatures (850 to 950 °C). These compounds are of interest due to the special relationship between their structure and electronic properties. Understanding this relationship will help guide future efforts in designing energy-saving devices. While some of these compounds have been synthesized before, this is the first time their synthesis has been reported for such a wide range of compounds in the form of bulk single crystals. The single crystal form is required for detailed characterization of the compounds. The materials were characterized chemically and structurally in order to validate the precise chemical compositions that were reported. Characterization methods included x-ray diffraction, inductively-coupled plasma atomic emission spectroscopy, and electron dispersive spectroscopy. The synthesis method is tunable, and the introduction of multiple steps both overcome the previous chemical challenges precluding synthesis of high molybdenum content crystals and allow for the creation of larger single crystals that were otherwise possible. The crystals synthesized this way are suitable for specialized measurements for determining the precise physical properties and atomic structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Leveraging on-shell interference to search for FCNCs of the top quark and the Z boson

Abstract Flavour-changing-neutral currents (FCNCs) involving the top quark are highly suppressed within the Standard Model (SM). Hence, any signal in current or planned future collider experiments would constitute a clear manifestation of physics beyond the SM. We propose a novel, interference-based strategy to search for top-quark FCNCs involving the Z boson that has the potential to complement traditional search strategies due to a more favourable luminosity scaling. The strategy leverages on-shell interference between the FCNC and SM decay of the top quark into hadronic final states. We estimate the feasibility of the most promising case of anomalous tZc couplings using Monte Carlo simulations and a simplified detector simulation. We consider the main background processes and discriminate the signal from the background with a deep neural network that is parametrised in the value of the anomalous tZc coupling. We present sensitivity projections for the HL-LHC and the FCC-hh. We find an expected 95% CL upper limit of $${\mathcal {B}}_{\textrm{excl}}(t\rightarrow Zc) = 6.4 \times 10^{-5}$$ B excl ( t → Z c ) = 6.4 × 10 - 5 for the HL-LHC. In general, we conclude that the interference-based approach has the potential to provide both competitive and complementary constraints to traditional multi-lepton searches and other strategies that have been proposed to search for tZc FCNCs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO 2 working capacity and CO 2 /N 2 selectivity properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

What's in a Name? Developing a Standardized Taxonomy for HVAC System Faults

Faults occurring in heating, ventilation and air-conditioning (HVAC) systems have significantly negative impacts on building energy consumption, occupant comfort, and indoor air quality. In the past thirty years, extensive research has been conducted on fault detection and diagnostics (FDD) methods, and there are now dozens of commercially available FDD software tools. Growing adoption of FDD tools has the potential to generate a massive and useful data set on fault characteristics. However, the lack of a unifying taxonomy is a significant barrier to efficient analysis and evaluation of FDD outputs. Therefore, there is a strong need to develop a robust taxonomy which can better represent and interpret FDD output data. This paper documents the development of a unifying taxonomy for HVAC system faults in commercial buildings, with initial focus on air handling units, variable air volume terminal units, and roof top unit systems. The developed fault taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model as tools to better understand and support root cause analysis for HVAC faults. A variable air volume terminal unit is used as an example to demonstrate the application of the developed fault taxonomy. The taxonomy has short-term application in a major U.S. study on fault prevalence, and promises longer term benefits to FDD software developers and building operators by creating a foundation for improved approaches to identifying and resolving HVAC faults.

Chen, Yimin↗
DOE OSTI2023

Search for Rare Processes: Mu2e and KOTO

The Standard Model (SM) of particle physics has been very successful in describing the fundamental forces, the properties of elementary particles, and predicting particles prior to their discovery, for example the top quark and the Higgs boson. Yet the SM does not explain all phenomena, and discoveries of physics processes beyond the SM will allow progress in our understanding of the fundamental nature of matter and forces. The search for rare processes is one of the paths to look for new physics. Two of these searches are for muon to electron conversion and the decay $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$. Our main effort was in the Mu2e experiment under construction at Fermilab, which has the goal of searching for the neutrinoless conversion of a muon into an electron at a single event sensitivity of 2.87 x 10-17 and, in the process, provide evidence of physics processes beyond the Standard Model. We developed the firmware to be used in the Mu2e tracker readout, assisted in the construction and operation of the Mu2e Vertical Slice Test, and provided input to studies of cosmic event reconstructions in Mu2e using simulated data. Under this reward we also concluded our effort in two KOTO experiment in J-PARC, Japan and set a limit on the rate of the rare decay, $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$ using the data collected in the 2016-2018 period. We also maintained and operated the DAQ system used for data collection thru 2021.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Design principles and physical properties of two-dimensional heterostructured borides

Principles of design to create dynamically stable transition metal-, lanthanide-, and actinide-based low-dimensional borides are presented. A charge transfer analysis of donor metal atoms to electron deficient honeycombed B lattices allows to predict complex covalent heterostructures hosting Dirac states. The applicable guidelines are supported with the analysis of phonon spectra computed with first-principles calculations to demonstrate the physical stability of nanometer-thick heterostructures. Similar or dissimilar layered borides can be stacked on top of each other in a layer-by-layer fashion creating compounds that can be fundamentally different from the individual layers, opening a rich playground to explore novel physical properties and new materials. Functionalities such as multiple Dirac states, highly dispersive electronic bands, and decoupled acoustic-optical phonon branches are studied. The combination of appealing electronic properties and physical realization makes of predicted layered borides promising materials to integrate a new generation of two-dimensional materials.

2-dimensional systems↗
DOE OSTI2020

Time resolved radiographs and fuel density of the stagnation phase of Inertial Confinement Fusion implosions (NSA report)

This report contains a figure and caption. Left: Compton radiographs of a 3-shock HDC THD layered implosion recorded 10ps before and 160ps after bang-time (BT) at the NIF, using >50keV point projection backlighters generated by irradiating a 25μm-diameter Au wire with 30ps-long ARC pulses. Right: Reconstructed density distributions of fuel and remaining ablator showing significant 3D structure and top-down asymmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Automated Disposal Debris Shield (ADDS) Test Stand, Version 2.0

One of the major refurbishing efforts mentioned in the National Ignition Facility (NIF) sustainability plan is improvements to prevent the debris generated during shots from reaching the high value optics. Addressing this issue will allow NIF to continue high powered shots while maintaining a manageable optic exchange rate. A major area of concern is right outside the Target Chamber in the Final Optics Assembly (FOA) where we have the Grating Debris Shield (GDS) being protected from target debris by the Automated Disposable Debris Shields (ADDS). The area that houses the ADDS will be referred to as the ADDS Doghouse. The schematic illustration of a single NIF beamline with the general area of the ADDS Doghouse outlined in red can be seen in Figure 1. Discovering methods to improve the ADDS Doghouse in preventing debris from passing through is top priority.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Search for top squarks in final states with many light-flavor jets and 0, 1, or 2 charged leptons in proton-proton collisions at $\sqrt{s}=13$ TeV

Several new physics models including versions of supersymmetry (SUSY) characterized by R-parity violation (RPV) or with additional hidden sectors predict the production of events with top quarks, low missing transverse momentum, and many additional quarks or gluons. The results of a search for top squarks decaying to two top quarks and six additional light-flavor quarks or gluons are reported. The search employs a novel machine learning method for background estimation from control samples in data using decorrelated discriminators. The search is performed using events with 0, 1, or 2 electrons or muons in conjunction with at least six jets. No requirement is placed on the magnitude of the missing transverse momentum. The result is based on a sample of proton-proton collisions at $\sqrt{s}=13$ TeV corresponding to 138 fb −1 of integrated luminosity collected with the CMS detector at the LHC in 2016–2018. With no statistically significant excess of events observed beyond the expected contributions from the standard model, the data are used to determine upper limits on the top squark pair production cross section in the frameworks of RPV and stealth SUSY. Models with top squark masses less than 700 (930) GeV are excluded at 95% confidence level for RPV (stealth) SUSY scenarios.

Hadron-Hadron Scattering↗
DOE OSTI2021

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.

Chan, Jay↗
DOE OSTI2021

Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models

Abstract. In the current global climate models (GCMs), the nonlinearity effect of subgrid cloud variations on the parameterization of warm-rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm-rain process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in situ measurements from a recent field campaign and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that tends to make EF < 1. This effect is especially strong at cloud top, where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc is significantly smaller than its counterpart that accounts only for the subgrid variation of qc, especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A neural network for beam background decomposition in Belle II at SuperKEKB

Here, we describe a neural network for predicting the background hit rate in the Belle II detector produced by the SuperKEKB electron-positron collider. The neural network, BGNet, learns to predict the individual contributions of different physical background sources, such as beam-gas scattering or continuous top-up injections into the collider, to Belle II sub-detector rates. The samples for learning are archived 1 Hz time series of diagnostic variables from the SuperKEKB collider subsystems and measured hit rates of Belle II used as regression targets. We test the learned model by predicting detector hit rates on archived data from different run periods not used during training. We show that a feature attribution method can help interpret the source of changes in the background level over time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Aiming for tops of ALPs with a muon collider

Future muon colliders with center-of-mass energy of $\mathcal{O}$ (1 – 10) TeV can provide a clean high-energy environment with advantages in searches for TeV-scale axion-like particles (ALPs), pseudo-Nambu-Goldstone bosons associated with spontaneously broken global symmetries, which are widely predicted in physics beyond the Standard Model (SM). We exploit ALP couplings to SM fermions, and guided by unitarity constraints, build a search strategy focusing on the ALP decay to top quark pairs at muon colliders. It is found that a large parameter space of TeV-scale ALPs with TeV-scale decay constants can be probed by utilizing the ALP-top quark coupling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗