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At least 127 records · Page 7

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NASA NTRS1985

Thermal barrier coatings for the space shuttle main engine turbine blades

The Space Shuttle Main Engine (SSME) turbopump turbine blades experience extremely severe thermal shocks during start-up and shut-down. For instance, the high pressure fuel turbopump turbine which burns liquid hydrogen operates at approximately 1500 F, but is shut down fuel rich with turbine blades quenced in liquid hydrogen. This thermal shock is a major contributor to blade cracking. The same thermal shock cause the protective ZrO2 thermal barrier coatings to spall or flake off, leaving only the NiCrAlY bond coating which provides only a minimum thermal protection. The turbine blades are therefore life limited to about 3000 sec for want of a good thermal barrier. A suitable thermal barrier coating (TBC) is being developed for the SSME turbine blades. Various TBCs developed for the gas turbine engines were tested in a specially built turbine blade tester. This tester subjects the coated blades to thermal and pressure cycles similar to those during actual operation of the turbine. The coatings were applied using a plasma spraying techniques both under atmospheric conditions and in vacuum. Results are presented. In general vacuum plasma sprayed coatings performed much better than those sprayed under atmospheric conditions. A 50 to 50 blend of Cr2O3 and NiCrAlY, vacuum plasma sprayed on SSME turbopump turbine blades appear to provide significant improvements in coating durability and thermal protection.

Bhat, B. N.↗

DOE OSTI2026

Broad frequency tuning of a Nb$_{3}$Sn superconducting microwave cavity for dark matter searches

We demonstrate a novel broad-frequency tuning mechanism for superconducting microwave cavities designed for dark matter searches. Using a Nb$_3$Sn-coated cigar-shaped cavity operating at approximately 9 GHz, we achieve continuous frequency tuning exceeding 1 GHz by mechanically separating the two cavity halves: a "tuning-by-opening" technique. Finite-element method simulations predict that radiative losses do not degrade the quality factor even for large openings, as a closed cavity with an intrinsic quality factor of $10^7$ maintains this value for apertures up to 9 mm, corresponding to a tuning range from 9.0 to 7.5 GHz. Experimental validation using both copper ring spacers and a continuous sliding mechanism confirms $Q_0$ values exceeding the dark matter quality factor across the entire explored frequency range, despite mechanical imperfections and film non-uniformities. This tuning approach avoids inserting elements into the resonant volume, making it particularly suitable for high-Q superconducting cavities in axion haloscope experiments and readily applicable to REBCO-based implementations capable of operating in multi-tesla magnetic fields.

Maiello, D. [Padua U.; INFN, Padua] (ORCID:0009000↗

NASA NTRS2007

Mo/Ti Diffusion Bonding for Making Thermoelectric Devices

An all-solid-state diffusion bonding process that exploits the eutectoid reaction between molybdenum and titanium has been developed for use in fabricating thermoelectric devices based on skutterudite compounds. In essence, the process is one of heating a flat piece of pure titanium in contact with a flat piece of pure molybdenum to a temperature of about 700 C while pushing the pieces together with a slight pressure [a few psi (of the order of 10 kPa)]. The process exploits the energy of mixing of these two metals to form a strong bond between them. These two metals were selected partly because the bonds formed between them are free of brittle intermetallic phases and are mechanically and chemically stable at high temperatures. The process is a solution of the problem of bonding hot-side metallic interconnections (denoted hot shoes in thermoelectric jargon) to titanium-terminated skutterudite n and p legs during the course of fabrication of a unicouple, which is the basic unit cell of a thermoelectric device (see figure). The hot-side operating temperature required for a skutterudite thermoelectric device is 700 C. This temperature precludes the use of brazing to attach the hot shoe; because brazing compounds melt at lower temperatures, the hot shoe would become detached during operation. Moreover, the decomposition temperature of one of the skutterudite compounds is 762 C; this places an upper limit on the temperature used in bonding the hot shoe. Molybdenum was selected as the interconnection metal because the eutectoid reaction between it and the titanium at the ends of the p and n legs has characteristics that are well suited for this application. In addition to being suitable for use in the present bonding process, molybdenum has high electrical and thermal conductivity and excellent thermal stability - characteristics that are desired for hot shoes of thermoelectric devices. The process takes advantage of the chemical potential energy of mixing between molybdenum and titanium. These metals have a strong affinity for each other. They are almost completely soluble in each other and remain in the solid state at temperatures above the eutectoid temperature of 695 C. As a result, bonds formed by interdiffusion of molybdenum and titanium are mechanically stable at and well above the original bonding temperature of about 700 C. Inasmuch as the bonds are made at approximately the operating temperature, thermomechanical stresses associated with differences in thermal expansion are minimized.

Sakamoto, Jeffrey↗

DOE OSTI2021

Approximation of periodic Green's operator in real space using numerical integration and its use in fast Fourier transform-based micromechanical models

In this work, we propose an expression for the periodic first derivative of Green's function in real space. The proposed expression allows an alternative way of computing the periodic Green's operator based on periodically summing the free-space Green's operator in terms of an appropriate quadrature rule. We provide computational examples, which show the accuracy of the proposed approach, together with reduced spurious oscillations in the solution fields.

42 ENGINEERING↗

DOE OSTI2025

Simultaneously Improving Process Efficiency and Mechanical Properties in Aluminum Alloys with Applied Magnetic Fields

In the aluminum heat treatment sector, large resistive batch furnaces are used for many heat treatment processes and are generally always running, operating at approximately 20–30% efficiency. The primary avenue for energy savings lies in shortening heat treatment times, which can be 24+ hours for solutionizing (450–625 °C) and aging treatments (150–250 ℃), depending on the alloy and specification requirements. The application of high magnetic fields (<9 T) has shown to significantly reduce aluminum alloy heat treatment times, while achieving enhanced mechanical properties. Here, we explore the effects of thermomagnetic processing on the heat treatment response of a commercial aluminum alloy A390.1. Heat treatment times were reduced by >80%, while achieving ~10% improvement in microhardness, and these improvements persisted after longer thermal exposures simulating use. The microstructural evolution and resulting properties will be discussed.

Kesler, Michael [ORNL] (ORCID:0000000161423128)↗

DOE OSTI2025

An apologia for islands

Entanglement islands have played a key role in the recent derivation of the Page curve and other progress on the black hole information problem. Arising from the inclusion of connected wormhole saddles in a gravitational replica trick, islands signal that degrees of freedom in the black hole interior are not microscopically independent of the exterior Hawking radiation. Islands were originally discovered in the context of AdS/CFT coupled to an external, nongravitating reservoir, where the coupling gives graviton excitations an anomalous boundary scaling dimension (or “mass”). It has been claimed in the literature that this mass is crucial for the existence of islands and even the Page curve itself. In this paper, however, we explain how entanglement islands can also appear in setups with massless gravitons and no external reservoir, giving a number of examples including the entanglement wedges of boundary CFT regions, of radiation at null infinity in asymptotically flat spacetimes, and of radiation inside a semiclassical but gravitating spacetime. In each case, the Page curve is physically observable and can be determined with sufficiently careful experiments on many copies of the black hole. We give general arguments for the existence of gauge-invariant operators in gravity which are compactly supported to all orders in perturbation theory (whenever no isometries of the background spacetime exist) and refine a recently-proposed explicit construction of such operators. When applied to islands, these results — together with entanglement wedge reconstruction — guarantee that semiclassical operators in the island can be approximated by nonperturbative operators on the Hawking radiation.

AdS-CFT Correspondence↗

DOE OSTI2023

Learning subgrid-scale models with neural ordinary differential equations

We propose a new approach to learning the subgrid-scale model when simulating partial differential equations (PDEs) solved by the method of lines and their representation in chaotic ordinary differential equations, based on neural ordinary differential equations (NODEs). Solving systems with fine temporal and spatial grid scales is an ongoing computational challenge, and closure models are generally difficult to tune. Machine learning approaches have increased the accuracy and efficiency of computational fluid dynamics solvers. In this approach neural networks are used to learn the coarse- to fine-grid map, which can be viewed as subgrid-scale parameterization. We propose a strategy that uses the NODE and partial knowledge to learn the source dynamics at a continuous level. Our method inherits the advantages of NODEs and can be used to parameterize subgrid scales, approximate coupling operators, and improve the efficiency of low-order solvers. Numerical results with the two-scale Lorenz 96 ODE, the convection-diffusion PDE, and the viscous Burgers' PDE are used to illustrate this approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

The use of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Beyond Idealized Models of Nanoscale Metal Hydrides for Hydrogen Storage

Metal hydrides are attractive for compact, low-pressure hydrogen storage, yet a foundational understanding of factors governing their thermodynamics and kinetics is still lacking. Predictive modeling from the atomic to the microstructural scale plays a critical role in addressing these gaps, particularly for nanoscale materials, which promise improved performance but are difficult to probe. In this paper, we summarize strategies being developed within the Hydrogen Materials—Advanced Research Consortium (HyMARC) for going beyond conventional models to incorporate more complex physics, more realistic structures, and better approximation of operation conditions in simulations of nanoscale metal hydrides. We highlight four beyond-ideal factors that influence predicted performance: (1) surface anharmonic dynamics, (2) interface and surface energy penalties, (3) mechanical stress under confinement, and (4) the presence of native surface oxide. Approaches for addressing these factors are demonstrated on model materials representative of high-capacity hydrogen storage systems, and implications for understanding performance under operating conditions are discussed.

08 HYDROGEN↗

DOE OSTI2026

Nonlinear thermodynamic computing out of equilibrium

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.

Whitelam, Stephen [Lawrence Berkeley National Labo↗

DOE OSTI2021

Evaluation of a high-performance storage buffer with 3D XPoint devices for the DUNE data acquisition system

The DUNE detector is a neutrino physics experiment that is expected to take data starting from 2028. The data acquisition (DAQ) system of the experiment is designed to sustain several TB/s of incoming data which will be temporarily buffered while being processed by a software based data selection system. In DUNE, some rare physics processes (e.g. Supernovae Burst events) require storing the full complement of data produced over 1-2 minute window. These are recognised by the data selection system which fires a specific trigger decision. Upon reception of this decision data are moved from the temporary buffers to local, high performance, persistent storage devices. In this paper we characterize the performance of novel 3DXPoint SSD devices under different workloads suitable for high-performance storage applications. We then illustrate how such devices may be applied to the DUNE use-case: to store, upon a specific signal, 100 seconds of incoming data at 1.5 TB/s distributed among 150 identical units each operating at approximately 10GB/s.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

From disorganized data to emergent dynamic models: Questionnaires to partial differential equations

Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate the data-driven derivation of parameter dependent, evolutionary partial differential equation (PDE) models capable of generating the data. This tensor type of data is reminiscent of shuffled (multidimensional) puzzle tiles. The independent variables for the evolution equations (their “space” and “time”) as well as their effective parameters are all emergent , i.e. determined in a data-driven way from our disorganized observations of behavior in them. We use a diffusion map based questionnaire approach to build a smooth parametrization of our emergent space/time/parameter space for the data. This approach iteratively processes the data by successively observing them on the “space,” the “time” and the “parameter” axes of a tensor. Once the data become organized, we use machine learning (here, neural networks) to approximate the operators governing the evolution equations in this emergent space. Our illustrative examples are based (i) on a simple advection–diffusion model; (ii) on a previously developed vertex-plus-signaling model of Drosophila embryonic development; and (iii) on two complex dynamic network models (one neuronal and one coupled oscillator model) for which no obvious smooth embedding geometry is known a priori. This allows us to discuss features of the process like symmetry breaking, translational invariance, and autonomousness of the emergent PDE model, as well as its interpretability.

generative models↗

DOE OSTI2024

Muon capture on Li 6 , C 12 , and O 16 from ab initio nuclear theory

Muon capture on nuclei is one of the most promising probes of the nuclear electroweak current driving the yet-hypothetical neutrinoless double-beta (0νββ) decay. Both processes involve vector and axial-vector currents at finite momentum transfer, q ~ 100 MeV, as well as the induced pseu doscalar and weak-magnetism currents. Comparing measured muon-capture rates with reliable ab initio nuclear-theory predictions could help us validate these currents. To this end, we compute partial muon-capture rates for 6 Li, 12 C and 16 O, feeding the ground and excited states in 6 He, 12 B and 16 N, using ab initio no-core shell model with two- and three-nucleon chiral interactions. Here, we remove the spurious center-of-mass motion by introducing translationally invariant operators and approximate the effect of hadronic two-body currents by Fermi-gas model. We solve the bound-muon wave function from the Dirac wave equations in the Coulomb field created by a finite nucleus. We find that the computed rates to the low-lying states in the final nuclei are in good agreement with the measured counterparts. We highlight sensitivity of some of the transitions to the sub-leading three-nucleon interaction terms. We also compare summed rates to several tens of final states with the measured total capture rates and note that we slightly underestimate the total rate with this simple approach due to limited range of excitation energies

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Tissue scale agent-based simulation of premalignant progressions in Barrett’s esophagus

Barrett’s esophagus (BE) is a benign condition of the distal esophagus that initiates a multistage pathway to esophageal adenocarcinoma (EAC). Short of frequent intrusive (and costly) surveillance, effective screening for neoplasia in BE populations is yet to be established since progressors are rare and virtually undetectable without routine biopsies, which often sample only a small portion of the BE tissue. As a result, reliable estimation of the true prevalence of dysplasia in a BE population and evidence-based optimization of screening for at-risk individuals is challenging. Data-driven microsimulations, i.e., model-generated instances of disease history in a predefined virtual population, have found utility in the EAC screening literature as low-overhead alternatives to real-world hypothesis testing of optimal interventions for dysplasia. Despite the successes, computational limitations, paucity of knowledge and data on Barrett’s dysplasia, and the complexities of disease progression as a multiscale multiphysics process have hindered the treatment of disease progression in BE as a spatial process. Agent-based modeling of nucleation and proliferation processes in dysplasia warrants exploration in this context as an approximation that operates at a trade-off between computational tractability and precise representation of the composition and physics of the substrate (tissue). In this study, we describe spatially resolved simulations of premalignant progression toward EAC in a coarse-grained model of Barrett’s tissue that resolves the metaplastic tissue at a length scale of 0.42 mm (~3300 crypts/mm 2 ). Finally, the model is calibrated to reproduce historical high-grade dysplasia prevalence when model-generated patients are screened using the Seattle protocol.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Quark Sivers Function at Small-$x$: Leading contribution from the Spin-Dependent Odderon

We present the calculation of the leading contribution to the quark Sivers function at small-Bjorken x as in [1]. This calculation uses the high energy scattering approximation and operator formalism developed in [2,3] to obtain a dominant contribution to the quark Sivers function coming from the spin-dependent odderon, in agreement with the results of [4]. We then calculate this dominant contribution in the diquark model of the proton to obtain a small-x estimate for the Sivers function.

Santiago, M. Gabriel↗

DOE OSTI2020

Mesocarnivore Surveys on Lawrence Livermore National Laboratory Site 300 Alameda and San Joaquin Counties, California

Lawrence Livermore National Laboratory (LLNL), operated under cooperative agreement between the University of California and the U. S. Department of Energy, administers and operates an approximately 11 mi2 (2$ km2) test site in the remote hills at the northern end of the South Coast Ranges of Central California. Known as Site 300, this expanse of rolling hills and canyons supports a diverse array of grassland communities typical of lowland central California. The facility serves a variety of functions related to testing non-nuclear explosives, lasers, and weapons subsystems. The primary purpose of this project was to determine the presence of any mesocamivores on Site 300 that use the property for foraging, denning, and other related activities. The surveys occurred from mid-September to mid-October, 2002.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Bolt: A Fast Solver for Kinetic Theories Using a High-Resolution Constrained Transport Scheme [Slides]

Our understanding of collisionless and semi-collisional plasmas in the nonlinear regime is limited by the expense of computing solutions numerically. Bolt is a fast, GPU-accelerated code for rapidly computing such solutions with accurate transport and an approximate collision operator. Such calculations are relevant to both problems in astrophysics, such as heat conduction and magnetic reconnection in accretion disks around black holes, and also to programmatic interests at LANL. The bolt code paper, demonstrating accuracy via a suite of test problems calculated on kodiak, is currently in preparation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

ProtoDUNE-SP's Performance, Physics status, and Future Plans

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber measuring$ 7.2 \times 6.1 \times 7.0 m^3$ in active volume. It is designed as a test bed and full-scale prototype for the elements of the first far detector module of the Deep Underground Neutrino Experiment (DUNE). Located at the CERN Neutrino Platform, the detector was exposed to a tagged and momentum-analyzed particle beam with momentum settings ranging from 0.3 GeV/c to 7 GeV/c and collected more than four million beam events. Additionally, the detector operated for approximately two years, continuously collecting cosmic ray events. We present the performance of the detector which has met or surpassed the specifications set for the DUNE far detector. The status of physics analyses including hadron-Ar cross sections and measurements of liquid argon properties will be summarized. Talk will conclude with plans for the coming Run2 with ProtoDUNE-HD (horizontal drift).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗