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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Toward fully coherent soft x-ray free-electron laser via echo-enabled harmonic generation in fourth generation synchrotron light sources

Having previously reported on bunching via echo-enabled harmonic generation (EEHG) as an effective way to improve the longitudinal coherence in the NSLS-II storage ring [X. Yang et al., Sci. Rep. 12, 9437 (2022)], we demonstrate that this EEHG scheme can be easily adopted to any fourth generation diffraction-limited synchrotron light source with significant benefits. The advantage of the scheme is that it requires no change of the lattice and is fully compatible with other beamlines. Since the EEHG performance is mainly determined by the momentum compaction, beam emittances, and beta functions of a SR lattice, we have identified these crucial parameters and successfully built a generalized model, which can predict the performance of nearly any SLS. Regarding the fourth generation SLSs, momentum compactions are often significantly smaller; thus, to cover the x rays with a photon energy of up to 1 keV, we utilize a specific design, including a 250 nm seed-laser wavelength. Our model predicts that for most of the current and future fourth generation SLSs, the EEHG scheme can produce significant prebunching up to harmonic 200 and, thus, generate a few MW scale peak power at 1.25 nm wavelength.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Generative network-based approaches to generate stochastic realizations

Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.

58 GEOSCIENCES↗
DOE OSTI2023

Impact of Wildfires on Solar Generation, Reserves and Energy Prices

Wildfire seasons in the Western U.S. become more prolonged and intense in recent years bringing significant variability and uncertainty of solar generation. It greatly challenges the bulk power system and electricity market operation managed by California Independent System Operator (CAISO) as California leads both the wildfire records and the solar power integration. This study presents a screening-level analysis of the impact of wildfires on solar generation, operating reserve and energy prices applying historical real-world wildfire and market operation data. To the best of the authors knowledge, it is a first-of-its-kind study and will lay the foundation for market impact quantification and wildfire mitigation strategies design based on projected wildfire activities in future years.

electricity price↗
DOE OSTI2021

Optimal Power System Black start using Inverter-Based Generation

Power system black start readiness is part of the system planning. Utility planners perform periodic studies to assess if their power system is capable of total restoration following a black out. Hydro and diesel generators are the most commonly used black start capable resources by power utilities. However, with increasing penetration of solar generation, inverter-based resources can be considered to provide black start capability. Since the solar inverters can be located at multiple locations throughout the power system, and in view of their unique characteristics, an optimal real-time capable plan is helpful for system operators for faster black start. Black start optimization is a multi stage mixed-integer non-linear optimization which is extremely hard to solve. In this paper, we propose an optimal black start methodology that is easier to solve and scalable in real-time. We demonstrate the proposed methodology on two test systems and illustrate how inverter-based resources can contribute and improve power system restoration.

power system restoration, blackstart, Inverter-bas↗
DOE OSTI2023

High-Resolution Synthetic Solar Irradiance Sequence Generation: An LSTM-Based Generative Adversarial Network

The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performance of inverter-based resources' primary frequency response and fast frequency response, require high-resolution renewable generation output data that are representative of renewable energy resources. This paper focuses on creating synthetic but realistic solar irradiance data and proposes a long short-term memory-based generative adversarial network to generate high-resolution (second-level) solar irradiance sequences from low-resolution (minute-level) measurements. Combined with a classifier to recognize the solar irradiance patterns, the proposed model is trained using multi-loss functions to accurately capture the temporal correlations among both high-resolution and low-resolution sequences. Verification of the proposed approach is performed on the data set of the Oahu Solar Measurement Grid collected through the National Renewable Energy Laboratory. The results of the case studies demonstrate the proposed approach's capability to capture the statistical characteristics of different solar irradiance patterns and to generate high-quality synthetic solar irradiance sequences in high resolution.

dynamic scheduling↗
DOE OSTI2022

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

Here, we propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., multiple “snapshots.” Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots. By training a physics-informed generative model that generates “fake” sample paths, we aim to fit the observed particle ensemble distributions with a curve in the probability measure space, which is induced from the inferred particle dynamics. We employ different metrics to quantify the differences between distributions, e.g., the sliced Wasserstein distances and the adversarial losses in generative adversarial networks. We refer to this method as generative “ensemble-regression” (GER), in analogy to the classic “point-regression,” where we infer the dynamics by performing regression in the Euclidean space. We illustrate the GER by learning the drift and diffusion terms of particle ensembles governed by SODEs with Brownian motions and Lévy processes up to 100 dimensions. We also discuss how to treat cases with noisy or truncated observations. Apart from systems consisting of independent particles, we also tackle nonlocal interacting particle systems with unknown interaction potential parameters by constructing a physics-informed loss function. Finally, we investigate scenarios of paired observations and discuss how to reduce the dimensionality in such cases by proving a convergence theorem that provides theoretical support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Search for pair-produced scalar and vector leptoquarks decaying into third-generation quarks and first- or second-generation leptons in $pp$ collisions with the ATLAS detector

A search for pair-produced scalar and vector leptoquarks decaying into quarks and leptons of different generations is presented. It uses the full LHC Run 2 (2015–2018) data set of 139 fb -1 collected with the ATLAS detector in proton–proton collisions at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV. Scalar leptoquarks with charge -(1/3)$e$ as well as scalar and vector leptoquarks with charge +(2/3)$e$ are considered. All possible decays of the pair-produced leptoquarks into quarks of the third generation ($t, b$) and charged or neutral leptons of the first or second generation ($e, μ, ν$) with exactly one electron or muon in the final state are investigated. No significant deviations from the Standard Model expectation are observed. Upper limits on the production cross-section are provided for eight models as a function of the leptoquark mass and the branching ratio of the leptoquark into the charged or neutral lepton. In addition, lower limits on the leptoquark masses are derived for all models across a range of branching ratios. Two of these models have the goal of providing an explanation for the recent $B$-anomalies. In both models, a vector leptoquark decays into charged and neutral leptons of the second generation with a similar branching fraction. Lower limits of 1980 GeV and 1710 GeV are set on the leptoquark mass for these two models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Selective butene formation in direct ethanol-to-C3+-olefin valorization over Zn-Y/Beta and single-atom alloy composite catalysts using in situ generated hydrogen

The selective production of C3+ olefins from renewable feedstocks, especially via C1 and C2 platform chemicals, is a critical challenge for obtaining economically viable low-carbon middle distillate transportation fuels (i.e., jet and diesel). Here, we report a multifunctional catalyst system composed of Zn-Y/Beta and “single-atom” alloy (SAA) Pt-Cu/Al2O3 which selectively catalyzes ethanol-to-olefins (C3+, ETO) valorization in the absence of cofed hydrogen, forming butenes as the primary olefin products. Beta zeolites containing predominately isolated Zn and Y metal sites catalyze ethanol upgrading steps (588 K, 3.1 kPa ethanol, ambient pressure) regardless of cofed hydrogen partial pressure (0-98.3 kPa H2), forming butadiene as the primary product (60% selectivity at 87% conversion). The Zn-Y/Beta catalyst possesses site-isolated Zn and Y Lewis acid sites (at ~7 wt% Y) and Brønsted acidic Y sites, the latter of which has been previously uncharacterized. A secondary bed of SAA Pt-Cu/Al2O3 selectively hydrogenates butadiene to butene isomers at a consistent reaction temperature using hydrogen generated in situ from ethanol-to-butadiene (ETB) conversion. This unique hydrogenation reactivity at near-stoichiometric hydrogen and butadiene partial pressures is not observed over monometallic Pt or Cu catalysts, highlighting these operating conditions as a critical SAA catalyst application area for conjugated diene selective hydrogenation at high reaction temperatures (>573 K) and low H2/diene ratios (e.g., 1:1). Single-bed steady state selective hydrogenation rates, associated apparent hydrogen and butadiene reaction orders, and DFT calculations of the Horiuti-Polanyi reaction mechanisms indicate that the unique butadiene selective hydrogenation reactivity over SAA Pt-Cu/Al2O3 reflects lower hydrogen scission barriers relative to monometallic Cu surfaces and limited butene binding energies relative to monometallic Pt surfaces. DFT calculations further indicate the preferential desorption of butene isomers over SAA Pt-Cu(111) and Cu(111) surfaces while Pt(111) surface favors subsequent butene hydrogenation reactions to form butane over butene desorption events. Under operating conditions without of hydrogen cofeeding, this combination of Zn-Y/Beta and SAA Pt-Cu catalysts can selectively form butenes (65% butenes, 78% C3+ selectivity at 94% conversion) and avoid butane formation using only in situ generated hydrogen, avoiding costly hydrogen cofeeding requirements that hinder many renewable energy processes.

Cordon, Michael↗
DOE OSTI2021

Connectivity-informed drainage network generation using deep convolution generative adversarial networks

Abstract Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb’s model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.

42 ENGINEERING↗
DOE OSTI2020

Coherent high-power RF wakefield generation by electron bunch trains in a metamaterial structure

In this paper, we present an experimental study of coherent high-power wakefield generation in a metamaterial (MTM) structure at 11.7 GHz by 65 MeV electron bunch trains at the Argonne Wakefield Accelerator (AWA), following a previous experiment, the Stage-I experiment, at the AWA. Both the Stage-II experiment, reported in this paper, and the Stage-I experiment were conducted using MTM structures, which are all-metal periodic structures with the period being much smaller than the wavelength. Differences between the two experiments include (1) structure length (Stage-I 8 cm and Stage-II 20 cm); (2) number of bunches used to excite the structure (Stage-I with two bunches, up to 85 nC of total charge; Stage-II with eight bunches, up to 224 nC of total charge); and (3) highest peak power measured (Stage-I 80 MW in a 2 ns pulse and Stage-II 380 MW in a 10 ns pulse). High-power radio frequency pulses were generated by reversed Cherenkov radiation of the electron beam due to the negative group velocity in the MTM structures. Because the radiation is coherent, a train of bunches with a proper spacing can build up to achieve a high peak power. The observed output power levels are very promising for future applications in direct collinear wakefield acceleration or in transfer to a second accelerator for two-beam acceleration.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Frequency comb generation in WGM microsphere based generators for telecommunication applications

We review the frequency comb generation process, main microresonator parameters such as free spectral range (FSR) and Q-factor, previously used optical frequency comb (OFC) generator parameters and resulting frequency combs, as well as the implementation of OFC for optical data transmission. An optical frequency comb is produced in a setup based on a tapered fibre and a SiO{sub 2} microsphere. The generated frequency comb has a frequency spacing of 2 nm or 257 GHz. During the fabrication of a tapered fibre from SMF28, use is made of the transmission signal to control the taper pulling process. The final measured tapered fibre transmission is ∼96%. A microsphere whispering gallery-mode resonator (WGMR), exhibiting a Q-factor of at least 2 × 10{sup 7}, is fabricated from an optical fibre with a thicker core than SSMF. Moreover, for future experiments, a frequency comb generator based on a free-space setup consisting of lenses, a prism, and a microsphere is developed, and the Q-factor dependence on different distances between the prism and the microsphere is investigated. (optical components of fibre-optic communication lines)

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterizing particle-based thermal storage performance using optical methods for use in next generation concentrating solar power plants

Concentrating Solar Power (CSP) generation is an attractive option for low-emission power generation; however, the high costs of thermal storage associated with concentrating solar create a large barrier for their use and adaptation into modern life. Lowering their operation costs, while maintaining high thermal storage and transfer performance is essential. Solid particle-based heat exchange systems can reduce CSP cost but are often less efficient. Efforts to increase their performance have led to use of binary size particle mixes. Presented is an optical-based thermal analysis technique used to measure near-wall thermal conductivity of particle beds essential in determining their heat exchanger efficiency. Modulated Photothermal Radiometry is used to make dynamic temperature measurements, allowing for the extraction of the most relevant thermal properties like thermal conductivity, specific heat, and effusivity. The system uses a modulated laser source causing a damped periodic heat flux, resulting in a frequency and thermal property dependent surface temperature, of which is measured using radiometry. Lock-In techniques are used to extrapolate the amplitude of the signal. Plotting the amplitude against the root angular frequency allows for effusivity measurement by ratio to a known sample. Using specific heat measurements from literature and density measurements, the thermal conductivity of the particle mixes can be calculated. The simplicity of MPTR to probe through the depth of the bed is ideal for use in CSP for dynamic thermal performance monitoring.

Corona, Javier↗
DOE OSTI2026

ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation

This dataset accompanies the publication "ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation". This paper introduces a new AI model for protein sequence generation. This dataset contains data related to experiments discussed in the publication. This includes generated sequences and evaluation metrics supporting all unconditional and bias-controlled experiments in the ProtNHF paper.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High-efficiency second harmonic generation of blue light on thin-film lithium niobate

The strength of interactions between photons in a χ (2) nonlinear optical waveguide increases at shorter wavelengths. These larger interactions enable coherent spectral translation and light generation at a lower power, over a broader bandwidth, and in a smaller device: all of which open the door to new technologies spanning fields from classical to quantum optics. Stronger interactions may also grant access to new regimes of quantum optics to be explored at the few-photon level. One promising platform that could enable these advances is thin-film lithium niobate (TFLN), due to its broad optical transparency window and possibility for quasi-phase matching and dispersion engineering. In this Letter, we demonstrate second harmonic generation of blue light on an integrated thin-film lithium niobate waveguide and observe a conversion efficiency of η 0 = 33, 000%/W- cm 2 , significantly exceeding previous demonstrations.

Park, Taewon↗
DOE OSTI2021

Superconducting RF Gun with High Current and the Capability to Generate Polarized Electron Beams

High-current low-emittance CW electron beams are indispensable for nuclear and high-energy physics fixed target and collider experiments, cooling high energy hadron beams, generating CW beams of monoenergetic X-rays (in FELs) and gamma-rays (in Compton sources). Polarization of electrons in these beams provides extra value by opening a new set of observables and frequently improving the data quality. We report on the upgrade of the unique and fully functional CW SRF 1.25 MeV SRF gun, built as part of the Coherent electron Cooling (CeC) project, which has demonstrated sustained CW operation with CsK2Sb photocathodes generating electron bunches with record-low transverse emittances and record-high bunch charge exceeding 10 nC. We propose to extend the capabilities of this system to high average current of 100 milliampere in two steps: increasing the current 30-fold at each step with the goal to demonstrate reliable long-term operation of the high-current low-emittance CW SRF guns. We also propose to test polarized GaAs photocathodes in the ultra-high vacuum (UHV) environment of the SRF gun, which has never been successfully demonstrated in RF accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

High power targets: Challenges of next-generation high-intensity neutrino beams

High power target systems are crucial elements in enabling future neutrino and other rare particle beams. These systems transform intense source of protons into secondary particles of interest to enable new scientific discoveries. As primary beam intensities increase in next-generation multi-megawatt accelerator facilities, high power target systems face key challenges. Thermal shock and radiation damage effects in beam-intercepting devices were identified as the leading cross-cutting challenges of high-power target facilities. Target materials R&D to address these challenges are therefore essential to enable and ensure reliable operation of future accelerator target facilities. This paper provides an overview of the R&D activities underway to help support high power target systems development for next-generation multi-MW facilities.

43 PARTICLE ACCELERATORS↗