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DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗

DOE OSTI2017

Movement of the Melt Metal Layer under Conditions Typical of Transient Events in ITER

During the operation of ITER, protective coatings of the divertor and the first wall will be exposed to significant plasma heat loads which may cause a huge erosion. One of the major failure mechanisms of metallic armor is diminution of their thickness due to the melt layer displacement. New experimental data are required in order to develop and validate physical models of the melt layer movement. The paper presents the experiments where metal targets were irradiated by a plasma stream at the quasi-stationary high-current plasma accelerator QSPA-T. The obtained data allow one to determine the velocity and acceleration of the melt layer at various distances from the plasma stream axis. The force causing the radial movement of the melt layer is shown to create an acceleration whose order of magnitude is 1000g. The pressure gradient is not responsible for creating this large acceleration. To investigate the melt layer movement under a known force, the experiment with a rotating target was carried out. The influence of centrifugal and Coriolis forces led to appearance of curved elongated waves on the surface. The surface profile changed: there is no hill in the central part of the erosion crater in contrast to the stationary target. The experimental data clarify the trends in the melt motion that are required for development of theoretical models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Thermal link design for conduction cooling of SRF cavities using cryocoolers

Superconducting radio frequency (SRF) cavities with quality factors ~1010 near 4 K have potential to be cooled using re-generative-cycle cryocoolers. Contrary to using liquid helium, cry-ogen-free operation can be realized by conductively linking a cavity to a cryocooler. The cavity-cryocooler thermal link needs careful design for balancing cooling capacity of the cryocooler with the dissipation rate in the cavity. We present a thermal analysis of a conduction-cooled SRF cavity that identifies the link thermal con-ductance requirement. The analysis uses published or expected RF dissipation characteristics of an Nb3Sn coated niobium cavity and in-house measured cooling capacity of a commercial pulse tube cryocooler. We describe mechanical design of a link that is con-structed using commercial high-purity aluminum and facilitates bolted-connection to elliptical SRF cavities. The thermal perfor-mance of the link is assessed by finite element simulations, taking into account temperature dependent thermal conductivities and measured thermal contact resistance of aluminum and niobium. The link is shown to support operation at an accelerating gradient of 10 MV/m with the lowest-known ‘perfect’ Nb3Sn surface re-sistance (10 nΩ) and also under non-ideal cases that assume certain static heat leak into the system and non-perfect Nb3Sn coatings

43 PARTICLE ACCELERATORS↗

DOE OSTI2015

Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks

Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here in this paper we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in “sloppy” systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Adaptive Activation Functions Accelerate Convergence in Deep and Physics-informed Neural Networks

We employ adaptive activation functions for regression in deep and physics-informed neural networks (PINNs) to approximate smooth and discontinuous functions as well as solutions of linear and nonlinear partial differential equations. In particular, we solve the nonlinear Klein-Gordon equation, which has smooth solutions, the nonlinear Burgers equation, which can admit high gradient solutions, and the Helmholtz equation. We introduce a scalable hyper-parameter in the activation function, which can be optimized to achieve best performance of the network as it changes dynamically the topology of the loss function involved in the optimization process. The adaptive activation function has better learning capabilities than the traditional one (fixed activation) as it improves greatly the convergence rate, especially at early training, as well as the solution accuracy. To better understand the learning process, we plot the neural network solution in the frequency domain to examine how the network captures successively different frequency bands present in the solution. We consider both forward problems, where the approximate solutions are obtained, as well as inverse problems, where parameters involved in the governing equation are identified. Our simulation results show that the proposed method is a very simple and effective approach to increase the efficiency, robustness and accuracy of the neural network approximation of nonlinear functions as well as solutions of partial differential equations, especially for forward problems. We theoretically prove that in the proposed method, gradient descent algorithms are not attracted to suboptimal critical points or local minima.

machine leaning, Bad minima, Inverse problems, Phy↗

DOE OSTI2021

High gradient C-band research at Los Alamos [Slides]

The Los Alamos C-band Test stand (CERF-NM) is operational. We are about to start testing and fabricating cavities using various materials and various fabrication methods for both ours and collaborators’ cavities.

42 ENGINEERING↗

DOE OSTI2010

Two Channel Dielectric-Lined Rectangular High Transformer Ratio Accelerator Structure Experiment

Current status of a two-channel cm-scale rectangular dielectric lined wakefield accelerator structure is described. This structure is installed at the Argonne Wakefield Accelerator facility (AWA), and is presently being evaluated. The device has a transformer ratio of {approx}12.5:1. When driven by a {approx}50 nC single drive bunch it is expected to obtain {approx}6 MV/m acceleration gradient. Related issues are discussed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Tune compensation in nearly scaling fixed field alternating gradient accelerators

In this paper, we investigate the stability of the particle trajectories in fixed field alternating gradient accelerators (FFAs) in the presence of field errors. The emphasis is on the scaling radial sector FFA type: A collaboration work is ongoing in view of better understanding the properties of the 150 MeV scaling FFA at Kyoto University Institute for Integrated Radiation and Nuclear Science in Japan and progress toward high-intensity operation. Analysis of certain types of field imperfections revealed some interesting features that required the development of an analytical model based on the scalloping angle of the orbits. This helped explain some of the experimental results as well as generalize the concept of a scaling FFA to a nonscaling one for which the tune variations obey a well-defined law. Based on this, a compensation scheme of tune variations in imperfect scaling FFAs is presented. This is the cornerstone of a novel concept of a fixed tune FFA in which the scaling is not achieved at every azimuthal position of the ring but rather in an average sense.

43 PARTICLE ACCELERATORS↗

NASA NTRS1992

Upwind scheme for solving the Euler equations on unstructured tetrahedral meshes

An upwind scheme is presented for solving the three-dimensional Euler equations on unstructured tetrahedral meshes. Spatial discretization is accomplished by a cell-centered finite-volume formulation using flux-difference splitting. Higher-order differences are formed by a multidimensional linear reconstruction process. The solution gradients required for the higher-order differenes are computed by a novel approach that yields highly resolved solutions in regions of smooth flow while avoiding oscillations across shocks without explicitly applying a limiter. Solutions are advanced in time by a three-stage Runge-Kutta time-stepping scheme with convergence accelerated to steady state by local time stepping and implicit residual smoothing. Transonic solutions are presented for two meshes around the ONERA M6 wing and demonstrate substantial accuracy and insensitivity to mesh size.

Frink, Neal T.↗

NASA NTRS2009

Advances in Non-contact Measurement of Creep Properties

Our team has developed a novel approach to measuring creep at extremely high temperatures using electrostatic levitation (ESL). This method has been demonstrated on niobium up to 2300 C, while ESL has melted tungsten (3400 C). High-precision machined spheres of the sample are levitated in the NASA MSFC ESL, a national user facility, and heated with a laser. The laser is aligned off-center so that the absorbed photons transfer their momentum to the sample, causing it to rotate at up to 250,000+ RPM. The rapid rotation loads the sample through centripetal acceleration, causing it to deform. The deformation of the sample is captured on high-speed video, which is analyzed by machine-vision software from the University of Massachusetts. The deformations are compared to finite element models to determine the constitutive constants in the creep relation. Furthermore, the noncontact method exploits stress gradients within the sample to determine the stress exponent in a single test. This method was validated in collaboration with the University of Tennessee for niobium at 1985 C, with agreement within the uncertainty of the conventional measurements. A similar method is being employed on Ultra-High-Temperature ZrB2- SiC composites, which may see application in rocket nozzles and sharp leading edges for hypersonic vehicles.

Hyers, Robert W.↗

DOE OSTI2014

SU-E-T-269: Quality Assurance of Spine Volumetric Modulated Arc Therapy with Flattening Filter Free Beams Using Gafchromic EBT3 Films

Purpose: We implemented the Gafchromic film-based patient specific QA of volumetric modulated arc therapy (VMAT) with flattening-filter free (FFF) beams for spine metastases and validated the accuracy of fast arc delivery. Methods: EBT3 films and a homemade cylindrical QA phantom were employed for dosimetric verification of VMATs. For 14 FFF VMAT plans (10 with 10-MV FFF beams and 4 with 6-MV FFF beams), the doses were recalculated on the phantom and delivered by a TrueBeam STx accelerator equipped with a high-definition 120 leaf MLC. The EBT3 films were scanned using an Epson 10000XL scanner through the FilmQA Pro software. All the irradiated film images were converted to dose map using a calibration response curve. The resulting dose map of film measurement was compared with treatment plan and evaluated using gamma analysis with dose tolerance of 2% within 2 mm. In addition, the point-dose measurement in the phantom using an ion chamber was evaluated as a reference in a ratio of measured and planned doses. Results: The gamma pass rates averaged over all FFF plans for composite-field measurements were 96.0 ± 3.6% (88.9%–99.5%). When adopting a tolerance level of 3% - 3 mm, the gamma pass rates were improved with the ranges from 98% to 100%. In addition, dose profiles and dose distributions showed that spinal cord was protected by the rapid dose fall-off and by delivering the treatment with high precision. In point-dose measurements, the average differences between the measured and planned doses were 0.5% ± 1.0% of the prescription dose. Conclusion: We demonstrated that Gafchromic EBT3 film would be an effective patient-specific QA tool, especially for VMAT of spine SBRT with treatment of small fields and highly gradient dose distributions. The results of film QA verified that the dosimetric accuracy of spine SBRT utilizing RapidArc with FFF beams in our institution is reliable.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Q-factor optimization for high-beta 650 MHz cavities for PIP-II

High Q-factors are of utmost importance to minimize losses of superconducting radio-frequency cavities deployed in continuous wave particle accelerators. This study elucidates the surface treatment that can maximize the Q-factors in high-beta 650 MHz elliptical niobium cavities. State-of-the-art surface treatments are applied in many single-cell cavities, and surface resistance studies are performed to understand the microwave dissipation at this unexplored frequency. The nitrogen doping treatment is confirmed to be necessary to maximize the Q-factors at medium RF fields. We applied this treatment in five-cell high-beta 650 MHz cavities and demonstrated that extremely high Q-factors were obtained at medium RF fields with this treatment. We also demonstrated that adding a cold electropolishing step after N-doping is crucial to push the quench field of multicell cavities to higher gradients.

43 PARTICLE ACCELERATORS↗

DOE OSTI2017

Electron Scattering by High-frequency Whistler Waves at Earth’s Bow Shock

Electrons are accelerated to non-thermal energies at shocks in space and astrophysical environments. While different mechanisms of electron acceleration have been proposed, it remains unclear how non-thermal electrons are produced out of the thermal plasma pool. Here, we report in situ evidence of pitch-angle scattering of non-thermal electrons by whistler waves at Earth’s bow shock. On 2015 November 4, the Magnetospheric Multiscale (MMS) mission crossed the bow shock with an Alfvén Mach number ∼11 and a shock angle ∼84°. In the ramp and overshoot regions, MMS revealed bursty enhancements of non-thermal (0.5–2 keV) electron flux, correlated with high-frequency (0.2–0.4 Ω{sub ce}, where Ω{sub ce} is the cyclotron frequency) parallel-propagating whistler waves. The electron velocity distribution (measured at 30 ms cadence) showed an enhanced gradient of phase-space density at and around the region where the electron velocity component parallel to the magnetic field matched the resonant energy inferred from the wave frequency range. The flux of 0.5 keV electrons (measured at 1 ms cadence) showed fluctuations with the same frequency. These features indicate that non-thermal electrons were pitch-angle scattered by cyclotron resonance with the high-frequency whistler waves. However, the precise role of the pitch-angle scattering by the higher-frequency whistler waves and possible nonlinear effects in the electron acceleration process remains unclear.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2017

Electron Scattering by High-Frequency Whistler Waves at Earth's Bow Shock

Electrons are accelerated to non-thermal energies at shocks in space and astrophysical environments. While different mechanisms of electron acceleration have been proposed, it remains unclear how non-thermal electrons are produced out of the thermal plasma pool. Here, we report in situ evidence of pitch-angle scattering of non-thermal electrons by whistler waves at Earths bow shock. On 2015 November 4, the Magnetospheric Multiscale (MMS) mission crossed the bow shock with an Alfvn Mach number is approximately 11 and a shock angle of approximately 84deg. In the ramp and overshoot regions, MMS revealed bursty enhancements of non-thermal (0.52 keV) electron flux, correlated with high-frequency (0.2 - 0.4 Omega(sub ce), where Omega(sub ce) is the cyclotron frequency) parallel-propagating whistler waves. The electron velocity distribution (measured at 30 ms cadence) showed an enhanced gradient of phase-space density at and around the region where the electron velocity component parallel to the magnetic field matched the resonant energy inferred from the wave frequency range. The flux of 0.5 keV electrons (measured at 1ms cadence) showed fluctuations with the same frequency. These features indicate that non-thermal electrons were pitch-angle scattered by cyclotron resonance with the high-frequency whistler waves. However, the precise role of the pitch-angle scattering by the higher-frequency whistler waves and possible nonlinear effects in the electron acceleration process remains unclear.

Oka, M.↗

DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Considerations for a TeV collider based on dielectric laser accelerators

Particle acceleration in dielectric microstructures powered by infrared lasers, or "dielectric laser acceleration" (DLA), is a promising area of advanced accelerator research with the potential to enable more affordable and higher-gradient accelerators for energy frontier science and a variety of other applications. DLA leverages well-established industrial fabrication capabilities and the commercial availability of tabletop lasers to reduce cost, with axial accelerating fields in the GV/m range. Further, desirable luminosities would be obtained by operating with very low charge per bunch but at extremely high repetition rates. And as a consequence of its unique operating parameter regime, coupling of the laser to the accelerator can potentially be in the 50% range and with low beamstrahlung energy loss due at the interaction point, making DLA a promising approach for a future multi-TeV linear collider.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

Magnetic Flux Expulsion Studies in Niobium SRF Cavities

With the recent discovery of nitrogen doping treatment for SRF cavities, ultra-high quality factors at medium accelerating fields are regularly achieved in vertical RF tests. To preserve these quality factors into the cryomodule, it is important to consider background magnetic fields, which can become trapped in the surface of the cavity during cooldown and cause Q₀ degradation. Building on the recent discovery that spatial thermal gradients during cooldown can significantly improve expulsion of magnetic flux, a detailed study was performed of flux expulsion on two cavities with different furnace treatments that are cooled in magnetic fields amplitudes representative of what is expected in a realistic cryomodule. In this contribution, we summarize these cavity results, in order to improve understanding of the impact of flux expulsion on cavity performance.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗