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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 OSTI2019

Review Article: Crystal alignment for high performance organic electronics devices

Organic electronics devices based on high-performance small-molecule organic semiconductors have gained substantial attention because of their unique advantages such as excellent charge transport, solution processability, and environmental stability. However, the intrinsic crystallization of small-molecule organic semiconductors is anisotropic, resulting in significant device performance variations of organic electronics devices. In this article, the authors review the various approaches and techniques developed to control and align the crystallization of some benchmark solution-processable, high-performance, small-molecule organic semiconductors, such as 6,13-bis(triisopropylsilylethynyl) pentacene, N,N'-1H,1H-perfluorobutyl dicyanoperylenecarboxydiimide, and 5,11-bis(triethylgermylethynyl)anthradithiophene. These alignment approaches are studied in the context of capillary force-based techniques, patterning-based techniques, solution-shearing-based techniques, and other miscellaneous techniques, including zone-casting, vertical flowing, air flow navigation, temperature gradient alignment, etc. The organic semiconductors and crystal alignment techniques reviewed in this article shed light on important relationship among crystallization, charge transport, and device performance and can be applied to various high-performance organic electronics devices, such as organic thin film transistors and solar cells.

42 ENGINEERING↗

DOE OSTI2021

Biochemical Conversion of Lignocellulosic Biomass to Hydrocarbon Fuels and Products: 2020 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for biochemical platform research. It allows the impact of research progress to be quantified in terms of economic improvements in the overall cellulosic biofuel production process for a particular conversion pathway. As such, initial benchmarks can be established for currently demonstrated performance and progress can be tracked towards out-year goals to ultimately demonstrate cost-competitive cellulosic biofuel technology. The purpose of this report is to benchmark the latest experimental developments across a number of potential bioconversion pathways as quantified by modeled minimum fuel selling prices (MFSPs), as a measure of current status relative to those final targets. For this state of technology, TEA models were run for two separate biological conversion pathways to fuels, based on available data for integrated biomass deconstruction and hydrolysate processing; namely carboxylic acids (primarily butyric acid) and diols (2,3-butanediol [BDO]), reflecting NREL's recently-published 2018 biochemical design report focused on those two pathways. The models were run across three scenarios for lignin utilization, namely combustion, conversion to coproducts based on "base case" performance with biomass hydrolysate, and conversion to coproducts based on "high" performance demonstrated with model lignin monomer components. Relative to prior 2019 benchmarks, the 2020 SOT achieved further MFSP reductions of approximately $1/gallon gasoline equivalent, primarily attributed to improvements in pretreatment that led to both cost reductions and yield improvements.

09 BIOMASS FUELS↗

DOE OSTI2019

Deleterious effects of non-framework Al species on the catalytic performance of ZSM-5 crystals synthesized at low temperature

ZSM-5 is one of the most widely studied and commercially relevant zeolites. It is also one of the few zeolites that can be synthesized in an all-silica form (silicalite-1), and thus serves as a prototype for mechanistic studies of zeolite crystallization. The MFI framework of ZSM-5 is amenable to a broad range of synthesis conditions, which has led to numerous discoveries of materials with unique physicochemical properties. The exceptional performance of ZSM-5 among zeolite catalysts, coupled with its facile synthesis, has given rise to its widespread use in industrial applications. In this study we highlight the challenges associated with synthesizing ZSM-5 at low temperature (ca. 100 °C), which is often necessary to generate small crystals (<200 nm) with an appreciable quantity of acid sites (i.e. Si/Al < 25). We focus on the incorporation of non-framework Al, which includes extra-framework (octahedral) Al and penta-coordinated Al, as well as Al zoning – a common phenomenon where the density of Al sites within the framework is more prevalent at the outer rim and exterior surfaces of ZSM-5 crystals. We show that synthesis at low temperature, and most notably in growth mixtures containing high aluminum concentration, results in the incomplete incorporation of Al into the ZSM-5 framework that can be partially removed through post-synthesis mild acid treatment. We also show that higher synthesis temperature (170 °C) facilitates the incorporation of framework Al; however, in all cases the catalysts prepared in this study exhibit relatively low activity owing in part to the presence of Al defects. The library of ZSM-5 catalysts prepared herein are characterized using methanol to hydrocarbons as a benchmark reaction to assess the performance of various as-made and acid treated samples. Our findings suggest that ZSM-5 may be more difficult to synthesize than is commonly perceived, most notably when examining the various types of defects in crystalline products over a wide range of synthesis conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Spin-orbit couplings within the equation-of-motion coupled-cluster framework: Theory, implementation, and benchmark calculations

We present a formalism and an implementation for calculating spin-orbit couplings (SOCs) within the EOM-CCSD (equation-of-motion coupled-cluster with single and double substitutions) approach. The following variants of EOM-CCSD are considered: EOM-CCSD for excitation energies (EOM-EE-CCSD), EOM-CCSD with spin-flip (EOM-SF-CCSD), EOM-CCSD for ionization potentials (EOM-IP-CCSD) and electron attachment (EOM-EA-CCSD). We employ a perturbative approach in which the SOCs are computed as matrix elements of the respective part of the Breit-Pauli Hamiltonian using zeroth-order non-relativistic wave functions. We follow the expectation-value approach rather than the response-theory formulation for property calculations. Both the full two-electron treatment and the mean-field approximation (a partial account of the two-electron contributions) have been implemented and benchmarked using several small molecules containing elements up to the fourth row of the periodic table. The benchmark results show the excellent performance of the perturbative treatment and the mean-field approximation. When used with an appropriate basis set, the errors with respect to experiment are below 5% for the considered examples. The findings regarding basis-set requirements are in agreement with previous studies. The impact of different correlation treatment in zeroth-order wave functions is analyzed. Overall, the EOM-IP-CCSD, EOM-EA-CCSD, EOM-EE-CCSD, and EOM-SF-CCSD wave functions yield SOCs that agree well with each other (and with the experimental values when available). Using an EOM-CCSD approach that provides a more balanced description of the target states yields more accurate results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

A comparison of wrought and powder metallurgical FeCrAl claddings under simulated LWR accident transients

Iron-chromium-aluminum (FeCrAl) alloys are potential accident tolerant fuel (ATF) cladding candidates for light-water reactors but are difficult to fabricate as thin-walled tubes via conventional cast-and-wrought routes. Powder metallurgy (PM) offers a manufacturing alternative with improved compositional control, but its transient accident performance has not been directly benchmarked against wrought variants. This study evaluates the burst behavior of commercially developed PM-processed FeCrAl alloys, PM-C26M (Fe-12Cr-6Al-2Mo) and the high precipitate density FA-SMT (Fe-22Cr-5Al-3Mo), under simulated light-water reactor accident transient conditions. Burst testing was conducted using the Severe Accident Test Station with heating rates of 5 °C/s and 50 °C/s and internal pressures ranging from 25 MPa to 100 MPa. PM-C26M reproduced wrought C26M burst behavior within 7–37 °C across the stress range, indicating that PM processing does not compromise transient strength. FA-SMT exhibited markedly higher burst temperatures and reduced heating-rate sensitivity, consistent with its engineered precipitate strengthening. FA-SMT rupture exhibited axial "unzipping" rather than the lateral tearing characteristic of PM- and wrought C26M. Post-test EBSD and fractography indicate that this behavior is strongly correlated with strain-gated intergranular void nucleation associated with the dense precipitate architecture of FA-SMT, a response absent in the comparatively clean PM-C26M matrix and consistent with rupture morphologies reported for oxide-dispersion strengthened (ODS) FeCrAl of similar base-matrix chemistry to PM-C26M. These findings highlight the potential of powder metallurgy as a viable fabrication route for ATF claddings from an accident performance standpoint.

Bell, Sam [ORNL] (ORCID:0000000251905657)↗

DOE OSTI2015

Benchmark Testing of a New 56 Fe Evaluation for Criticality Safety Applications

The SAMMY code was used to evaluate resonance parameters of the 56 Fe cross section in the resolved resonance energy range of 0–2 MeV using transmission data, capture, elastic, inelastic, and double differential elastic cross sections. The resonance analysis was performed with the code SAMMY that fits R-matrix resonance parameters using the generalized least-squares technique (Bayes’ theory). The evaluation yielded a set of resonance parameters that reproduced the experimental data very well, along with a resonance parameter covariance matrix for data uncertainty calculations. Benchmark tests were conducted to assess the evaluation performance in benchmark calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗

DOE OSTI2015

Benchmark Testing of a new {sup 56}Fe evaluation for criticality safety applications - 14451

The SAMMY code was used to evaluate resonance parameters of the {sup 56}Fe cross section in the resolved resonance energy range of 0-2 MeV using transmission data, capture, elastic, inelastic, and double differential elastic cross sections. The resonance analysis was performed with the code SAMMY that fits R-matrix resonance parameters using the generalized least-squares technique (Bayes' theory). The evaluation yielded a set of resonance parameters that reproduced the experimental data very well, along with a resonance parameter covariance matrix for data uncertainty calculations. Benchmark tests were conducted to assess the evaluation performance in benchmark calculations. (authors)

42 ENGINEERING↗

DOE OSTI2022

Climbing the Summit and Pushing the Frontier of Mixed Precision Benchmarks at Extreme Scale

The rise of machine learning (ML) applications and their use of mixed precision to perform interesting science are driving forces behind AI for science on HPC. The convergence of ML and HPC with mixed precision offers the possibility of transformational changes in computational science. The HPL-AI benchmark is designed to measure the performance of mixed precision arithmetic as opposed to the HPL benchmark which measures double precision performance. Pushing the limits of systems at extreme scale is nontrivial -little public literature explores optimization of mixed precision computations at this scale. In this work, we demonstrate how to scale up the HPL-AI benchmark on the pre-exascale Summit and exascale Frontier systems at the Oak Ridge Leadership Computing Facility (OLCF) with a cross-platform design. We present the implementation, performance results, and a guideline of optimization strategies employed for delivering portable performance on both AMD and NVIDIA GPUs at extreme scale.

Lu, Hao↗

DOE OSTI2018

Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning

We perform a complementarity study of gravitational waves and colliders in the context of electroweak phase transitions choosing as our template the xSM model, which consists of the Standard Model augmented by a real scalar. We carefully analyze the gravitational wave signal at benchmark points compatible with a first order phase transition, taking into account subtle issues pertaining to the bubble wall velocity and the hydrodynamics of the plasma. In particular, we comment on the tension between requiring bubble wall velocities small enough to produce a net baryon number through the sphaleron process, and large enough to obtain appreciable gravitational wave production. For the most promising benchmark models, we study resonant di-Higgs production at the high-luminosity LHC using machine learning tools: a Gaussian process algorithm to jointly search for optimum cut thresholds and tuning hyperparameters, and a boosted decision trees algorithm to discriminate signal and background. The multivariate analysis on the collider side is able either to discover or provide strong statistical evidence of the benchmark points, opening the possibility for complementary searches for electroweak phase transitions in collider and gravitational wave experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Additively manufacturing high-temperature aluminum alloy for automotive engine pistons

The purpose of the project was to evaluate DuAlumin-3D, an ORNL designed Al alloy, for additive manufacturing of high-performance automotive components. The project consisted of additively manufacturing a cylindrical block of representative material which was then heat treated before machining into test coupons. Testing included tensile tests as a function of temperature, high-cycle fatigue testing as a function of temperature, density and elastic modulus measurements, and thermophysical property measurements. Fractography was also performed on failed high-cycle fatigue specimens. The results were compared against benchmark AM Al alloys and current high-performance conventionally manufactured alloys. Overall, the DuAlumin-3D material outperformed the incumbent materials in mechanical properties, especially at elevated temperatures, while maintaining broadly similar thermophysical properties. The results of this study support further maturation of the material for additive manufacturing of high-performance automotive components.

36 MATERIALS SCIENCE↗

DOE OSTI2016

TH-D-204-01: The Pursuit of Radiation Oncology Performance Excellence

The Malcolm Baldrige National Quality Improvement Act was signed into law in 1987 to advance U.S. business competitiveness and economic growth. Administered by the National Institute of Standards and Technology NIST, the Act created the Baldrige National Quality Program, now renamed the Baldrige Performance Excellence Program. The comprehensive analytical approaches referred to as the Baldrige Healthcare Criteria, are very well suited for the evaluation and sustainable improvement of radiation oncology management and operations. A multidisciplinary self-assessment approach is used for radiotherapy program evaluation and development in order to generate a fact based knowledge driven system for improving quality of care, increasing patient satisfaction, building employee engagement, and boosting organizational innovation. The methodology also provides a valuable framework for benchmarking an individual radiation oncology practice against guidelines defined by accreditation and professional organizations and regulatory agencies. Learning Objectives: To gain knowledge of the Baldrige Performance Excellence Program as it relates to Radiation Oncology. To appreciate the value of a multidisciplinary self-assessment approach in the pursuit of Radiation Oncology quality care, patient satisfaction, and workforce commitment. To acquire a set of useful measurement tools with which an individual Radiation Oncology practice can benchmark its performance against guidelines defined by accreditation and professional organizations and regulatory agencies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in the cold nuclear matter environment in electron-nucleus collisions. We establish first benchmarks and contrast the estimated performance of flavor tagging at the EIC with that at the Large Hadron Collider. We perform studies relevant to aspects of detector design including particle identification, charge information, and minimum transverse momentum capabilities. Additionally, we study the impact of using full event information instead of using only information associated with the identified jet. These methods can be deployed either on suitably accurate Monte Carlo event generators, or, for several applications, directly on experimental data. We provide an outlook for ultimately connecting these machine learning-based methods with first principles calculations in quantum chromodynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Newton-Krylov method in solving drift-flux two-phase flow problems for both pre-CHF and post-CHF conditions at steady state

Furthermore, this paper presents numerical simulations of two-phase flow heat transfer for both pre-CHF and post-CHF conditions typical in light water nuclear reactors. The conjugate heat transfer problem is modeled with one-dimensional drift-flux two-phase flow model for coolant flow and two-dimensional heat conduction equation in solid walls. Fully implicit time integration schemes were employed, and the resulted non-linear equations were solved with a Newton-Krylov method. For the drift-flux two-phase flow model, the EPRI drift-flux closure correlation is used to model the relative motion between the two phases. Additional closure correlations were adopted from REALP5-3D to determine two-phase flow regime, wall boiling heat and mass transfer, interfacial heat and mass transfer, and two-phase flow frictional pressure drop. Three sets of experiments were used for code validation, including Bartolomei subcooled flow boiling in vertical pipes, FRIGG boiling tests in vertical bundles, and Bennett heated tube tests. RELAP5-3D simulation results were also provided to perform code-to-code benchmark. Overall, numerical results of this work showed good agreements with both experimental data and RELAP5-3D simulation results. Discrepancy between them were also identified in post-CHF region, which suggests necessary further improvement.

42 ENGINEERING↗

DOE OSTI2018

Validation of statistical uncertainties in subcritical benchmark measurements: Part II – Measured data

Part I of this work presented an uncertainty approach which incorporates the singles and doubles counting rates determined by the Hage-Cifarelli formalism of the Feynman Variance-to-Mean method. This moments-based approach utilizes time correlations between prompt neutrons detections to assess the system multiplication. Additionally, it presented a validation which utilized simulated data generated using a 0-D point-kinetics Monte Carlo code. In Part II, this same method and validation approach is applied to measured data of a subcritical benchmark experiment. These measurements were performed with a 4.5 kg sphere of alpha-phase weapons grade plutonium reflected by copper and/or polyethylene. Finally, the results of 17 configurations are shown including detailed validation for four of the configurations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗