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DOE OSTI2020

Stochastic AC optimal power flow: A data-driven approach

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.

42 ENGINEERING↗

DOE OSTI2024

Assessing Ground State Energy of Molecules and Energy Profile of the NH3 Capturing CO2 System Using the Quantum Computing Algorithms

Molecule size correlates with the number of electrons on electronic energies and strength of anharmonicity on vibrational properties, however, it is challenging to address using classical computing. In this study, variational quantum eigensolver (VQE) algorithm was implemented on a quantum simulator to quantify electronic and vibrational energies and reaction pathways of CO2 + NH3 = NH2COOH. The VQE-based Hartree-Fock-Embedding algorithm was adopted to benchmark electronic energies for a series of molecules (doi.org/10.1063/5.0188249) and quantify the reaction energy profile of the CO2 capture reaction (doi.org/10.1116/5.0137750). The generated reaction profile is in good agreement with the classical high-level Coupled-Cluster-Singles-and-Doubles (CCSD) results. The quantum computing algorithm also helps enhance the calculation of vibrational ground-state energies by considering the many-body coupling using the Vibrational Self-Consistent Field method, providing results for CO2 and NH3 molecules with accuracy comparable to the direct diagonalization method. Our approach indicates quantum computing can be applied to solve practical problems.

Lee, Yueh-Lin↗

NASA NTRS1992

Branch recovery with compiler-assisted multiple instruction retry

In processing systems where rapid recovery from transient faults is important, schemes for multiple instruction rollback recovery may be appropriate. Multiple instruction retry has been implemented in hardware by researchers and also in mainframe computers. This paper extends compiler-assisted instruction retry to a broad class of code execution failures. Five benchmarks were used to measure the performance penalty of hazard resolution. Results indicate that the enhanced pure software approach can produce performance penalties consistent with existing hardware techniques. A combined compiler/hardware resolution strategy is also described and evaluated. Experimental results indicate a lower performance penalty than with either a totally hardware or totally software approach.

Alewine, N. J.↗

DOE OSTI2023

Two-photon double ionization with finite pulses: Application of the virtual sequential model to helium

As a step toward the full ab initio description of two-photon double ionization processes, we present a finite-pulse version of the virtual-sequential model for polyelectronic atoms. The model relies on the ab initio description of the single ionization scattering states of both the neutral and ionized target system. As a proof of principle and a benchmark, the model is applied to the helium atom using the newstock atomic photoionization code. The results of angularly integrated observables, which are in excellent agreement with existing time-dependent Schrödinger equation (TDSE) simulations, show how the model is able to capture the role of electron correlation in the nonsequential regime, and the influence of autoionizing states in the sequential regime, at a comparatively modest computational cost. The model also reproduces the two-particle interference with ultrashort pulses, which is within reach of current experimental technologies. Furthermore, the model shows the modulation of the joint energy distribution in the vicinity of autoionizing states, which can be probed with extreme-ultraviolet pulses of duration much longer than the characteristic lifetime of the resonance. Furthermore, the formalism discussed here applies also to polyelectronic atoms and molecules, thus opening a window on nonsequential and sequential double ionization in these more complex systems.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

Destabilizing high-capacity high entropy hydrides via earth abundant substitutions: From predictions to experimental validation

The vast chemical space of high entropy alloys (HEAs) makes trial-and-error experimental approaches for materials discovery intractable and often necessitates data-driven and/or first principles computational insights to successfully target materials with desired properties. In the context of materials discovery for hydrogen storage applications, a theoretical prediction-experimental validation approach can vastly accelerate the search for substitution strategies to destabilize high-capacity hydrides based on benchmark HEAs, e.g. TiVNbCr alloys. Here, in this study, machine learning predictions, corroborated by density functional theory calculations, predict substantial hydride destabilization with increasing substitution of earth-abundant Fe content in the (TiVNb) 75 Cr 25-x Fe x system. The as-prepared alloys crystallize in a single-phase bcc lattice for limited Fe content x < 7, while larger Fe content favors the formation of a secondary C14 Laves phase intermetallic. Short range order for alloys with x < 7 can be well described by a random distribution of atoms within the bcc lattice without lattice distortion. Hydrogen absorption experiments performed on selected alloys validate the predicted thermodynamic destabilization of the corresponding fcc hydrides and demonstrate promising lifecycle performance through reversible absorption/desorption. This demonstrates the potential of computationally expedited hydride discovery and points to further opportunities for optimizing bcc alloy ↔ fcc hydrides for practical hydrogen storage applications.

36 MATERIALS SCIENCE↗

NASA NTRS2019

NASA's Role in Gas Turbine Technology Development: Accelerating Technical Progress via Collaboration Between Academia, Industry, and Government Agencies

Given the maturity of the gas turbine engine since its invention and also considering the limited and flattened level of resources expected to be allocated for NASA aeronautics research and development, we ask the question are NASA technology investments still needed to enable future turbine engine-based propulsion systems? If so, what is NASA’s unique role to justify NASA’s investment? To address this topic, we will first review the accomplishments and the impact that NASA Glenn Research Center has made on turbine engine technologies over the last 78 years. Specifically, this paper discusses NASA’s role and contributions to turbine engine development, specific to both 1) NASA’s role in conducting experiments to understand flow physics and provide relevant benchmark validation experiments for Computational Fluid Dynamics (CFD) code development, validation, and assessment; and 2) the impact of technologies resulting from NASA collaborations with industry, academia, and other government agencies. Note that the scope of the discussion is limited to the NASA technology contributions with which the author was intimately associated, and does not represent the entirety of the NASA contributions to turbine engine technology. The specific research, development, and demonstrations discussed herein were selected to both 1) provide a comprehensive review and reference list of the technology and its impact, and 2) identify NASA’s unique role and highlight how NASA’s involvement resulted in additional benefit to the gas turbine engine community. Secondly, we will discuss current NASA collaborations that are in progress and provide a status of the results. Finally, we discuss the challenges anticipated for future turbine engine-based propulsion systems for civil aviation and identify potential opportunities for collaboration where NASA involvement would be beneficial. Ultimately, the gas turbine engine community will decide if NASA involvement is needed to contribute to the development of the design and analysis tools, databases, and technology demonstration programs to meet these challenges for future turbine engine-based propulsion systems.

Suder, Kenneth L.↗

DOE OSTI2021

Validation of Jezebel Reactivity Coefficients and Sensitivity Analysis

Nuclear data validation is often performed today using criticality measurements. The gold standard for criticality measurements is the International Criticality Safety Benchmark Experiment Project (ICSBEP). The validation specifically focuses on the effective multiplication factor ($k_{eff}$). $K_{eff}$ is a relatively easy parameter to infer and has reduced uncertainty due to being at or above critical. However, while $k_{eff}$ is the most documented parameter and its uncertainties and sensitivities have been evaluated in great detail, it cannot be used as a standalone metric to determine inaccuracies in nuclear data (e.g., cross section data, PFNS, nu), which is based on theory, physics, and differential measurements. The Experiments Underpinned by Computational Learning for Improvements in nuclear Data (EUCLID) project aims to identify compensating errors in specific isotope nuclear data by optimally designing experiments that are, or are not sensitive to a suite of measurement parameters beyond $k_{eff}$. By identifying parameters that are sensitive to each other, oppositely sensitive, or have substantial magnitude differences in sensitivity, experiments can be designed to constrain questionable nuclear data. One sensitivity that is of particular interest to this project includes the sensitivity of reactivity coefficients. Reactivity coefficients compare reactivity, which is related to $k_{eff}$ at two different states therefore being sensitive to small changes in the system. The most common type of reactivity coefficient measurements is comparison to void for a small sample within the assembly. It is key that the sample sizes are small enough to not affect the flux of the full system. Reactivity coefficients were evaluated for many early experiments to better understand transport corrected cross sections. In fact, ICSBEP includes reactivity coefficient results as “Supplemental Measurements” in appendices for a handful of older benchmarks. One of those benchmarks is Jezebel, the bare Pu critical assembly. This paper compares new simulations of reactivity coefficients for Jezebel, and explores the sensitivity of reactivity coefficients to small changes in nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Explicitly correlated coupled cluster method for accurate treatment of open-shell molecules with hundreds of atoms

We present a near-linear scaling formulation of the explicitly correlated coupled-cluster singles and doubles with the perturbative triples method [CCSD(T)F12¯] for high-spin states of open-shell species. The approach is based on the conventional open-shell CCSD formalism [M. Saitow et al., J. Chem. Phys. 146, 164105 (2017)] utilizing the domain local pair-natural orbitals (DLPNO) framework. The use of spin-independent set of pair-natural orbitals ensures exact agreement with the closed-shell formalism reported previously, with only marginally impact on the cost (e.g., the open-shell formalism is only 1.5 times slower than the closed-shell counterpart for the C160H322 n-alkane, with the measured size complexity of ≈1.2). Evaluation of coupled-cluster energies near the complete-basis-set (CBS) limit for open-shell systems with more than 550 atoms and 5000 basis functions is feasible on a single multi-core computer in less than 3 days. The aug-cc-pVTZ DLPNO-CCSD(T)F12¯ contribution to the heat of formation for the 50 largest molecules among the 348 core combustion species benchmark set [J. Klippenstein et al., J. Phys. Chem. A 121, 6580–6602 (2017)] had root-mean-square deviation (RMSD) from the extrapolated CBS CCSD(T) reference values of 0.3 kcal/mol. For a more challenging set of 50 reactions involving small closed- and open-shell molecules [G. Knizia et al., J. Chem. Phys. 130, 054104 (2009)], the aug-cc-pVQ(+d)Z DLPNO-CCSD(T)F12¯ yielded a RMSD of ∼0.4 kcal/mol with respect to the CBS CCSD(T) estimate.

Kumar, Ashutosh (ORCID:0000000175896030)↗

DOE OSTI2024

LibERI—A portable and performant multi-GPU accelerated library for electron repulsion integrals via OpenMP offloading and standard language parallelism

A portable and performant graphics processing unit (GPU)-accelerated library for electron repulsion integral (ERI) evaluation, named LibERI, has been developed and implemented via directive-based (e.g., OpenMP and OpenACC) and standard language parallelism (e.g., Fortran DO CONCURRENT). Offloaded ERIs consist of integrals over low and high contraction s, p, and d functions using the rotated-axis and Rys quadrature methods. GPU codes are factorized based on previous developments with two layers of integral screening and quartet presorting. In this work, the density screening is moved to the GPU to enhance the computational efficacy for large molecular systems. Here, the L-shells in the Pople basis set are also separated into pure S and P shells to increase the ERI homogeneity and reduce atomic operations and the memory footprint. LibERI is compatible with any quantum chemistry drivers supporting the MolSSI Driver Interface. Benchmark calculations of LibERI interfaced with the GAMESS software package were carried out on various GPU architectures and molecular systems. The results show that the LibERI performance is comparable to other state-of-the-art GPU-accelerated codes (e.g., TeraChem and GMSHPC) and, in some cases, outperforms conventionally developed ERI CUDA kernels (e.g., QUICK) while fully maintaining portability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Abstract. Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

58 GEOSCIENCES↗

NASA NTRS2015

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

Cloud computing capabilities have rapidly expanded within the private sector, offering new opportunities for meteorological applications. Collaborations between NASA Marshall, NASA Ames, and contractor partners led to evaluations of private (NASA) and public (Amazon) resources for executing short-term NWP systems. Activities helped the Marshall team further understand cloud capabilities, and benchmark use of cloud resources for NWP and other applications

Disasters↗

NASA NTRS2017

New Flutter Analysis Technique for Time-Domain Computational Aeroelasticity

A new time-domain approach for computing flutter speed is presented. Based on the time-history result of aeroelastic simulation, the unknown unsteady aerodynamics model is estimated using a system identification technique. The full aeroelastic model is generated via coupling the estimated unsteady aerodynamic model with the known linear structure model. The critical dynamic pressure is computed and used in the subsequent simulation until the convergence of the critical dynamic pressure is achieved. The proposed method is applied to a benchmark cantilevered rectangular wing.

HWB aircraft↗

DOE OSTI2025

Enabling Low-Temperature (LTP) Ignition Technologies for Multi-Mode Engines through the Development of a Validated High-Fidelity LTP Model for Predicative Simulations Tools

The goal of multi-mode engine architectures is to extend current lean-burn dilution limits with renewable fuels, which requires spark plugs to deposit high energies (hundreds of mJ) in order to initiate ignition and complete combustion. At elevated energy deposition rates, spark plugs experience increased electrode erosion and thermal losses, which ultimately shortens the spark-plug lifetime and lowers ignition efficiency. As such, in order to safeguard the efficiency gains of multi-mode concepts, new and improved ignition technologies are required. Recently, non-equilibrium low-temperature plasmas (LTP) have been shown to promote energy-efficient ignition via quenching and transport of electronically excited atoms and molecules, selective radical production and fast heating of hydrocarbon/air mixtures [1-2]. Thus, LTP is seen as a technology that can potentially improve the energy extraction efficiency of fuels, while enabling kinetically controlled combustion modes towards fuel leaner conditions to realize current DOE VTO goals of improving the sustainability of future mobility [3]. Although many previous studies have demonstrated the efficacy of plasma-assisted ignition to enhance combustion, the detailed enhancement mechanisms remain largely unknown, especially for oxygenated fuels and at elevated pressures that are most relevant to practical engine conditions. These barriers hinder the development of accurate and comprehensive numerical models that seek to describe LTP-based ignition in existing engine design software tools and methods. Current state-of-the-art simulation capabilities for LTP ignition systems are in need of improvements since they deliver qualitative results only due to important limitations of existing approaches. Firstly, validated kinetic models with elementary steps for plasma discharges in oxygenated fuel/air mixtures of relevance to the transportation sector are required. Such kinetic models do not exist at present and will be developed and validated within this project. Secondly, plasma discharges and reactive mixture ignition are multi-scale, unsteady processes requiring high-performance numerical methods and software that execute efficiently on DOE supercomputers. Such software does not exist at present and will be developed and applied to practical LTP ignition scenarios as part of this project. Thirdly, experimental databases that are tailored to serve as benchmark in support of the development of predictive computational models of LTP ignition do not exist and will be part of this project.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Scalable DPG multigrid solver for Helmholtz problems: A study on convergence

This paper presents a scalable multigrid preconditioner targeting large-scale systems arising from discontinuous Petrov–Galerkin (DPG) discretizations of high-frequency wave operators. This work is built on previously developed multigrid preconditioning techniques of Petrides and Demkowicz (Comput. Math. Appl. 87 (2021) pp. 12–26) and extends the convergence results from $\mathscr{O}$(10 7 ) degrees of freedom (DOFs) to $\mathscr{O}$(10 9 ) DOFs using a new scalable parallel MPI/OpenMP implementation. Novel contributions of this paper include an alternative definition of coarse-grid systems based on restriction of fine-grid operators, yielding superior convergence results. In the uniform refinement setting, a detailed convergence study is provided, demonstrating h and p robust convergence and linear scaling with respect to the wave frequency. Finally, the paper concludes with numerical results on hp -adaptive simulations including a large-scale seismic modeling benchmark problem with high material contrast.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

Evaluating the Suitability of Commercial Clouds for NASA's High Performance Computing Applications: A Trade Study

NASA’s High-End Computing Capability (HECC) Project is periodically asked if it could be more cost effective through the use of commercial cloud resources. To answer the question, HECC’s Application Performance and Productivity (APP) team undertook a performance and cost evaluation comparing three domains: two commercial cloud providers, Amazon and Penguin, and HECC’s in-house resources—the Pleiades and Electra systems. In the study, the APP team used a combination of the NAS Parallel Benchmarks (NPB) and six full applications from NASA’s workload on Pleiades and Electra to compare performance of nodes based on three different generations of Intel Xeon processors—Haswell, Broadwell, and Skylake. Because of export control limitations, the most heavily used applications on Pleiades and Electra could not be used in the cloud; therefore, only one of the applications, OpenFOAM, represents work from the Aeronautics Research Mission Directorate and the Human and Exploration Mission Directorate. The other five applications are from the Science Mission Directorate.

High Performance↗

DOE OSTI2021

Sparse invariant domain preserving discontinuous Galerkin methods with subcell convex limiting

In this paper, we develop high-order nodal discontinuous Galerkin (DG) methods for hyperbolic conservation laws that satisfy invariant domain preserving properties using subcell flux corrections and convex limiting. These methods are based on a subcell flux corrected transport (FCT) methodology that involves blending a high-order target scheme with a robust, low-order invariant domain preserving method that is obtained using a graph viscosity technique. Furthermore, the new low-order discretizations are based on sparse stencils which do not increase with the polynomial degree of the high-order DG method. As a result, the accuracy of the low-order method does not degrade when used with high-order target methods. The method is applied to both scalar conservation laws, for which the discrete maximum principle is naturally enforced, and to systems of conservation laws such as the Euler equations, for which positivity of density and a minimum principle for specific entropy are enforced. Numerical results are presented on a number of benchmark test cases.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Summary Report Of The FY25 Computational Fluid Dynamics Verification And Validation Exercises In The Advanced Reactor Technologies - Gas-cooled Reactor Program

Verification and Validation (V&V) of numerical tools is critical for ensuring reasonable predictions during design, safety analysis, and licensing. Recent work in the Advanced Reactor Technologies - Gas-cooled Reactor (ART-GCR) program has focused on V&V of common Computational Fluid Dynamics (CFD) tools that are used within the Untied States. This report presents an update on these CFD V&V activities. These Generation IV Forum (GIF) Very High Temperature Reactor (VHTR) Computational Methods, Validation, and Benchmarking (CMVB) is an international organization dedicated to the verification and validation of High Temperature Gas-Cooled Reactor (HTGR) simulation tools. Participation in the CMVB provides additional value to the V&V activities, as it allows for access to a wider range of data, and provides valuable benchmarking exercises. Three HTGR phenomena are targeted: Reactor Cavity Cooling System (RCCS) performance, core bypass flow, and lower plenum mixing. Simulations of the University of Wisconsin-Madison (UW-Madison) RCCS facilities are performed with Reynolds Averaged Navier-Stokes (RANS) in StarCCM+. Results are compared for both forced and natural convection conditions, with both exhibiting good agreement with experimental measurements. The Idaho National Laboratory (INL) matched index of refraction (MIR) and Korean Atomic Energy Research Institute (KAERI) bypass flow expeirments are used to validation CFD predictions of bypass flow. Simulations are performed with RANS in StarCCM+ and with Large Eddy Simulation (LES) in NekRS. Finally, preliminary simulations of the Institute of Nuclear and New Energy Technology (INET) lower plenum mixing facilities are presented. Initial work has developed models with LES, RANS, and porous media models. These preliminary models are presented and compared to each other to gauge differences in predictions with each of the three methods.

and Benchmarking (CMVB)↗

DOE OSTI2026

Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications

This study presents a physics-informed neural network (PINN) framework for reactive transport modeling for simulating fast bimolecular reactions in porous media. Accurate characterization of cAhemical interactions and product formation in surface and subsurface environments is essential for advancing critical mineral extraction and related geoscience applications. The proposed methodology sequentially addresses the flow and diffusion–reaction subproblems. The flow field is computed using a mixed formulation, while the diffusion–reaction system is modeled via two uncoupled tensorial diffusion equations reformulated in terms of chemical invariants. PINNs are employed to solve the governing equations, enabling data-efficient, mesh-free prediction of chemical concentration fields. The framework is validated through a series of benchmark problems involving flow in heterogeneous porous media. Initial verification is conducted using patch tests for the flow field, followed by validation of the transport problem with emphasis on preserving non-negativity of concentrations. The complete fast bimolecular reaction scenario is then solved, yielding spatial distributions of reactants and product species. Results demonstrate that the PINNs-based approach effectively captures sharp, mixing-limited reaction fronts and dispersive mixing behavior, offering reliable predictions of reactive plume evolution. These capabilities are crucial for evaluating long-term subsurface behavior in applications such as fluid storage, energy extraction, and efficient extraction of critical minerals.

42 ENGINEERING↗