Search NASA⌕ Search

SEARCH · Search NASA

Results for “benchmark testing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Development Status of PROTEUS-MOC

The PROTEUS-MOC solver is intended to resolve the outstanding computational challenges that the PROTEUS-SN is limited by and bridge the gap between PROTEUS-SN and existing homogenized structured grid solvers. The primary push behind all of this work is to carry out coupled thermal-mechanical, fluid dynamics, and neutronics physics problems. The PROTEUS-MOC development has taken advantage of the combined experience with PROTEUS-SN and DeCART with the goal of producing a tool which combines the best features of both tools. Taking the DeCART tool as the model, it merges a subgroup cross section interpolation technique with a 2D method of characteristics approach and a 1D SN formulation. DeCART has been specifically demonstrated to be accurate on PWR and VHTR problems, but it has displayed several issues with regard to accuracy on thin axial mesh sizes which are attributable to the 2D1D scheme it is built upon. There are also notable problems with performance and scalability all of which we hope to resolve in PROTEUS-MOC. PROTEUS-MOC is at its core, based upon the success achieved with MOCFE which demonstrated excellent parallelization with respect to angle and good parallelization with respect to space. More recent efforts have indicated parallelization with respect to energy is also feasible. The work completed yielded a novel methodology where the axial basis functions are treated separately from the radial ones, but the geometry is extruded. This eliminates the need to carry out and store 3D ray tracing data and simultaneously allows the coarse axial node sizes typical with DeCART. To rapidly build a prototype, a standard power iteration algorithm was constructed using an unpreconditioned GMRES solution algorithm for solving the flux vector in each fission source iteration. The new PROTEUS-MOC solver was deployed on two test problems: Takeda and a heterogeneous cylindrical heavy water system. The Takeda benchmark problem is a standard homogeneous test problem which was previously found to be too difficult for the 3D MOCFE code to solve. Using the PROTEUS-MOC we were able to show space-angle convergence although full convergence requires further improvements in performance. The second benchmark problem was constructed more for future work than this year, but the results generated are promising indicating that it will be very useful in highlighting the weaknesses and strengths of the PROTEUS-MOC tool. While we are far from a production tool, the results included in this report give us confidence that we are on the right path to constructing an interceding code between the plethora of structured grid homogenized assembly solvers and the supercomputer consuming PROTEUS-SN code. With time, the creation of a suitable preconditioner along with typical optimization should reduce the performance shortcomings that are observable today.

Marin-Lafleche, A.↗
DOE OSTI2021

Disjunctive linear separation conditions and mixed-integer formulations for aircraft conflict resolution

In this paper, we address the aircraft conflict resolution problem in air traffic control. We introduce new mixed-integer programming formulations for aircraft conflict resolution with speed, heading and altitude control which are based on disjunctive linear separation conditions. We first examine the two-dimensional aircraft conflict resolution problem with speed and heading control represented as continuous decision variables. We show that the proposed disjunctive linear separation conditions are equivalent to the classical nonlinear conditions for aircraft separation. Further, we characterise conflict-free trajectories based on aircraft velocity bounds and propose a simple pre-processing algorithm to identify aircraft pairs which are either always conflict-free, or which cannot be separated using speed and heading control only. We then incorporate altitude control and propose a lexicographic optimisation formulation that aims to minimise the number of flight level changes before resolving outstanding conflicts via two-dimensional velocity control. The proposed mixed-integer programming formulations are nonconvex, and we propose convex relaxations, decomposition methods and constraint generation algorithms to solve the two-dimensional and lexicographic optimisation formulations to guaranteed optimality. Numerical experiments on four types of conflict resolution benchmarking instances are conducted to test the performance of the proposed mixed-integer formulations. Further, the proposed method is compared against two benchmarks based on state-of-the-art approaches for the aircraft conflict resolution problem. Our numerical results show that the proposed method largely outperforms both benchmarks in terms of runtime and is able to solve significantly more instances to global optimality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Explicitly correlated ring-coupled-cluster-doubles theory

The connection between the random-phase approximation and the ring-coupled-cluster-doubles method bridges the gap between density-functional and wave-function theories and the importance of the random-phase approximation lies in both its broad applicability and this linking role in electronic-structure theory. In this contribution, we present an explicitly correlated approach to the random-phase approximation, based on the direct ring-coupled-cluster-doubles ansatz, which overcomes the problem of slow basis-set convergence, inherent to the random-phase approximation. Benchmark results for a test set of 106 molecules and a selection of 10 organic complexes from the S22 test set demonstrate that convergence to within 99% of the basis-set limit is reached for triple-zeta basis sets for atomisation energies, while quadruple-zeta basis sets are required for interaction energies. Corrections due to single excitations into the complementary auxiliary space reduce the basis-set incompleteness error by one order of magnitude, while contributions due to the coupling of conventional and geminal amplitudes are in general negligible. We find that a non-iterative explicitly correlated correction to first order in perturbation theory exhibits the best ratio of accuracy to computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

LBNL Fault Detection and Diagnostics Datasets

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

AC↗
DOE OSTI2011

Data Testing for ENDF/B-VII.1beta2

Calculations have been performed for 390 critical assemblies from the International Handbook of Evaluated Criticality Safety Benchmark Experiments using the beta2 release of ENDF/B-VII.1. The results are compared to previous results for ENDF/B-VII. Cases that changed between the two versions are highlighted, and the results are discussed. The Cross Section Evaluation Working Group (CSEWG) is working on a new release of the ENDF/B-VII library of evaluated nuclear data, and the 'beta2' set of files was recently made available by the National Nuclear Data Center (NNDC). A set of about 850 input files for the MCNP Monte Carlo code to run critical assemblies from the International Handbook of Evaluated Criticality Safety Benchmark Experiments was available from our previous data testing work for ENDF/B-VII.0. We have now run 390 of those cases using data based on the beta2 files, and those results will be presented below. The ENDF files were downloaded from the NNDC to a Mac workstation. They were then processed using NJOY10 into ACE format files for use in the MCNP Monte Carlo code. The processing was limited to materials needed for the data testing work at this point. The existing MCNP input decks were used. No checking was done to see if any of the benchmarks had been updated since the ENDF/B-VII testing was finished. Most runs used 50 million histories in order to get Monte Carlo statistical uncertainties down the 0.01% range.

Atomic and Nuclear Physics↗
DOE OSTI2017

An overlapping Domain Decomposition preconditioning method for monolithic solution of shear bands

Metals subjected to high strain rate impact often exhibit a sudden and profound drop in the material’s load bearing capability, a ductile failure phenomenon known as shear banding. Shear bands, characterized as material instabilities, are driven by shear heating and described as narrow regions that have sustained intense plastic deformation and high temperature rise. This coupled thermo-mechanical localization problem can be formulated as a nonlinear system with two balance equations, momentum and energy, and two constitutive laws for elasticity and plasticity. Here in our formulation, mixed finite elements are used to discretize the equations in space and an implicit finite difference scheme is used to advance the system in time, where at every time step, a Newton type method is used to solve the system monolithically. To that end, a block Jacobian matrix is formed analytically using Gâteaux derivatives. The resulting Jacobian is sparse, nonsymmetric and its sparsity pattern and eigenvalue content vary with the different stages of shear bands formation, consequently posing a significant challenge to iterative solvers. To address this issue, an overlapping Domain Decomposition preconditioner that takes into account the physics of shear bands and the domain in which it forms, is proposed. The key idea is to resolve the shear band domain accurately while only solving the remaining domain approximately, with selective updates when necessary. The preconditioner is formed on the basis of an additive Schwartz method that is applied to a Schur complement partition of the system. The proposed preconditioner is implemented in parallel and tested on three different benchmark problems as compared against off-the-shelf solvers. We study the effect of h- and k-refinement that are obtained from Isogeometric discretizations of the system, the overlapping strategy and its parallel performance. Excellent performance is demonstrated in serial and parallel on all benchmark examples.

42 ENGINEERING↗
DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗
DOE OSTI2018

SSZ-70 Borosilicate Delamination Without Sonication: Effect of Framework Topology on Olefin Epoxidation Catalysis

Here, we report a scalable delamination procedure for a SSZ-70-framework layered-zeolite precursor, which for the first time does not involve either sonication or long-chain surfactants. Our approach instead relies on the mild heating of layered zeolite precursor B-SSZ-70(P) in an aqueous solution containing Zn(NO 3 ) 2 and tetrabutylammonium fluoride. Powder X-ray diffraction data are consistent with a loss of long-range order along the z-direction, while 29 Si MAS NMR spectroscopy demonstrates preservation of the zeolite framework crystallinity during delamination. The resulting delaminated material, DZ-2, possesses 1.4-fold higher external surface area relative to the nondelaminated three-dimensional zeolite B-SSZ-70, based on N2 physisorption data at 77 K. DZ-2 was functionalized with cationic Ti heteroatoms to synthesize Ti-DZ-2 via exchange with framework B. Ti-DZ-2 contains isolated titanium centers in its crystalline framework, as shown by UV-Vis spectroscopy. The generality of the synthetic delamination approach and catalyst synthesis is demonstrated with the synthesis of delaminated material DZ-3, which is derived from layered zeolite precursor ERB-1(P) with MWW framework topology. Upon catalytic testing for the epoxidation of 1-octene with ethylbenzene hydroperoxide as oxidant, under harsh tail-end conditions that deactivate amorphous Ti-silica-based catalysts, Ti-DZ-2 exhibits the highest per-Ti-site activity, selectivity, and stability for 1-octene epoxidation of all catalysts investigated. This testing includes the prior benchmark delaminated zeolite catalyst in this area, Ti-UCB-4, which possesses similar external surface area to Ti-DZ-2 but requires sonication and long-chain surfactants for its synthesis. The synthesis of DZ-2 is the first example of an economical delamination of layered zeolite precursor SSZ-70(P) and opens up new doors to the development of delaminated zeolites as commercial catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Discovery of hydrogen storage molecules using large language models and machine learning

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.

Harb, Hassan [Argonne National Laboratory (ANL), A↗
DOE OSTI2013

photon-plasma: A modern high-order particle-in-cell code

We present the photon-plasma code, a modern high order charge conserving particle-in-cell code for simulating relativistic plasmas. The code is using a high order implicit field solver and a novel high order charge conserving interpolation scheme for particle-to-cell interpolation and charge deposition. It includes powerful diagnostics tools with on-the-fly particle tracking, synthetic spectra integration, 2D volume slicing, and a new method to correctly account for radiative cooling in the simulations. A robust technique for imposing (time-dependent) particle and field fluxes on the boundaries is also presented. Using a hybrid OpenMP and MPI approach, the code scales efficiently from 8 to more than 250.000 cores with almost linear weak scaling on a range of architectures. The code is tested with the classical benchmarks particle heating, cold beam instability, and two-stream instability. We also present particle-in-cell simulations of the Kelvin-Helmholtz instability, and new results on radiative collisionless shocks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Line coupling effects in the isotropic Raman spectra of N{sub 2}: A quantum calculation at room temperature

We present quantum calculations of the relaxation matrix for the Q branch of N{sub 2} at room temperature using a recently proposed N{sub 2}-N{sub 2} rigid rotor potential. Close coupling calculations were complemented by coupled states studies at high energies and provide about 10 200 two-body state-to state cross sections from which the needed one-body cross-sections may be obtained. For such temperatures, convergence has to be thoroughly analyzed since such conditions are close to the limit of current computational feasibility. This has been done using complementary calculations based on the energy corrected sudden formalism. Agreement of these quantum predictions with experimental data is good, but the main goal of this work is to provide a benchmark relaxation matrix for testing more approximate methods which remain of a great utility for complex molecular systems at room (and higher) temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Benchmark between antenna code TOPICA, RAPLICASOL and Petra-M for the ICRH ITER antenna

ITER will be equipped with three plasma heating systems: neutral beam (NB), electron cyclotron (EC), and ion cy-clotron resonance heating (ICRH). The latter consists of two identical ICRH antennas to deliver 20 MW to the plasma (baseline, upgradable to 40 MW). ICRH will play a crucial role in the ignition and sustainment of burning plasmas in ITER. A high fidelity and robust modeling effort to understand the interaction of the IC waves with the scrape-off-layer (SOL) plasma is a very important aspect. Among the main important research topics, we have the assessment of the antenna loading for different plasma scenarios, the role of the lower hybrid resonance in front of the antenna and how to include it in our models, and the RF sheath boundary conditions to evaluate the antenna impurity generation. In this work, we tackle the first of these by reporting on ICRF simulations employing the Petra-M code, which is an electromagnetic simulation tool for modeling RF wave propagation based on MFEM [http://mfem.org] for the ITER ICRH antenna. Moreover, a benchmark between the well tested antenna codes TOPICA, RAPLI-CASOL, which is based on COMSOL [www.comsol.com], and the Petra-M code is also presented. In conclusion, S- and Z-matrices and wave electric field are compared showing an excellent agreement among these codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Taming the virtual space for incremental full configuration interaction

Incremental full configuration interaction (iFCI) closely approximates the FCI limit with polynomial cost through a many-body expansion of the correlation energy, providing highly accurate total energies within a given basis set. To extend iFCI beyond previous basis set limitations, this work introduces a novel natural orbital (NO) screening approach, incremental NO full configuration interaction (iNO-FCI). By consideration of the importance of virtual orbital selection in the convergence of iFCI, iNO-FCI maximizes the consistency between orbitals selected for each correlated body. iNO-FCI employs a principle of cancellation of errors and ensures that the same set of virtual NOs is used for interdependent terms. Here, this strategy significantly reduces computational cost without compromising precision. Computational savings of up to 95% are demonstrated, allowing access to larger basis sets that were previously computationally prohibitive. iNO-FCI is herein introduced and benchmarked for several difficult test cases involving double-bond dissociation, biradical systems, conjugated π systems, and the spin gap of a Cu-based transition metal complex.

Correlation energy↗
DOE OSTI2014

Simulated Stochastic Approximation Annealing for Global Optimization with a Square-Root Cooling Schedule

Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to have such a long CPU time. This paper proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation Markov chain Monte Carlo, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, e.g., a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors.

Liang, Faming↗
DOE OSTI2023

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function

Abstract Motivation Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently. Results We developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy. Availability and implementation The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A general Bayesian algorithm for the autonomous alignment of beamlines

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

47 OTHER INSTRUMENTATION↗