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At least 703 records · Page 39

Solvation of carbonaceous molecules by para-H{sub 2} and ortho-D{sub 2} clusters. I. Polycyclic aromatic hydrocarbons

This work theoretically examines the progressive coating of planar polycyclic aromatic hydrocarbon (PAH) molecules ranging from benzene to circumcoronene (C{sub 54}H{sub 18}) by para-hydrogen and ortho-deuterium. The coarse-grained Silvera-Goldman potential has been extended to model the interactions between hydrogen molecules and individual atoms of the PAH and parametrized against quantum chemical calculations for benzene-H{sub 2}. Path-integral molecular dynamics simulations at 2 K were performed for increasingly large amounts of hydrogen coating the PAH up to the first solvation shell and beyond. From the simulations, various properties were determined such as the size of the first shell and its thickness as well as the solvation energy. The degree of delocalization was notably quantified from an energy landscape perspective, by monitoring the fluctuations among inherent structures sampled by the trajectories. Our results generally demonstrate a high degree of localization owing to relatively strong interactions between hydrogen and the PAH, and qualitatively minor isotopic effects. In the limit of large hydrogen amounts, the shell size and solvation energy both follow approximate linear relations with the numbers of carbon and hydrogen in the PAH.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Nek5000 Simulations on Turbulent Coolant Flow in a Fuel Assembly Experiment - AREVA/ANL collaboration for advancing CFD tools

The NEAMS program aims to develop an integrated multi physics simulation capability “pellet to plant” for the design and analysis of future generations of nuclear power plants. In particular, the Reactor Product Line code suite’s multi resolution hierarchy is being designed to ultimately span the full range of length and time scales present in relevant reactor design and safety analyses, as well as scale from desktop to petaflop computing platforms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Dynamic simulation and load-following control of an integrated gasification combined cycle (IGCC) power plant with CO{sub 2} capture

Load-following control of future integrated gasification combined cycle (IGCC) plants with pre-combustion CO{sub 2} capture is expected to be far more challenging as electricity produced by renewable energy is connected to the grid and strict environmental limits become mandatory requirements. To study control performance during load following, a plant-wide dynamic simulation of a coal-fed IGCC plant with CO{sub 2} capture has been developed. The slurry-fed gasifier is a single-stage, downward-fired, oxygen-blown, entrained-flow type with a radiant syngas cooler (RSC). The syngas from the outlet of the RSC goes to a scrubber followed by a two-stage sour shift process with inter-stage cooling. The acid gas removal (AGR) process is a dual-stage physical solvent-based process for selective removal of H{sub 2}S in the first stage and CO{sub 2} in the second stage. Sulfur is recovered using a Claus unit with tail gas recycle to the AGR. The recovered CO{sub 2} is compressed by a split-shaft multistage compressor and sent for sequestration after being treated in an absorber with triethylene glycol for dehydration. The clean syngas is sent to two advanced “F”-class gas turbines (GTs) partially integrated with an elevated-pressure air separation unit. A subcritical steam cycle is used for heat recovery steam generation. A treatment unit for the sour water strips off the acid gases for utilization in the Claus unit. The steady-state model developed in Aspen Plus® is converted to an Aspen Plus Dynamics® simulation and integrated with MATLAB® for control studies. The results from the plant-wide dynamic model are compared qualitatively with the data from a commercial plant having different configuration, operating condition, and feed quality than what has been considered in this work. For load-following control, the GT-lead with gasifier-follow control strategy is considered. A modified proportional–integral–derivative (PID) control is considered for the syngas pressure control. For maintaining the desired CO{sub 2} capture rate while load-following, a linear model predictive controller (LMPC) is implemented in MATLAB®. A combined process and disturbance model is identified by considering a number of model forms and choosing the final model based on an information-theoretic criterion. The performance of the LMPC is found to be superior to the conventional PID control for maintaining CO{sub 2} capture rates in an IGCC power plant while load following.

01 COAL, LIGNITE, AND PEAT↗

Variational integrators for electric circuits

In this contribution, we develop a variational integrator for the simulation of (stochastic and multiscale) electric circuits. When considering the dynamics of an electric circuit, one is faced with three special situations: 1. The system involves external (control) forcing through external (controlled) voltage sources and resistors. 2. The system is constrained via the Kirchhoff current (KCL) and voltage laws (KVL). 3. The Lagrangian is degenerate. Based on a geometric setting, an appropriate variational formulation is presented to model the circuit from which the equations of motion are derived. A time-discrete variational formulation provides an iteration scheme for the simulation of the electric circuit. Dependent on the discretization, the intrinsic degeneracy of the system can be canceled for the discrete variational scheme. In this way, a variational integrator is constructed that gains several advantages compared to standard integration tools for circuits; in particular, a comparison to BDF methods (which are usually the method of choice for the simulation of electric circuits) shows that even for simple LCR circuits, a better energy behavior and frequency spectrum preservation can be observed using the developed variational integrator.

97 MATHEMATICS AND COMPUTING↗

Energy-dependent and Energy-integrated Two-moment General-relativistic Neutrino Transport Simulations of a Hypermassive Neutron Star

Abstract We compare two-moment-based energy-dependent and three variants of energy-integrated neutrino transport general-relativistic magnetohydrodynamics simulations of a hypermassive neutron star. To study the impacts due to the choice of the neutrino transport schemes, we perform simulations with the same setups and input neutrino microphysics. We show that the main differences between energy-dependent and energy-integrated neutrino transport are found in the disk and ejecta properties, as well as in the neutrino signals. The properties of the disk surrounding the neutron star and the ejecta in energy-dependent transport are very different from the ones obtained using energy-integrated schemes. Specifically, in the energy-dependent case, the disk is more neutron-rich at early times and becomes geometrically thicker at later times. In addition, the ejecta is more massive and, on average, more neutron-rich in the energy-dependent simulations. Moreover, the average neutrino energies and luminosities are about 30% higher. Energy-dependent neutrino transport is necessary if one wants to better model the neutrino signals and matter outflows from neutron star merger remnants via numerical simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

Progress on the Electron Gun Design for a McMillan Electron Lens in the Fermilab Integrable Optics Test Accelerator (IOTA)

This paper covers the progress made so far in designing the first McMillan electron lens for the Fermilab IOTA ring. The novel design allows for an increase in tune spread without limiting the dynamic aperture due to its integrability. Shown are simulations for an electron gun design to generate the specific required current density distribution for the nonlinear integrable system in IOTA.

43 PARTICLE ACCELERATORS↗

Open-Source Integrated Design-Analysis Environment For Nuclear Energy Advanced Modeling & Simulation Final Scientific/Technical Report

The framework created through the Open-Source Integrated Design-Analysis Environment (IDAE) for Nuclear Energy Advanced Modeling & Simulation grant has simplify and democratize advanced modeling and simulation in the nuclear energy industry that works on a range of nuclear engineering applications. It leverages millions of investment dollars from the Department of Energy's Office of Nuclear Energy for modeling and simulation of light water reactors and the Office of Nuclear Energy's research and development. The IDEA framework enhanced Kitware’s Computational Model Builder (CMB) while leveraging existing open-source toolkits and creating a graphical end-to-end umbrella guiding end-users and developers through the nuclear energy advanced modeling and simulation lifecycle. In addition, the work deliver strategic advancements in meshing and visualization for ensembles.

97 MATHEMATICS AND COMPUTING↗

Computational Design of Creep-Resistant Alloys and Experimental Validation in Ferritic Superalloys

A new class of ferritic superalloys containing B2-type zones inside parent L21-type precipitates in a disordered solid-solution matrix, also known as a hierarchical-precipitate strengthened ferritic alloy (HPSFA), has been developed for high-temperature structural applications in fossil-energy power plants. These alloys were designed by the addition of the Ti element into a previously-studied NiAl-strengthened ferritic alloy (denoted as FBB8 in this study). In the present research, systematic investigations, including advanced experimental techniques, first-principles calculations, and numerical simulations, have been integrated and conducted to characterize the complex microstructures and excellent creep resistance of HPSFAs. The experimental techniques include transmission-electron microscopy, scanningtransmission- electron microscopy, neutron diffraction, and atom-probe tomography, which provide detailed microstructural information of HPSFAs. Systematic tension/compression creep tests revealed that HPSFAs exhibit the superior creep resistance, compared with the FBB8 and conventional ferritic steels (i.e., the creep rates of HPSFAs are about 4 orders of magnitude slower than the FBB8 and conventional ferritic steels.) First-principles calculations include interfacial free energies, anti-phase boundary (APB) free energies, elastic constants, and impurity diffusivities in Fe. Combined with kinetic Monte- Carlo simulations of interdiffusion coefficients, and the integration of computational thermodynamics and kinetics, these calculations provide great understanding of thermodynamic and mechanical properties of HPSFAs. In addition to the systematic experimental approach and first-principles calculations, a series of numerical tools and algorithms, which assist in the optimization of creep properties of ferritic superalloys, are utilized and developed. These numerical simulation results are compared with the available experimental data and previous first-principles calculations, providing deep insight of creep mechanisms of the creep-resistant ferritic superalloys. With the above investigations, the HPSFA has been proved with superior creep resistance, and its microstructure, creep mechanism, and thermal/mechanical properties have been well studied and understood. In the future, HPSFAs with different additions and sizes of precipitates will be investigated and developed to further enhance the creep resistance of the ferritic superalloys and provide promising applications of the fossil-energy power plants.

Liaw, Peter↗

HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling

Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as- well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.

Maiterth, Matthias [ORNL] (ORCID:000000018698460X)↗

An Integrated High-performance Computing and Digital Real-time Simulation Testbed to Benchmark Closed-loop Load Shedding Algorithms in Power Systems

An integrated testbed using digital real-time simulator (DRTS) and a high-performance computing (HPC) cluster is presented here to compare speed and performance of computational schemes to mitigate time-critical issues in electric power systems. The first approach in this testbed validation is taken by running a set of closed-loop load shedding algorithms to compare and contrast two paradigms of arresting cascading failure propagation. Two algorithms involve solving DC and AC power flow model-based optimization problems to compute load shedding at different buses, while a model-based stochastic search using parallel computing provides a viable alternative. The algorithms are implemented in the DRTS-HPC testbed for the IEEE 14-bus benchmark transmission system. As a proof of the concept, simulation results are presented for implementation of closed-loop load-shedding algorithms for cascading failures in the DRTS-HPC testbed

24 POWER TRANSMISSION AND DISTRIBUTION↗

Integrated Radiation Transport and Thermo-Mechanics Simulation of a PWR Assembly

The Advanced Multi-Physics (AMP) Nuclear Fuel Performance code (AMPFuel) is focused on predicting the temperature and strain within a nuclear fuel assembly to evaluate the performance and safety of existing and advanced nuclear fuel bundles within existing and advanced nuclear reactors. AMPFuel was extended to include an integrated nuclear fuel assembly capability for (one-way) coupled radiation transport and nuclear fuel assembly thermo-mechanics. This capability is the initial step towards incorporating an improved predictive nuclear fuel assembly modeling capability to accurately account for source terms, such as the neutron flux distribution, coolant conditions, and assembly mechanical stresses, of traditional (single-pin) nuclear fuel performance simulation. AMPFuel was used to model an entire 17 x 17 Pressurized Water Reactor (PWR) fuel assembly with many of the features resolved in three dimensions (for thermo-mechanics and/or neutronics), including the fuel, gap, and cladding of each of the 264 fuel pins, the 25 guide tubes, top and bottom structural regions, and the upper and lower (neutron) reflector regions. The final full-assembly calculation was executed on Jaguar (Cray XT5) at the Oak Ridge Leadership Computing Facility using 40,000 cores in under 10 hours to model over 162 billion degrees of freedom for 10 loading steps.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

PYSIMFRAC: A Python library for synthetic fracture generation and analysis

In this paper, we introduce PYSIMFRAC, an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. PYSIMFRAC allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, PYSIMFRAC includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present PYSIMFRAC along with integration examples and discuss the ability to extend PYSIMFRAC from a single complex fracture to complex fracture networks.

58 GEOSCIENCES↗

Extended Lagrangian Born–Oppenheimer molecular dynamics using a Krylov subspace approximation

It is shown how the electronic equations of motion in extended Lagrangian Born–Oppenheimer molecular dynamics simulations can be integrated using low-rank approximations of the inverse Jacobian kernel. This kernel determines the metric tensor in the harmonic oscillator extension of the Lagrangian that drives the evolution of the electronic degrees of freedom. The proposed kernel approximation is derived from a pseudoinverse of a low-rank estimate of the Jacobian, which is expressed in terms of a generalized set of directional derivatives with directions that are given from a Krylov subspace approximation. The approach allows a tunable and adaptive approximation that can take advantage of efficient preconditioning techniques. The proposed kernel approximation for the integration of the electronic equations of motion makes it possible to apply extended Lagrangian first-principles molecular dynamics simulations to a broader range of problems, including reactive chemical systems with numerically sensitive and unsteady charge solutions. This can be achieved without requiring exact full calculations of the inverse Jacobian kernel in each time step or relying on iterative non-linear self-consistent field optimization of the electronic ground state prior to the force evaluations as in regular direct Born–Oppenheimer molecular dynamics. We note the low-rank approximation of the Jacobian is directly related to Broyden’s class of quasi-Newton algorithms and Jacobian-free Newton–Krylov methods and provides a complementary formulation for the solution of nonlinear systems of equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Velo: A Knowledge Management Framework for Modeling and Simulation

Modern scientific enterprises are inherently knowledge-intensive. Scientific studies in domains such as geosciences, climate, and biology require the acquisition and manipulation of large amounts of experimental and field data to create inputs for large-scale computational simulations. The results of these simulations are then analyzed, leading to refinements of inputs and models and additional simulations. The results of this process must be managed and archived to provide justifications for regulatory decisions and publications that are based on the models. In this paper we introduce our Velo framework that is designed as a reusable, domain independent knowledge management infrastructure for modeling and simulation. Velo leverages, integrates and extends open source collaborative and content management technologies to create a scalable and flexible core platform that can be tailored to specific scientific domains. We describe the architecture of Velo for managing and associating the various types of data that are used and created in modeling and simulation projects, as well as the framework for integrating domain-specific tools. To demonstrate realizations of Velo, we describe examples from two deployed sites for carbon sequestration and climate modeling. These provide concrete example of the inherent extensibility and utility of our approach.

99 GENERAL AND MISCELLANEOUS↗

Validation of the stochastic inversion algorithm for acoustic travel-time tomography: a large eddy simulation study

Acoustic tomography (AT) is explored as a remote sensing technique to obtain instantaneous snapshots of temperature and velocity fluctuations for wind energy applications. This study integrates Large Eddy Simulation (LES) with the Stochastic Inversion (SI) method to validate the algorithm’s capacity for accurate reconstruction of atmospheric fluctuations. The initial findings demonstrate the efficacy of the method in accurately capturing the predominant flow structures. Normalized L2 error evaluations further inform the algorithm’s precision, with errors accentuated in less sampled peripheral regions. The results underscore the method’s promise as a non-intrusive observational tool, with ongoing development poised to improve its precision and reliability.

17 WIND ENERGY↗

The ADAQ framework: An integrated toolkit for data acquisition and analysis with real and simulated radiation detectors

Here, the ADAQ framework is a collection of software tools that is designed to streamline the acquisition and analysis of radiation detector data produced in modern digital data acquisition (DAQ) systems and in Monte Carlo detector simulations. The purpose of the framework is to maximize user scientific pro-ductivity by minimizing the effort and expertise required to fully utilize radiation detectors in a variety of scientific and engineering disciplines. By using a single set of tools to span the real and simulation domains, the framework eliminates redundancy and provides an integrated workflow for high-fidelity comparison between experimental and simulated detector performance. Built on the ROOT data analysis framework, the core of the ADAQ framework is a set of Cþþ and Python libraries that enable high-level control of digital DAQ systems and detector simulations with data stored into standardized binary ROOT files for further analysis. Two graphical user interface programs utilize the libraries to create powerful tools: ADAQAcquisition handles control and readout of real-world DAQ systems and ADAQAnalysis provides data analysis and visualization methods for experimental and simulated data. At present, the ADAQ framework supports digital DAQ hardware from CAEN S.p.A. and detector simulations performed in Geant4; however, the modular design will facilitate future extension to other manufacturers and simulation platforms.

detector simulation↗

Arctic Critical Infrastructure: Assessing and Predicting the Risk to Critical Permafrost Infrastructure from Climate Change: A New Thermomechanical Approach

This study presents the development of a computational framework designed to predict the interaction between permafrost and infrastructure, addressing potential failure modes and mitigation strategies in the context of climate change. The framework, rooted in advanced modeling and simulation (mod/sim) techniques, integrates thermomechanical coupling to account for the complex interplay between heat flow, ice content, and mechanical behavior in permafrost. Existing models fail to fully capture these dynamics, particularly as they relate to the effects of ice saturation on structural integrity. Our innovative Arctic Coastal Erosion (ACE) framework fills this gap by coupling thermal and mechanical models to accurately simulate subsidence and deformation in permafrost environments. We applied the ACE framework to a representative runway, demonstrating its capability to predict settlement due to rising temperatures and subsequent permafrost thaw. This proof-of-concept showcases the potential of the framework to evaluate risks to Arctic infrastructure, which supports over four million people and 70% of existing permafrost-based structures. By simulating various infrastructure types and environmental conditions, our research offers insights into failure mechanisms and evaluates structural solutions to mitigate risk. The anticipated deliverables, including a prototype runway exemplar, position this project as a critical advancement in permafrost infrastructure modeling, with applications in national security and resilience planning.

54 ENVIRONMENTAL SCIENCES↗

Velo: Riding the Knowledge Management Wave for Simulation and Modeling

Modern scientific enterprises are inherently knowledge-intensive. In general, scientific studies in domains such as geosciences, climate, and biology require the acquisition and manipulation of large amounts of experimental and field data in order to create inputs for large-scale computational simulations. The results of these simulations must then be analyzed, leading to refinements of inputs and models and further simulations. Further, these results must be managed and archived to provide justifications for publications and regulatory decisions that are based on these models. In this paper we describe our Velo framework that is designed as a reusable, domain independent knowledge management infrastructure for modeling and simulation. Velo leverages, integrates, and extends open source collaborative and content management technologies to create a scalable and flexible core platform that can be tailored to specific scientific domains. In this paper we describe the architecture of Velo for managing and associating the various types of data that are used and created in modeling and simulation projects, as well as the framework for integrating domain-specific tools. To demonstrate a realization of Velo, we describe the Geologic Sequestration Software Suite (GS3) that has been developed to support geologic sequestration modeling. This provides a concrete example of the inherent extensibility and utility of our approach.

99 GENERAL AND MISCELLANEOUS↗