Integrating optimization methods into simulation workflows for advanced product engineering and manufacturing
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This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.
The Center for Simulation of Wave Interactions with Magnetohydrodynamics (SWIM) project is dedicated to conduct research on integrated multi-physics simulations. The Integrated Plasma Simulator (IPS) is a framework that was created by the SWIM team. It provides an integration infrastructure for loosely coupled component-based simulations by facilitating services for code execution coordination, computational resource management, data management, and inter-component communication. The IPS framework features improving resource utilization, implementing application-level fault tolerance, and support of the concurrent multi-tasking execution model. The General Atomics (GA) team worked closely with other team members on this contract, and conducted research in the areas of computational code monitoring, meta-data management, interactive visualization, and user interfaces. The original website to monitor SWIM activity was developed in the beginning of the project. Due to the amended requirements, the software was redesigned and a revision of the website was deployed into production in April of 2010. Throughout the duration of this project, the SWIM Monitoring Portal (http://swim.gat.com:8080/) has been a critical production tool for supporting the project's physics goals.
Immobilization of CO 2 injected into deep subsurface storage reservoirs is a critical component of risk assessment for geologic CO 2 storage (GCS). Local capillary trapping (LCT) is a recently established mode of immobilization that arises when CO 2 migrates due to buoyancy through heterogeneous storage reservoirs. This project sought to assess the amount and extent of LCT expected in storage formations under a range of injection conditions, and to confirm the persistence of LCT if the seal overlying the reservoir were to lose its integrity. Numerical simulation using commercial reservoir simulation software was conducted to assess the influence of injection. Laboratory experiments, modeling and numerical simulation were conducted to assess the effect of compromised seal integrity. Bench-scale (0.6 m by 0.6 m by 0.03 m) experiments with surrogate fluids provided the first empirical confirmation of the key concepts underlying LCT: accumulation of buoyant nonwetting phase at above residual saturations beneath capillary barriers in a variety of structures, which remains immobile under normal capillary pressure gradients. Immobilization of above-residual saturations is a critical distinction between LCT and the more familiar “residual saturation trapping.” To estimate the possible extent of LCT in a storage reservoir an algorithm was developed to identify all potential local traps, given the spatial distribution of capillary entry pressure in the reservoir. The algorithm assumes that the driving force for CO 2 migration can be represented as a single value of “critical capillary entry pressure” P c,entry crit , such that cells with capillary entry pressure greater/less than P c,entry crit act as barriers/potential traps during CO 2 migration. At intermediate values of P c,entry crit , the barrier regions become more laterally extensive in the reservoir, approaching a percolation threshold while non-barrier regions remain numerous. The maximum possible extent of LCT thus occurs at P c,entry crit near this threshold. Testing predictions of this simple algorithm against full-physics simulations of buoyancy-driven CO 2 migration support the concept of critical capillary entry pressure. However, further research is needed to determine whether a single value of critical capillary entry pressure always applies and how that value can be determined a priori. Simulations of injection into high-resolution (cells 0.3 m on a side) 2D and 3D heterogeneous domains show two characteristic behaviors. At small gravity numbers (vertical flow velocity much less than horizontal flow velocity) the CO 2 fills local traps as well as regions that would act as local barriers if CO 2 were moving only due to buoyancy. When injection ceases, the CO 2 migrates vertically to establish large saturations within local traps and residual saturation elsewhere. At large gravity numbers, the CO 2 invades a smaller portion of the perforated interval. Within this smaller swept zone the local barriers are not invaded, but local traps are filled to large saturation during injection and remain during post-injection gravity-driven migration. The small gravity number behavior is expected in the region within 100 m of a vertical injection well at anticipated rates of injection for commercial GCS. Simulations of leakage scenarios (through-going region of large permeability imposed in overlying seal) indicate that LCT persists (i.e. CO 2 remains held in a large fraction of the local iv traps) and the persistence is independent of injection rate during storage. Simulations of leakage for the limiting case of CO 2 migrating vertically from an areally extensive emplacement in the lower portion of a reservoir showed similar strong persistence of LCT. This research has two broad implications for GCS. The first is that LCT can retain a significant fraction of the CO 2 stored in a reservoir – above and beyond the residual saturation -- if the overlying seal were to fail. Thus frameworks for risk assessment should be extended to account for LCT. The second implication is that compared to pressure driven flow in reservoirs, CO 2 migration and trapping behave in a qualitatively different manner in heterogeneous reservoirs when buoyancy is the dominant driving force for flow. Thus simulations of GCS that neglect capillary heterogeneity will fail to capture important features of the CO 2 plume. While commercial reservoir simulation software can account for fine scale capillary heterogeneity, it has not been designed to work efficiently with such domains, and no simulators can handle fine-scale resolution throughout the reservoir. A possible way to upscale the migration and trapping is to apply an “effective residual saturation” to coarse-scale grids. While the extent of overall immobilization can be correlated in this way, all coarser grids failed to capture the distance traveled by the migrating CO 2 for large gravity number. Thus it remains unclear how best to account for LCT in the routine simulation work-flow that will be needed for large-scale GCS. Alternatives meriting investigation include streamline methods, reduced-physics proxies (e.g. particle tracking), and biased invasion percolation algorithms, which are based on precisely the capillary heterogeneity essential for LCT.
The thermodynamic and structural properties of the planar soft-sites dumbbell fluid are examined by Monte Carlo simulations and integral equation theory. The dimers are built of two Lennard-Jones segments. Site-site integral equation theory in two dimensions is used to calculate the site-site radial distribution functions for a range of elongations and densities and the results are compared with Monte Carlo simulations. The critical parameters for selected types of dimers were also estimated. We analyze the influence of the bond length on critical point as well as tested correctness of site-site integral equation theory with different closures. The integral equations can be used to predict the phase diagram of dimers whose molecular parameters are known.
The solidification microstructure in IN718 during additive manufacturing was modeled using phase field simulations. The novelty of the research includes the use of a surrogate Ni–Fe–Nb alloy that has the same equilibrium solidification range as IN718 as the model system for phase field simulations, the integration of the model alloy thermodynamics with the phase field simulations, and the use of high-performance computing tools to perform the simulations with a high enough spatial resolution for realistically capturing the dendrite morphology and the level of microsegregation seen under additive manufacturing conditions. Heat transfer and fluid flow models were used to compute the steady state temperature gradient and an average value of the solid-liquid (s-l) interface velocity that were used as input for the phase field simulations. The simulations show that the solidification morphology is sensitive to the spacing between the columnar structures. Spacing narrower than a critical value results in continued growth of a columnar microstructure, while above a critical value the columnar structure evolves into a columnar dendritic structure through the formation of secondary arms. These results are discussed in terms of the existing columnar to dendritic transition (CDT) theories. The measured interdendritic Nb concentration, the primary and secondary arm spacing is in reasonable agreement with experimental measurements performed on the nickel-base superalloy IN718.
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Here, we discuss the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Lastly, initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.
Tensor network decompositions of path integrals for simulating open quantum systems have recently been proven to be useful. However, these methods scale exponentially with the system size. This makes it challenging to simulate the non-equilibrium dynamics of extended quantum systems coupled with local dissipative environments. In this work, we extend the tensor network path integral (TNPI) framework to efficiently simulate such extended systems. The Feynman–Vernon influence functional is a popular approach used to account for the effect of environments on the dynamics of the system. In order to facilitate the incorporation of the influence functional into a multisite framework (MS-TNPI), we combine a matrix product state (MPS) decomposition of the reduced density tensor of the system along the sites with a corresponding tensor network representation of the time axis to construct an efficient 2D tensor network. The 2D MS-TNPI network, when contracted, yields the time-dependent reduced density tensor of the extended system as an MPS. The algorithm presented is independent of the system Hamiltonian. We outline an iteration scheme to take the simulation beyond the non-Markovian memory introduced by solvents. Applications to spin chains coupled to local harmonic baths are presented; we consider the Ising, XXZ, and Heisenberg models, demonstrating that the presence of local environments can often dissipate the entanglement between the sites. We discuss three factors causing the system to transition from a coherent oscillatory dynamics to a fully incoherent dynamics. The MS-TNPI method is useful for studying a variety of extended quantum systems coupled with solvents.
Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow–CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event–scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process–based models without sacrificing fidelity.
One of the objectives of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Integration Product Line (IPL) is to facilitate the deployment of the high-fidelity codes developed within the program. The Workbench initiative was launched in FY-2017 by the IPL to facilitate the transition from conventional tools to high fidelity tools. The Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes.
For neutron scattering and first principles simulation we integrate them to reveal the atomic-level to nanoscale structure of lithium lanthanum titanate (LLTO), a representative solid electrolyte material with applications in Li-ion batteries. Furthermore, the integrated approach solves the hierarchical local structure of LLTO in detail, including the coupled chemical order and topological distortion, as well as their correlation length scale and the spatial modulation with coherent boundaries. Ab initio molecular dynamics simulations are used to map out the distribution of the mobile ions and identify the migration pathway. This integrated approach provides a powerful means for detailed study of materials with complex local chemical and topological environment.
I report results from accurate numerical integrations of solar system orbits over the past 100 Myr with the integrator package HNBody. The simulations used different integrator algorithms, step sizes, and initial conditions, and included effects from general relativity, different models of the Moon, the Sun’s quadrupole moment, and up to 16 asteroids. I also probed the potential effect of a hypothetical Planet 9, using one set of possible orbital elements. The most expensive integration (Bulirsch–Stoer) required 4 months of wall-clock time with a maximum relative energy error ≲3×10{sup −13}. The difference in Earth’s eccentricity (Δe{sub E}) was used to track the difference between two solutions, considered to diverge at time τ when max |Δe{sub E}| irreversibly crossed ∼10% of mean e{sub E} (∼0.028×0.1). The results indicate that finding a unique orbital solution is limited by initial conditions from current ephemerides and asteroid perturbations to ∼54 Myr. Bizarrely, the 4-month Bulirsch–Stoer integration and a symplectic integration that required only 5 hr of wall-clock time (12-day time step, with the Moon as a simple quadrupole perturbation), agree to ∼63 Myr. Internally, such symplectic integrations are remarkably consistent even for large time steps, suggesting that the relationship between time step and τ is not a robust indicator of the absolute accuracy of symplectic integrations. The effect of a hypothetical Planet 9 on Δe{sub E} becomes discernible at ∼65 Myr. Using τ as a criterion, the current state-of-the-art solutions all differ from previously published results beyond ∼50 Myr. I also conducted an eigenmode analysis, which provides some insight into the chaotic nature of the inner solar system. The current study provides new orbital solutions for applications in geological studies.
The increased penetration of renewable generation induces unbalanced demand-supply on a power grid, while integrated energy systems (IES) can provide high part-load efficiency and high flexibility to support grid resilience. To study the transients and improve dynamic operability, it’s important to investigate the interplay between IES and power grid concurrently using real-time co-simulation approaches. In this presentation, we showed the previous co-simulation test between NETL and INL and discussed how this co-simulation can be beneficial for both IES and power grid research. In addition, we gave an overview about the SuperLab2.0 project, which was a federated national platform to address future power grid challenges. (Virtual presentation to the MILLENNIUM CLEAN and SUSTAINABLE POWER workshop 2025).
Using the kinetic Monte Carlo simulator, Stochastic Parallel PARticle Kinetic Simulator, from Sandia National Laboratories, a user routine has been developed to simulate mesoscale predictions of a grain structure near a moving heat source. Here, we demonstrate the use of this user routine to produce voxelized, synthetic, three-dimensional microstructures for electron-beam welding by comparing them with experimentally produced microstructures. When simulation input parameters are matched to experimental process parameters, qualitative and quantitative agreement for both grain size and grain morphology are achieved. The method is capable of simulating both single- and multipass welds. As a result, the simulations provide an opportunity for not only accelerated design but also the integration of simulation and experiments in design such that simulations can receive parameter bounds from experiments and, in turn, provide predictions of a resultant microstructure.
Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.
The changing landscape of the electricity industry, characterized by a surge in distributed energy resources (DERs) and proactive customers, necessitates practical solutions for coordinated operations especially at the distribution-level. This paper introduces a community-based transactive coordination mechanism designed to incentivize customers for providing localized and system-level services reflected through real-time prices. The work presents a bidding approach for communities with DERs, such as solar photovoltaic (PV) and battery energy storage systems (BESS), to formulate their price-responsiveness for retail energy coordination, emphasizing a community-centric model. By sending bidding curves to a third-party, the mechanism enables customers with DER assets to actively participate in localized coordination with the load serving entity (LSE), thereby supplementing each other’s and even the utilities needs through a shared energy economy. The proposed transactive mechanism is implemented leveraging a co-simulation framework that integrates a distribution grid simulator and Python-based agents for performance evaluation. Collaboration with a local utility to access real distribution feeder models and consumption profiles yields simulation results demonstrating the potential to reduce costs by 12\% for communities with DERs like PV and BESS.