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DOE OSTI2016

University Research in Support of TREAT Modeling and Simulation, FY 2016

Idaho National Laboratory is currently evolving the modeling and simulation (M&S) capability that will enable improved core operation as well as design and analysis of TREAT experiments. This M&S capability primarily uses MAMMOTH, a reactor physics application being developed under the Multi-physics Object Oriented Simulation Environment (MOOSE) framework. MAMMOTH allows the coupling of a number of other MOOSE-based applications. In support of this research, INL is working with four universities to explore advanced solution methods that will complement or augment capabilities in MAMMOTH. This report consists of a collection of year end summaries of research from the universities performed in support of TREAT modeling and simulation. This research was led by Prof. Sedat Goluoglu at the University of Florida, Profs. Jim Morel and Jean Ragusa at Texas A&M University, Profs. Benoit Forget and Kord Smith at Massachusetts Institute of Technology, Prof. Leslie Kerby of Idaho State University and Prof. Barry Ganapol of University of Arizona. A significant number of students were supported at various levels though the projects and, for some, also as interns at INL.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Modeling Integrated Electricity and Natural Gas Systems Using Co-Simulation

One approach for modeling natural gas and electricity networks as integrated systems is co-simulation, which involves separating a single model into subproblems that are solved iteratively, with coupling information passed between then. Although a fully integrated problem might be more direct, co-simulation offers advantages in that it can sometimes be more computationally tractable, and can also leverage existing simulation tools to represent the subproblems. Here we present a framework for co-simulation of natural gas and electricity networks using the HELICS tool. We provide validation of the co-simulation approach by comparing results with a fully integrated approach that uses the same simulation tool, finding that the two approaches provide identical results for a range of test cases. We then present results from natural gas and electricity network co-simulation case study of the Colorado Front Range, with an evaluation of the present-day system and also a future system with higher shares of renewable power. The presentation will close with a discussion on future avenues of work for co-simulation of gas and power networks.

co-simulation↗
DOE OSTI2026

Probabilistic Inference of Low-Surface-Brightness Galaxy Morphological Parameters Using Simulation-Based Inference

Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.

Batbayar, Bilguun [U. Chicago (main)]↗
DOE OSTI2024

Performance of high-order Godunov-type methods in simulations of astrophysical low Mach number flows

High-order Godunov methods for gas dynamics have become a standard tool for simulating different classes of astrophysical flows. Their accuracy is mostly determined by the spatial interpolant used to reconstruct the pair of Riemann states at cell interfaces and by the Riemann solver that computes the interface fluxes. In most Godunov-type methods, these two steps can be treated independently, so that many different schemes can in principle be built from the same numerical framework. Because astrophysical simulations often test out the limits of what is feasible with the computational resources available, it is essential to find the scheme that produces the numerical solution with the desired accuracy at the lowest computational cost. However, establishing the best combination of numerical options in a Godunov-type method to be used for simulating a complex hydrodynamic problem is a nontrivial task. In fact, formally more accurate schemes do not always outperform simpler and more diffusive methods, especially if sharp gradients are present in the flow. For this work, we used our fully compressible Seven-League Hydro (SLH) code to test the accuracy of six reconstruction methods and three approximate Riemann solvers on two- and three-dimensional (2D and 3D) problems involving subsonic flows only. We considered Mach numbers in the range from 10 −3 to 10 −1 , which are characteristic of many stellar and geophysical flows. In particular, we considered a well-posed, 2D, Kelvin–Helmholtz instability problem and a 3D turbulent convection zone that excites internal gravity waves in an overlying stable layer. Although the different combinations of numerical methods converge to the same solution with increasing grid resolution for most of the quantities analyzed here, we find that (i) there is a spread of almost four orders of magnitude in computational cost per fixed accuracy between the methods tested in this study, with the most performant method being a combination of a low-dissipation Riemann solver and a sextic reconstruction scheme; (ii) the low-dissipation solver always outperforms conventional Riemann solvers on a fixed grid when the reconstruction scheme is kept the same; (iii) in simulations of turbulent flows, increasing the order of spatial reconstruction reduces the characteristic dissipation length scale achieved on a given grid even if the overall scheme is only second order accurate; (iv) reconstruction methods based on slope-limiting techniques tend to generate artificial, high-frequency acoustic waves during the evolution of the flow; and (v) unlimited reconstruction methods introduce oscillations in the thermal stratification near the convective boundary, where the entropy gradient is steep.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

University Research in Support of TREAT Modeling and Simulation, FY 2017

INL is currently evolving a modeling and simulation (M&S) capability that will enable im- proved core operation as well as design and analysis of TREAT experiments. This M&S capability primarily uses MAMMOTH, a reactor physics application being developed under the Multi-physics Object Oriented Simulation Environment (MOOSE) framework. MAMMOTH allows the coupling of a number of other MOOSE-based applications. This work is being supplemented by related research projects at a number of universities. INL is funding TREAT-related research at North Carolina State University, University of New Mexico, Oregon State University, and MIT under internally funded projects within the INL/Battelle National University Consortium Program. Under the DOE Nuclear Energy Advanced Modeling and Simulation program (NEAMS), other research is being performed at MIT, University of Florida, and Texas A&M University. In addition, Dr. Barry Ganapol of University of Arizona was funded for summer work at the INL. This report is a compilation of conference papers that were submitted as part of this research by the NEAMS University partners and from the visiting professor.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Lie-algebraic classical simulations for quantum computing

The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity and in the development of quantum technologies. Efficient techniques such as those based on the Gottesman-Knill theorem for Clifford circuits, tensor networks for low entanglement-generating circuits, or Wick's theorem for fermionic Gaussian states have become central tools in quantum computing. In this work, we contribute to this body of knowledge by presenting a framework for classical simulations, dubbed “𝔤-sim”, which is based on the underlying Lie algebraic structure of the dynamical process. When the dimension of the algebra grows at most polynomially in the system size, there exist observables for which the simulation is efficient. Indeed, we show that 𝔤-sim enables new regimes for classical simulations, is able to deal with certain forms of noise in the evolution, as well as can be used to tackle several paradigmatic variational and nonvariational quantum computing tasks. For the former, we perform Lie-algebraic simulations to train and optimize parametrized quantum circuits (thus effectively showing that some variational models can be dequantized), design enhanced parameter initialization strategies, solve tasks of quantum circuit synthesis, and train a quantum-phase classifier. For the latter, we report large-scale noiseless and noisy simulations on benchmark problems. By comparing the limitations of 𝔤-sim and certain Wick's theorem-based simulations, we find that the two methods become inefficient for different types of states or observables, hinting at the existence of distinct, nonequivalent resources for classical simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

IMPLEMENTATION OF DETAILED CHEMISTRY MECHANISMS IN ENGINE SIMULATIONS

The stiffness of large chemistry mechanisms has been proved to be a major hurdle toward predictive engine simulations. As a result, detailed chemistry mechanisms with a few thousand species need to be reduced based on target conditions so that they can be accommodated within the available computational resources. The computational cost of simulations typically increases super-linearly with the number of species and reactions. This work aims to bring detailed chemistry mechanisms within the realm of engine simulations by coupling the framework of unsteady flamelets and fast chemistry solvers. A previously developed tabulated flamelet model (TFM) framework for nonpremixed combustion was used in this study. The flamelet solver consists of the traditional operator-splitting scheme with variable coefficient ordinary differential equation (ODE) solver (VODE) and a numerical Jacobian for solving the chemistry. In order to use detailed mechanisms with thousands of species, a new framework with the Livermore solver for ODEs in sparse form (LSODES) chemistry solver and an analytical Jacobian was implemented in this work. Results from 1D simulations show that with the new framework, the computational cost is linearly proportional to the number of species in a given chemistry mechanism. As a result, the new framework is 2-3 orders of magnitude faster than the conventional variable coefficient ODE (VODE) solver for large chemistry mechanisms. This new framework was used to generate unsteady flamelet libraries for ndodecane using a detailed chemistry mechanism with 2755 species and 11,173 reactions. The engine combustion network (ECN) spray A experiments, which consist of an igniting n-dodecane spray in turbulent, high-pressure engine conditions are simulated using large eddy simulations (LES) coupled with detailed mechanisms. A grid with 0.06 mm minimum cell size and 22 x 10(6) peak cell count was implemented. The framework is validated across a range of ambient temperatures against ignition delay and liftoff lengths (LOLs). Qualitative results from the simulations were compared against experimental OH and CH2O planar laser-induced fluorescence (PLIF) data. The models are able to capture the spatial and temporal trends in species compared to those observed in the experiments. Quantitative and qualitative comparisons between the predictions of the reduced and detailed mechanisms are presented in detail. The main goal of this study is to demonstrate that detailed reaction mechanisms (similar to 1000 species) can now be used in engine simulations with a linear increase in computation cost with number of species during the tabulation process and a small increase in the 3D simulation cost.

Kundu, Prithwish↗
DOE OSTI2016

Smoothed Particle Hydrodynamic Simulator

This code is a highly modular framework for developing smoothed particle hydrodynamic (SPH) simulations running on parallel platforms. The compartmentalization of the code allows for rapid development of new SPH applications and modifications of existing algorithms. The compartmentalization also allows changes in one part of the code used by many applications to instantly be made available to all applications.

Source record↗
DOE OSTI2018

Plasticization behavior in polymers of intrinsic microporosity (PIM-1): A simulation study from combined Monte Carlo and molecular dynamics

Microporous glassy polymers currently in use for gas capture and separations can undergo plasticization and swelling through a high uptake of adsorbates. In this paper, we evaluated their effects on the structural and adsorption properties of a polymer of intrinsic microporosity (PIM-1) through atomistic molecular simulations. The sorption-relaxation behavior in PIM-1 induced by CO 2 , CH 4 , H 2 , N 2 , and O 2 gases and binary mixtures have been studied using a combined approach of Monte Carlo and molecular dynamics. Through accounting for polymer relaxation, we showed that structural properties were significantly altered, especially by CO 2 and CH 4 . For adsorption properties, significant differences in loading and configuration of gases in PIM-1 were observed when comparing between fixed and flexible frameworks. Our MC/MD simulations revealed a threshold value around 3 mmol/g at 300 K at which gas loading initiate plasticization and swelling within the polymer matrix. Furthermore, working capacity and regenerability were improved upon sorption-relaxation in several cases indicating that the phenomena do not always correlate with a reduction in performance as usually assumed in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.

Barthel Sorensen, B.↗
DOE OSTI2025

Field-level reconstruction from foreground-contaminated 21-cm maps

Current and upcoming 21-cm experiments will soon be able to map 21-cm spatial fluctuations in three dimensions for a wide range of redshifts. However, bright foreground contamination and the nature of radio interferometry create significant challenges, making it difficult to access rich cosmological information from the Fourier modes that lie within the “foreground wedge”. Here, in this work, we introduce two approaches aiming to reconstruct the full 21-cm density field, including the missing modes in the wedge: (a) a field-level inference under an effective field theory (EFT) framework; (b) a diffusion-based deep generative model trained on simulations. Under the EFT framework, we implement a fully differentiable forward model that maps the initial conditions of matter fluctuations to the observed, foreground-filtered 21-cm maps. This enables a gradient-based sampler to simultaneously sample the initial conditions and bias parameters, allowing a physically motivated mode reconstruction. Alternatively, we apply a variational diffusion model to perform 21-cm density reconstruction at the map level. Our model is trained on semi-numerical simulations over a wide range of astrophysical parameters. Our results from both approaches should provide improved cosmological constraints from the field level and also enable cross-correlation between experiments that have little or no overlapping modes.

cosmological perturbation theory↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2016

Warthog: Progress on Coupling BISON and PROTEUS

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program from the Office of Nuclear Energy at the Department of Energy (DOE) provides a robust toolkit for modeling and simulation of current and future advanced nuclear reactor designs. This toolkit provides these technologies organized across product lines, with two divisions targeted at fuels and end-to-end reactor modeling, and a third for integration, coupling, and high-level workflow management. The Fuels Product Line (FPL) and the Reactor Product Line (RPL) provide advanced computational technologies that serve each respective field effectively. There is currently a lack of integration between the product lines, impeding future improvements of simulation solution fidelity. In order to mix and match tools across the product lines, a new application called Warthog was produced. Warthog is built on the Multi-physics Object-Oriented Simulation Environment (MOOSE) framework developed at Idaho National Laboratory (INL). This report details the continuing efforts to provide the Integration Product Line (IPL) with interoperability using the Warthog code. Currently, this application strives to couple the BISON fuel performance application from the FPL using the PROTEUS Core Neutronics application from the RPL. Warthog leverages as much prior work from the NEAMS program as possible, enabling interoperability between the independently developed MOOSE and SHARP frameworks, and the libMesh and MOAB mesh data formats. Previous work performed on Warthog allowed it to couple a pin cell between the two codes. However, as the temperature changed due to the BISON calculation, the cross sections were not recalculated, leading to errors as the temperature got further away from the initial conditions. XSProc from the SCALE code suite was used to calculate the cross sections as needed. The remainder of this report discusses the changes to Warthog to allow for the implementation of XSProc as an external code. It also discusses the changes made to Warthog to allow it to fit more cleanly into the MultiApp syntax of the MOOSE framework. The capabilities, design, and limitations of Warthog will be described, in addition to some of the test cases that were used to demonstrate the code. Future plans for Warthog will be discussed, including continuation of the modifications to the input and coupling to other SHARP codes such as Nek5000.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

PyFLOSIC: Python-based Fermi–Löwdin orbital self-interaction correction

We present pyflosic, an open-source, general-purpose python implementation of the Fermi–Löwdin orbital self-interaction correction (FLO-SIC), which is based on the python simulation of chemistry framework (pyscf) electronic structure and quantum chemistry code. Thanks to pyscf, pyflosic can be used with any kind of Gaussian-type basis set, various kinds of radial and angular quadrature grids, and all exchange-correlation functionals within the local density approximation, generalized-gradient approximation (GGA), and meta-GGA provided in the libxc and xcfun libraries. A central aspect of FLO-SIC is the Fermi-orbital descriptors, which are used to estimate the self-interaction correction. Importantly, they can be initialized automatically within pyflosic; they can also be optimized within pyflosic with an interface to the atomic simulation environment, a python library that provides a variety of powerful gradient-based algorithms for geometry optimization. Although pyflosic has already facilitated applications of FLO-SIC to chemical studies, it offers an excellent starting point for further developments in FLO-SIC approaches, thanks to its use of a high-level programming language and pronounced modularity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Pruning of Discrete Dynamic Event Trees Using Density Peaks and Dynamic Time Warping

Discrete Dynamic Event Trees (DDETs) are used in probabilistic risk assessment (PRA) to determine the risk associated with complex systems, such as nuclear power plants, where accident scenarios need to be realistically modeled. One of the most challenging aspects of the DDET method is that a large number of branches are produced for each initiating event. This can result in data that is difficult to organize and time consuming to analyze. As the number of uncertain parameters increases, so does the complexity of the event tree. An event tree often has nodes that do not contribute to the overall results and predictive capabilities; therefore, we suggest a pre-pruning algorithm that can work to remove these nodes, and consequently, reduce the complexity of the tree. Sandia National Laboratories (SNL) is currently working on a two year dynamic cyber-risk informed project to assess cyber failure in a nuclear power plant. This will be achieved by coupling SNL's dynamic scheduler known as Analysis of Dynamic Accident Progression Trees (ADAPT) with MELCOR, a reactor accident simulator, and a System for Circuit Evaluation and Prediction of Transient Radiation Effects (SCEPTRE), a cyber Emulytics code. This paper will discuss the preliminary results of a feasibility study for using density peaks (DP) clustering with dynamic time warping (DTW) to prune branches that are formed from large DDETs, like the ones that will be produced throughout SNL's project. Papers from the project that are submitted to this conference discuss the following: - The MELCOR model for a Pressurized Water Reactor that uses a digital instrumentation and control system. - Proposed generic cyber exploits for the above reactor. - Modifications to ADAPT to extend the framework for multiple simulators, incorporate importance measures into DDET analysis, as well as sort and reduce DDETs based on inputs to or outputs from the tree. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

SOMAFOAM: An OpenFOAM based solver for continuum simulations of low-temperature plasmas

Here, we report the development of SOMAFOAM, a finite volume framework for performing continuum simulations of low-temperature plasmas. The primary goal of this work is to discuss the features of SOMAFOAM along with representative results provided as examples for a range of operating conditions and geometries. This includes plasma and plasma–dielectric systems operating in direct current, radio frequency, and microwave regimes from pressures as low as 100 mTorr to atmospheric pressure. The code has several useful features including the ability to run massively parallel simulations using arbitrary geometries, structured/unstructured meshes, choice of models such as drift–diffusion/full-momentum at runtime, and species-dependent timesteps to name a few. The verification/validation studies presented include comparison with previously published continuum simulations (low-pressure direct current and radio frequency plasma), with experiments (Gaseous Electronics Conference Reference Cell and microwave microplasma ignited in a split ring resonator), and previously published kinetic simulations (low-pressure radio frequency plasma). Other examples provided include a direct current atmospheric pressure microplasma bounded by dielectric sidewalls and a helium–nitrogen plasma ignited using a needle electrode facing a dielectric. The performance of the code is also discussed with serial and distributed memory parallel runs demonstrated up to 512 cores. The design and implementation of the code in a modular object-oriented framework allows for easy extension and seamless coupling with other codes and can be expected to play an important role in both academia and industry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Numerical Codes for the DESC-LSST Analysis Pipeline: Core Cosmology Library Standard Modules and Beyond wCDM Modules (Final Technical Report)

The overall objective of the project is to investigate and develop specific software modules and analysis components for the software pipeline of LSST Dark Energy Science Collaboration (DESC). Following the key projects of DESC Science Roadmap (SRM), we will write and test computer codes for the Core Cosmology Library (CCL) in order to complete its modules, functionalities, and interface to work with the analysis pipelines from the five science probes of DESC (parts of SRM deliverables CX4.2TJP, CX6.2CS). We will also code modules for CCL to test models beyond w-Cold-Dark-Matter (wCDM) and modification to gravity (MG). Interfaces for MG models will also be developed for the TJPCOSMO software which is the main pipeline of the Theory and Joint Probe (TJP) working group (deliverable TJP2.3). In order to use the full power of LSST data to constrain MG models, we will also work on constraints from nonlinear regime by running and analyzing MG N-Body simulations using a Parameterized-Post-Friedmann framework into the Gadget-2 simulation package (deliverable TJP2.2). Preliminary results for the simulations were obtained in the past. In collaboration with other DESC groups, we plan to make these simulations feedable to cosmic emulators that are practical for likelihood analyzes (deliverable TJP2.2, parts of CX6.2CS). We will also modify and integrate our current codes for consistency tests between data sets and probes into the pipeline (parts of deliverables CX8.2TJP, TJP2.3). We understand that other groups will contribute to some of these objectives but our team will focus and collaborate with others on the particular part of testing MG and models beyond wCDM and refine the DESC pipeline for this purpose. PI has been coordinating his work with the TJP and CS working groups and the DESC management team. PI is a full member of DESC since June 2013. He and his students have been contributing to LSST-DESC activities and work including TJP telecons, collaboration meetings, hack-weeks, and workshops. PI chaired or co-chaired sessions at collaboration meetings and hack-weeks about testing gravity and models beyond wCDM using LSST. He is coordinating the TJP2 projects for testing models beyond wCDM including the writing of DESC-research-note, development of code for pipeline, and N-Body simulations for MG and beyond wCDM models testable with LSST analyses. As stressed in the DESC white paper, SRM, and P5 report, one of the important questions in understanding cosmic acceleration and dark energy is to be able to distinguish whether the acceleration is due to a dark energy component in the universe or a modification to gravity. Answering these questions will have a significant impact on the question of cosmic acceleration and dark energy. The methods that we will use include analytical work, numerical code, and N-Body simulations. A first approach that that we will use consists of using growth rate parameters that enter the perturbed dynamics equations. These parameters take distinctive values for distinct gravity theories and have potential to distinguish between Dark Energy and Modified Gravity. The second method is to look for inconsistencies in Dark Energy parameter spaces using specific combinations of cosmological data sets. Our investigation addresses the Dark Energy problem that is relevant to the mission of the HEP program to understand how our universe works at its most fundamental level. It will allow us to make progress on the HEP mission to explore the nature of Dark Energy and the basic nature of space and time using future surveys such as LSST. The investigation supports the DOE HEP program Cosmic Frontier as it will contribute to the study and understanding of dark energy and fundamental properties of the universe. The investigation contributes directly to LSST-DESC key projects and their deliverables as described in the Science Road-map document to build analysis pipeline and to test dark energy and beyond wCDM models using LSST.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗