George Shu Heng Pau

An adaptive mesh refinement algorithm for compressible two-phase flow in porous media

We describe a second-order accurate sequential algorithm for solving two-phase multicomponent flow in porous media. The algorithm incorporates an unsplit second-order Godunov scheme that provides accurate resolution of sharp fronts. The method is implemented within a block structured adaptive mesh refinement (AMR) framework that allows grids to dynamically adapt to features of the flow and enables efficient parallelization of the algorithm. We demonstrate the second-order convergence rate of the algorithm and the accuracy of the AMR solutions compared to uniform fine-grid solutions. The algorithm is then used to simulate the leakage of gas from a Liquified Petroleum Gas (LPG) storage cavern, demonstrating its capability to capture complex behavior of the resulting flow. We further examine differences resulting from using different relative permeability functions.

Observation of Multiple, Identical Binding Sites in the Exchange of Carboxylic Acid Ligands with CdS Nanocrystals.

We study ligand exchange between the carboxylic acid group and 5.0 nm oleic-acid capped CdS nanocrystals (NCs) using fluorescence resonance energy transfer (FRET). This is the first measurement of the initial binding events between cadmium chalcogenide NCs and carboxylic acid groups. The binding behavior can be described as an interaction between a ligand with single binding group and a substrate with multiple, identical binding sites. Assuming Poissonian binding statistics, our model fits both steady-state and time-resolved photoluminescence (SSPL and TRPL, respectively) data well. A modified Langmuir isotherm reveals that a CdS nanoparticle has an average of 3.0 new carboxylic acid ligands and binding constant, Ka, of 3.4 × 10(5) M(-1).

Reduced order models for many-query subsurface flow applications

Inverse modeling involves repeated evaluations of forward models, which can be computationally prohibitive for large numerical models. To reduce the overall computational burden of these simulations, we study the use of reduced order models (ROMs) as numerical surrogates. These ROMs usually involve using solutions to high-fidelity models at different sample points within the parameter space to construct an approximate solution at any point within the parameter space. This paper examines an input–output relational approach based on Gaussian process regression (GPR). We show that these ROMs are more accurate than the linear lookup tables with the same number of high-fidelity simulations. We describe an adaptive sampling procedure that automatically selects optimal sample points and demonstrate the use of GPR to a smooth response surface and a response surface with abrupt changes. We also describe how GPR can be used to construct ROMs for models with heterogeneous material properties. Finally, we demonstrate how the use of a GPR-based ROM in two many-query applications—uncertainty quantification and global sensitivity analysis—significantly reduces the total computational effort.

A high-performance workflow system for subsurface simulation

The U.S. Department of Energy (DOE) recently invested in developing a numerical modeling toolset called ASCEM (Advanced Simulation Capability for Environmental Management) to support modeling analyses at legacy waste sites. This investment includes the development of an open-source user environment called Akuna that manages subsurface simulation workflows. Core toolsets accessible through the Akuna user interface include model setup, grid generation, sensitivity analysis, model calibration, and uncertainty quantification. Additional toolsets are used to manage simulation data and visualize results. This new workflow technology is demonstrated by streamlining model setup, calibration, and uncertainty analysis using high performance computation for the BC Cribs Site, a legacy waste area at the Hanford Site in Washington State. For technetium-99 transport, the uncertainty assessment for potential remedial actions (e.g., surface infiltration covers) demonstrates that using multiple realizations of the geologic conceptual model results in greater variation in concentration predictions than when a single model is used. Akuna provides integrated toolset needed for subsurface modeling workflow.Akuna streamlines process of executing multiple simulations in HPC environment.Akuna provides visualization tools for spatial and temporal data.Example application demonstrates risk with remediation impacting infiltration rates.

Reduced order modeling in iTOUGH2

The inverse modeling and uncertainty quantification capabilities of iTOUGH2 are augmented with reduced order models (ROMs) that act as efficient surrogates for computationally expensive high fidelity models (HFMs). The implementation of the ROM capabilities involves integration of three main computational components. The first component is the ROM itself. Two response surface approximations are currently implemented: Gaussian process regression (GPR) and radial basis function (RBF) interpolation. The second component is a multi-output adaptive sampling procedure that determines the sample points used to construct the ROMs. The third component involves defining appropriate error measures for the adaptive sampling procedure, allowing ROMs to be constructed efficiently with limited user intervention. Details in all three components must complement one another to obtain an accurate approximation. The new capability and its integration with other analysis tools within iTOUGH2 are demonstrated in two examples. The results from using the ROMs in an uncertainty quantification analysis and a global sensitivity analysis compare favorably with the results obtained using the HFMs. GPR is more accurate than RBF, but the difference can be small and similar conclusion can be deduced from the analyses. In the second example involving a realistic numerical model for a hypothetical industrial-scale carbon storage project in the Southern San Joaquin Basin, California, USA, significant reduction in computational effort can be achieved when ROMs are used to perform a rigorous global sensitivity analysis.

TOUGH3: A new efficient version of the TOUGH suite of multiphase flow and transport simulators

The TOUGH suite of nonisothermal multiphase flow and transport simulators has been updated by various developers over many years to address a vast range of challenging subsurface problems. The increasing complexity of the simulated processes as well as the growing size of model domains that need to be handled call for an improvement in the simulators computational robustness and efficiency. Moreover, modifications have been frequently introduced independently, resulting in multiple versions of TOUGH that (1) led to inconsistencies in feature implementation and usage, (2) made code maintenance and development inefficient, and (3) caused confusion to users and developers. TOUGH3—a new base version of TOUGH—addresses these issues. It consolidates both the serial (TOUGH2 V2.1) and parallel (TOUGH2-MP V2.0) implementations, enabling simulations to be performed on desktop computers and supercomputers using a single code. New PETSc parallel linear solvers are added to the existing serial solvers of TOUGH2 and the Aztec solver used in TOUGH2-MP. The PETSc solvers generally perform better than the Aztec solvers in parallel and the internal TOUGH3 linear solver in serial. TOUGH3 also incorporates many new features, addresses bugs, and improves the flexibility of data handling. Due to the improved capabilities and usability, TOUGH3 is more robust and efficient for solving tough and computationally demanding problems in diverse scientific and practical applications related to subsurface flow modeling.

Ligand Binding to Distinct Sites on Nanocrystals Affecting Energy and Charge Transfer.

Hybrid optoelectronic devices are attractive because they offer the promise of low-cost, roll-to-roll fabrication. Despite this, energy transfer between organic and inorganic interfaces is not well understood. Device engineering on this class of solution-processed materials generally focuses on replacing the long insulating ligands with short ones. Here, we show that energy and charge transfer between an inorganic nanocrystal (NC) donor and organic molecular acceptor is acutely sensitive to the chemical moiety linking the two species. Our results reveal that the CdS NCs have distinct binding sites for different chemical species because only resonance energy transfer (RET) is observed for the carboxylic-acid-functionalized ligand, while both RET and charge transfer are observed for the amine-functionalized ligand. We observe that the equilibrium constant for this static quenching term increases with decreasing particle size. This finding offers a new approach in the design of hybrid thin films for devices and NC probes based on RET used for imaging, sensing, signal transduction, and photon management.

iTOUGH2: A multiphysics simulation-optimization framework for analyzing subsurface systems

Abstract iTOUGH2 is a simulation-optimization framework for the TOUGH suite of nonisothermal multiphase flow models and related simulators of geophysical, geochemical, and geomechanical processes. After appropriate parameterization of subsurface structures and their properties, iTOUGH2 runs simulations for multiple parameter sets and analyzes the resulting output for parameter estimation through automatic model calibration, local and global sensitivity analyses, data-worth analyses, and uncertainty propagation analyses. Development of iTOUGH2 is driven by scientific challenges and user needs, with new capabilities continually added to both the forward simulator and the optimization framework. This review article provides a summary description of methods and features implemented in iTOUGH2, and discusses the usefulness and limitations of an integrated simulation-optimization workflow in support of the characterization and analysis of complex multiphysics subsurface systems.

Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data

Abstract Bayesian inverse modeling techniques are computationally expensive because many forward simulations are needed when sampling the posterior distribution of the parameters. In this paper, we combine the implicit sampling method and generalized polynomial chaos expansion (gPCE) to significantly reduce the computational cost of performing Bayesian inverse modeling. There are three steps in this approach: (1) find the maximizer of the likelihood function using deterministic approaches; (2) construct a gPCE-based surrogate model using the results from a limited number of forward simulations; and (3) efficiently sample the posterior distribution of the parameters using implicit sampling method. The cost of constructing the gPCE-based surrogate model is further decreased by using sparse Bayesian learning to reduce the number of gPCE coefficients that have to be determined. We demonstrate the approach for a synthetic ponded infiltration experiment simulated with TOUGH2. The surrogate model is highly accurate with mean relative error that is 0.035 % in predicting saturation and 0.25 % in predicting the likelihood function. The posterior distribution of the parameters obtained using our proposed technique is nearly indistinguishable from the results obtained from either an implicit sampling method or a Markov chain Monte Carlo method utilizing the full model.

Iterative Importance Sampling Algorithms for Parameter Estimation

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is a challenging task. Several sampling algorithms have been proposed over the past years that take an iterative approach to constructing a proposal distribution. We investigate the applicability of such algorithms by applying them to two realistic and challenging test problems, one in subsurface flow, and one in combustion modeling. More specifically, we implement importance sampling algorithms that iterate over the mean and covariance matrix of Gaussian or multivariate t-proposal distributions. Our implementation lever...