Publications

Safta, Cosmin S.; Huan, Xun H.; Najm, H.N.; Sargsyan, Khachik S.; Eldred, Michael S.; Geraci, Gianluca G.

Abstract not provided.

Najm, H.N.; Eldred, Michael S.; Debusschere, Bert D.; Chowdhary, Kamaljit S.; Jakeman, John D.; Safta, Cosmin S.; Sargsyan, Khachik S.

Abstract not provided.

Christopher, Blais C.; Diaz-Ibarra, Oscar H.; Mazeau, Emily J.; Gierada, Maciej G.; Hermes, Eric H.; Safta, Cosmin S.; Kim, Kyungjoo K.; Najm, H.N.; Bylaska, Eric J.; Zador, Judit Z.; Goldsmith, C.F.; West, Richard H.

Abstract not provided.

Michelsen, Hope A.; Bambha, Ray B.; Liu, Zhen L.; Schrader, Paul E.; Ivey, Mark D.; Roesler, Erika L.; Taylor, Mark A.; LaFranchi, Brian L.; Helsel, Frederick M.; Najm, H.N.; Sargsyan, Khachik S.; Safta, Cosmin S.

Abstract not provided.

Diaz-Ibarra, Oscar H.; Kim, Kyungjoo K.; Safta, Cosmin S.; Najm, H.N.

CSPlib is an open source software library for analyzing general ordinary differential equation (ODE) systems and detailed chemical kinetic ODE systems. It relies on the computational singular perturbation (CSP) method for the analysis of these systems. The software provides support for: General ODE models (gODE model class) for computing source terms and Jacobians for a generic ODE system; TChem model (ChemElemODETChem model class) for computing source term, Jacobian, other necessary chemical reaction data, as well as the rates of progress for a homogenous batch reactor using an elementary step detailed chemical kinetic reaction mechanism. This class relies on the TChem [2] library; A set of functions to compute essential elements of CSP analysis (Kernel class). This includes computations of the eigensolution of the Jacobian matrix, CSP basis vectors and co-vectors, time scales (reciprocals of the magnitudes of the Jacobian eigenvalues), mode amplitudes, CSP pointers, and the number of exhausted modes. This class relies on the Tines library; A set of functions to compute the eigensolution of the Jacobian matrix using Tines library GPU eigensolver; A set of functions to compute CSP indices (Index Class). This includes participation indices and both slow and fast importance indices.

Diaz-Ibarra, Oscar H.; Kim, Kyungjoo K.; Safta, Cosmin S.; Najm, H.N.

CSPlib is an open source software library for analyzing general ordinary differential equation (ODE) systems and detailed chemical kinetic ODE/DAE systems. It relies on the computational singular perturbation (CSP) method for the analysis of these systems.

Sargsyan, Khachik S.; Najm, H.N.; Safta, Cosmin S.; Michelsen, Hope A.; Bambha, Ray B.; Liu, Zhen L.; van Bloemen Waanders, Bart G.

Abstract not provided.

Najm, H.N.; Ghanem, Roger G.; Eldred, Michael S.

Abstract not provided.

Adams, Brian M.; Devine, Karen D.; Najm, H.N.; Marzouk, Youssef M.

Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern since the anthrax attacks of 2001. The ability to characterize the parameters of such attacks, i.e., to estimate the number of people infected, the time of infection, the average dose received, and the rate of disease spread in contemporary American society (for contagious diseases), is important when planning a medical response. For non-contagious diseases, we address the characterization problem by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To keep the approach relevant for response planning, we limit ourselves to 3.5 days of data. In computational tests performed for anthrax, we usually find these observation windows sufficient, especially if the outbreak model employed in the inverse problem is accurate. For contagious diseases, we formulated a Bayesian inversion technique to infer both pathogenic transmissibility and the social network from outbreak observations, ensuring that the two determinants of spreading are identified separately. We tested this technique on data collected from a 1967 smallpox epidemic in Abakaliki, Nigeria. We inferred, probabilistically, different transmissibilities in the structured Abakaliki population, the social network, and the chain of transmission. Finally, we developed an individual-based epidemic model to realistically simulate the spread of a rare (or eradicated) disease in a modern society. This model incorporates the mixing patterns observed in an (American) urban setting and accepts, as model input, pathogenic transmissibilities estimated from historical outbreaks that may have occurred in socio-economic environments with little resemblance to contemporary society. Techniques were also developed to simulate disease spread on static and sampled network reductions of the dynamic social networks originally in the individual-based model, yielding faster, though approximate, network-based epidemic models. These reduced-order models are useful in scenario analysis for medical response planning, as well as in computationally intensive inverse problems.

Safta, Cosmin S.; Sargsyan, Khachik S.; Bambha, Ray B.; Michelsen, Hope A.; Debusschere, Bert D.; Najm, H.N.; van Bloemen Waanders, Bart G.

Abstract not provided.

Najm, H.N.; Sargsyan, Khachik S.; Kim, Kyungjoo K.; Goldsmith, C.F.; West, Richard H.; Bylaska, Eric J.; Bross, David H.; Ruscic, Branko R.; Safta, Cosmin S.; Zador, Judit Z.; Blais, Christopher B.; Blöndal, Katrín B.; Diaz-Ibarra, Oscar H.; Hermes, Eric H.; Mazeau, Emily J.

Abstract not provided.

Casey, Tiernan A.; Debusschere, Bert D.; Eldred, Michael S.; Geraci, Gianluca G.; Ghanem, Roger G.; Jakeman, John D.; Marzouk, Youssef M.; Najm, H.N.; Safta, Cosmin S.; Sargsyan, Khachik S.

Abstract not provided.

AIAA Journal

Huan, Xun H.; Safta, Cosmin S.; Geraci, Gianluca G.; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem M.; Oefelein, Joseph C.; Najm, H.N.

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertainparameters involvedandthe high computational costofflow simulations. These difficulties are addressedin this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying themin the current studyto large-eddy simulations ofajet incrossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system's stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. These methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.

AIAA Journal

Huan, Xun H.; Safta, Cosmin S.; Sargsyan, Khachik S.; Geraci, Gianluca G.; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem L.; Oefelein, Joseph C.; Najm, H.N.

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. Finally, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.

Huan, Xun H.; Safta, Cosmin S.; Sargsyan, Khachik S.; Geraci, Gianluca G.; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem M.; Oefelein, Joseph C.; Najm, H.N.

Abstract not provided.

Huan, Xun H.; Safta, Cosmin S.; Sargsyan, Khachik S.; Geraci, Gianluca G.; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem M.; Oefelein, Joseph C.; Najm, H.N.

Abstract not provided.

Huan, Xun H.; Safta, Cosmin S.; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem M.; Oefelein, Joseph C.; Sargsyan, Khachik S.; Najm, H.N.

Abstract not provided.

Liu, Zhen L.; Safta, Cosmin S.; Sargsyan, Khachik S.; Najm, H.N.; van Bloemen Waanders, Bart G.; LaFranchi, Brian L.; Ivey, Mark D.; Schrader, Paul E.; Michelsen, Hope A.; Bambha, Ray B.

In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of green- house gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO₂ . This will allow for the examination of regional-scale transport and distribution of CO₂ along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO₂ inversions. We have tested the approach using data and model outputs from the TransCom3 global CO₂ inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses de- terministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two ap- proaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and un- certainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model - FLEXPART-WRF. These two models share the same WRF assimilated meteorology fields, making it possible to perform a hybrid simulation, in which the Eulerian model (CMAQ) can be used to compute the initial condi- tion needed by the Lagrangian model, while the source-receptor relationships for a large state vector can be efficiently computed using the Lagrangian model in its backward mode. In ad- dition, CMAQ has a complete treatment of atmospheric chemistry of a suite of traditional air pollutants, many of which could help attribute GHGs from different sources. The inference of emissions sources using atmospheric observations is cast as a Bayesian model calibration problem, which is solved using a variety of Bayesian techniques, such as the bias-enhanced Bayesian inference algorithm, which accounts for the intrinsic model deficiency, Polynomial Chaos Expansion to accelerate model evaluation and Markov Chain Monte Carlo sampling, and Karhunen-Lo %60 eve (KL) Expansion to reduce the dimensionality of the state space. We have established an atmospheric measurement site in Livermore, CA and are collect- ing continuous measurements of CO₂ , CH₄ and other species that are typically co-emitted with these GHGs. Measurements of co-emitted species can assist in attributing the GHGs to different emissions sectors. Automatic calibrations using traceable standards are performed routinely for the gas-phase measurements. We are also collecting standard meteorological data at the Livermore site as well as planetary boundary height measurements using a ceilometer. The location of the measurement site is well suited to sample air transported between the San Francisco Bay area and the California Central Valley.

Safta, Cosmin S.; Geraci, Gianluca G.; Eldred, Michael S.; Najm, H.N.; Riegner, David R.; Windl, Wolfgang W.

This study explores a Bayesian calibration framework for the RAMPAGE alloy potential model for Cu-Ni and Cu-Zr systems, respectively. In RAMPAGE potentials, it is proposed that once calibrated potentials for individual elements are available, the inter-species interac- tions can be described by fitting a Morse potential for pair interactions with three parameters, while densities for the embedding function can be scaled by two parameters from the elemen- tal densities. Global sensitivity analysis tools were employed to understand the impact each parameter has on the MD simulation results. A transitional Markov Chain Monte Carlo al- gorithm was used to generate samples from the multimodal posterior distribution consistent with the discrepancy between MD simulation results and DFT data. For the Cu-Ni system the posterior predictive tests indicate that the fitted interatomic potential model agrees well with the DFT data, justifying the basic RAMPAGE assumtions. For the Cu-Zr system, where the phase diagram suggests more complicated atomic interactions than in the case of Cu-Ni, the RAMPAGE potential captured only a subset of the DFT data. The resulting posterior distri- bution for the 5 model parameters exhibited several modes, with each mode corresponding to specific simulation data and a suboptimal agreement with the DFT results.

Safta, Cosmin S.; Phipps, Eric T.; Najm, H.N.

Abstract not provided.

D'Elia, Marta D.; Bochev, Pavel B.; Foster, John E.; Glusa, Christian A.; Gulian, Mamikon G.; Gunzburger, Max G.; Trageser, Jeremy T.; Kuhlman, Kristopher L.; Martinez, Mario A.; Najm, H.N.; Silling, Stewart A.; Tupek, Michael T.; Xu, Xiao X.

Nonlocal models provide a much-needed predictive capability for important Sandia mission applications, ranging from fracture mechanics for nuclear components to subsurface flow for nuclear waste disposal, where traditional partial differential equations (PDEs) models fail to capture effects due to long-range forces at the microscale and mesoscale. However, utilization of this capability is seriously compromised by the lack of a rigorous nonlocal interface theory, required for both application and efficient solution of nonlocal models. To unlock the full potential of nonlocal modeling we developed a mathematically rigorous and physically consistent interface theory and demonstrate its scope in mission-relevant exemplar problems.

Safta, Cosmin S.; Huan, Xun H.; Geraci, Gianluca G.; Eldred, Michael S.; Sargsyan, Khachik S.; Vane, Zachary P.; Oefelein, Joseph C.; Najm, H.N.

Abstract not provided.

Huan, Xun H.; Geraci, Gianluca G.; Vane, Zachary P.; Safta, Cosmin S.; Eldred, Michael S.; Oefelein, Joseph C.; Najm, H.N.; Sargsyan, Khachik S.

Abstract not provided.

Huan, Xun H.; Geraci, Gianluca G.; Safta, Cosmin S.; Eldred, Michael S.; Sargsyan, Khachik S.; Vane, Zachary P.; Oefelein, Joseph C.; Najm, H.N.

Abstract not provided.

Debusschere, Bert D.; Adalsteinsson, Helgi A.; Long, Kevin R.; Najm, H.N.; Hickner, Michael A.; Simmons, Blake S.; Davalos, Rafael V.; Ponce, Pierre P.

Abstract not provided.