Second Annual Texas A&M Research Computing Symposium

Last Updated: June 10, 2018


Numerical study of traveling wave oscillation on turbulent flow: from channel flow to separated flow

Amir Akbarzadeh, Iman Borazjani
Department of Mechanical Engineering, Texas A&M University,

Flow separation over an inclined flat plate, airfoil (turbine blade) reduces aerodynamic performance and efficiency. In order to overcome the flow separation multiple methods such as vortex generators, boundary layer trips and periodic excitation have been employed to reduce separation (increase lift coefficient, decrease drag coefficient and improve flow reattachment). In addition, in nature, flying birds/bats and hovering insects can control the flow separation via their flexible moving wings. Recently, the flow reattachment also has been observed among aquatic animals like stingray. Here, we study flow control separation via undulatory motion inspired from aquatic swimmers using our immersed boundary, large-eddy simulations (LES). The undulation is a traveling wave, which has a constant amplitude of 0.01 with respect to chord length and a different wavelength (lan) and reduced frequency (f*=fL/U, f: frequency, and U: free stream velocity) for each case. Furthermore, we study the effect of these waves on turbulence statistics for an open channel flow. Parallelism is achieved by PETSc library and the speed of our simulations is studied.

A Numerical Diagonal Preconditioner for Matrix-free Newton-Krylov Method for Solving Navier-Stokes Equations

Hossein Asadi, Mechanical Engineering Department, Texas A&M University,
Iman Borazjani, Mechanical Engineering Department, Texas A&M University

One of the main applications of parallel computing in mechanical engineering is to solve system of nonlinear Navier-Stokes equations. We used Petsc libraries based on MPI to solve these equations on large domains to study vortex dynamics. A new exact numerical preconditioner is developed to solve the system using the matrix-free Newton-Krylov method in parallel. Its required number of iterations, computational time, and parallel performance is compared to our previously developed approximate analytical preconditioner.

A Novel Derivative-Free Optimization Method on Single Dimension Projection

Authors: Ishan Bajaj, M. M. Faruque Hasan; Affiliation: Department of Chemical Engineering; email:,

With the advent of high-fidelity, complex process modeling and simulation in many areas including computational fluid dynamics-based reactor design, Nonlinear Algebraic and Partial Differential Equations (NAPDE)-based multiscale optimization, data-driven methods are gaining more importance than ever. Most often, these optimization problems are formulated as black-box problems where the objective function and/or constraints cannot be expressed analytically as explicit functions of decision variables. This implies that the values of the objective and/or constraints become available only after performing a full-scale expensive simulation. To this end, derivative free optimization (DFO) has emerged as a promising approach to optimize black-box problems. In this work, we present an algorithm to address multi-dimensional black-box problems based on projection onto a one-dimensional space. Specifically, we consider a problem of minimizing G(t), which can be interpreted as the projection of the original objective function f(x)taken in the t-space defined as sum of decision variables. We show that the minimum of G(t) also corresponds to the minimum of f(x). This enables us to solve the original optimization problem in two steps. The first step involves identifying G(t) and is referred as the inner loop. In this work, the inner loop is applied at discrete values of t. Once the solution at the previous t is known, sensitivity theorem is utilized to obtain close solution for the inner loop at next t. The second step is optimizing the univariate function G(t) to find the solution of the original problem. The algorithm is applied to a large set of test problems and compared to existing model-based DFO solvers.

DFT Study of Hydrocarbon Decomposition Mechanism on Copper Catalyst: Precursors to Graphene Growth

Behnaz Rahmani Didar, Chemical Engineering Department,
Perla B. Balbuena, Chemical Engineering Department,

In the past few decades, research on graphene and carbon nanotubes has demonstrated their extraordinary electrical, thermal, mechanical and optical properties. However their commercial and widespread use has been stifled by upscaling and quality control challenges. Chemical Vapor Deposition is the most common method of synthesis of these materials whereby a carbon precursor gas is deposited and decomposed on the surface of a catalyst at high temperatures. A key element in this procedure is the catalyst. Traditional and most common catalysts include transition metals such as Fe, Co and Ni and more recently Cu. Among those metals, Cu has proven to produce better quality graphene. However, the mechanism of growth with Cu, is not completely understood although it has been indicated to be different from other traditional metals. Knowledge of this mechanism can provide valuable information regarding the species involved in the initial stages of growth and factors that can affect this mechanism. This study investigates the decomposition pathway of ethylene, acetylene and methane using theoretical methods. Density Functional Theory (DFT) is used to find adsorption sites and energies of all initial and intermediate species. Then, DFT-NEB calculations are performed to find reaction (dehydrogenation, isomerization and C-C bond breaking) and diffusion barriers. Results provide invaluable information regarding the chemistry of the species in the early stages of graphene nucleation. These results are currently being combined with kinetic Monte Carlo to determine the decomposition pathway of these carbon-containing gases and the mechanism of nucleation on Cu.

Seasonal Predictability Study of Tropical Cyclones Using a High-Resolution Tropical Channel Model

Dan Fu (Texas A&M;, Ping Chang (Texas A&M)
Christina M. Patricola (Lawrence Berkeley National Laboratory)

The hyperactive 2017 Atlantic hurricane season reminds us of the threat tropical cyclones (TCs) posing on lives and property, and the value of improving seasonal TC forecasting. In this poster, we present results of a seasonal predictability study of TCs using a 27-km horizontal resolution Tropical Channel Model (TCM) based on the Weather Research and Forecasting (WRF) model with latitudinal extent from 30°S to 50°N. We performed 10-member ensemble simulations of northern hemisphere hurricane seasons over the 1980 – 2016 period, using boundary conditions from the 6-hour National Centers for Environmental Prediction – Climate Forecast System (NCEP-CFS) datasets. Interannual variations of TCs over the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NA) were well captured with correlation coefficients between simulated and observed accumulated cyclone energy (ACE) of 0.81, 0.59, and 0.60, respectively. Furthermore, the pronounced increasing trend of NA TC activity observed in recent decades was also well simulated. Cluster analysis revealed that model has the highest skills to predict those TCs directly migrated towards Gulf of Mexico. On the basis of these encouraging results, we made the successful reforecast for the 2017 hurricane season by downscaling the global seasonal forecast from CFS.

Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain

Ehsan Hajiramezanali, Department of Electrical and Computer Engineering, Texas A&M University (
Siamak Zamani Dadaneh, Department of Electrical and Computer Engineering, Texas A&M University
Paul de Figueiredo, Department of Microbial Pathogenesis and Immunology, Texas A&M Health Science Center
Sing-Hoi Sze, Department of Computer Science and Engineering, and Department of Biochemistry & Biophysics, Texas A&M University
Mingyuan Zhou, Department of Information, Risk, and Operations Management (IROM), The University of Texas at Austin
Xiaoning Qian, Department of Electrical and Computer Engineering, Texas A&M University (

Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments. Nonetheless, the majority of existing statistical tools for analyzing NGS data lack the capability of exploiting the richer information embedded in temporal data. Several recent tools have been developed to analyze such data but they typically impose strict model assumptions, such as smoothness on gene expression dynamic changes. To capture a broader range of gene expression dynamic patterns, we develop the gamma Markov negative binomial (GMNB) model that integrates a gamma Markov chain into a negative binomial distribution model, allowing flexible temporal variation in NGS count data. Using Bayes factors, GMNB enables more powerful temporal gene differential expression analysis across different phenotypes or treatment conditions. In addition, it naturally handles the heterogeneity of sequencing depth in different samples, removing the need for ad-hoc normalization. Efficient Gibbs sampling inference of the GMNB model parameters is achieved by exploiting novel data augmentation techniques. Extensive experiments on both simulated and real-world RNA-seq data show that GMNB outperforms existing methods in both receiver operating characteristic (ROC) and precision-recall (PR) curves of differential expression analysis results.

Differential Gene expression in Tribolium: Transcriptional Control

Sher Afzal Khan, Heather Eggleston and Zach N. Adelman (Department of Entomology, Texas A & M University College Station, TX 77843 (

Transcriptomic studies of Tribolium castaneum have led to intense advances in understanding of co-regulation and differential expression of genes in development. However, previously used microarrays approach covered only a subset of known genes. The aim of this study was to investigate the gene expression regulation pattern of beetle embryo, germline and somatic tissues. We dissect the whole organism in dynamic development stage, carcass and germline tissues were used to further analyze stage and gender specific differential gene expression by using the deep RNA sequencing (Seq) approach. We identified 12302 loci corresponding to expressed transcripts (polyA) in beetle genome and identified differentially expressed up and down regulated genes among all samples, for example, 1632 and 3641 differentially upregulated expressed transcripts (≥2- log2 fold, P < 0.05) in testes vs ovary (virgin female) and ovary vs embryo (≤ 5 hours), respectively. Of these, many development, somatic and germlines specific unique factors were identified. Furthermore, many maternal deposited transcripts were characterized, concurrent with oogenesis which are either degraded rapidly or persist during embryogenesis. Genes with largest effect size were predominantly down regulated at early embryogenesis compare to ovary. This study defines genes induced after fertilization. Genes with greater effect sizes were largely upregulated at the germline tissues. From the biological point of view the enriched functional annotation of genes were determined at specific stage. Our comprehensive transcriptome data showed that in T. castaneum, the genes are differentially co-regulated in expression and are decisively assigned to a specific differentiation in development. The information should be acquired for desirable manipulation in insects. "

Characterization of the Stiffness Distribution in 2D and 3D

Baik Jin Kim, Mechanical Engineering, TAMU,
Ping Luo, High Performance Research Computing, TAMU
Yue Mei, Zienkiewicz Centre for Computational Engineering, Swansea University, UK
Maulik Kotecha, Mechanical Engineering, TAMU
Sevan Goenezen, Professor, Mechanical Engineering Department, TAMU

In this poster, the feasibility to recover the material property distribution of a three-dimensional heterogeneous sample is presented. This is done using only measured surface displacements and inverse algorithms without making any assumptions about local homogeneities or the material property distribution. The findings of this research could benefit the field of breast cancer detection and other medical imaging related applications. This inverse approach also finds its application in the field of manufacturing and material science for non-destructive testing of materials and material characterization. To better represent the actual scenario, measured displacements used in simulated experiments are augmented with noise, significantly higher than anticipated measurement noise. Three dimensional problems of the cube with one and multiple stiff inclusions are tested with simulated experiments. The inverse method recovers the shear modulus values in the inclusions and background well and reveals the shape as well as location of the inclusions clearly. The finite element method is being used as part of solving the inverse problem, which requires high computational power and thus more computational time as well. With efficient incorporation of OpenMP and MPI parallelization algorithms at various stages of computation, the required computational time is significantly reduced.

A regional ocean forecast and hindcast system for the Texas-Louisiana shelf contributing to rapid oil spill responses and oceanographic research

Daijiro Kobashi (Texas A&M), Robert Hetland (Texas A&M)
Kristen Thyng (Texas A&M)
Martino Marta-Almeida (Universidade de Aveiro)
Steve Baum (Texas A&M)

A regional ocean forecast system has been developed for the Texas-Louisiana shelf. The primary objective of the system is to provide ocean current prediction for rapid oil spill responses. The forecast data are used to run an oil-spill model by the state government agency during actual spill events and thus, the system has been an integral part of the state rapid oil spill response efforts by providing critical data sets in timely manner. The forecast system has been implemented on two of the university's High Performance Computing (HPC) clusters: Ada and Terra, of which Ada has been used as the primary system and Terra as a backup system. The university HPC clusters have played a critical role in the system by providing necessary computational resources to implement robust forecast system and to provide ocean current data in timely manner. The forecast output data are validated against real-time ocean observing data and visualized on an interactive website ( The modeling system used for the forecasts has also provided high-resolution spatio-temporal data sets spanning for 24 years (1993-2016) for oceanographic research. In the past, numerous scientific papers have been published using the prototype modeling system implemented on the university HPC clusters.

Atomistic Simulations of the Viscoelastic Response of a model Single Crystal Equiatomic Solid Solution

Tung Yan Liu, Department of Materials Science and Engineering, Texas A&M University,
Michael Demkowicz, Department of Materials Science and Engineering, Texas A&M University

We use molecular dynamics simulations of cyclic deformation to investigate the viscoelastic response of two-component, defect-free, face-centered cubic equiatomic solid solutions (ESSs). All the simulations are conducted using the LAMMPS molecular dynamics code on the HPRC’s Ada cluster. Rather than simulate a specific alloy composition, we use a Lennard-Jones model to study the effect of loading frequency, temperature, model size, and atomic misfit on mechanical energy dissipation. Although free of defects, these models exhibit viscoelastic behavior. We attribute this behavior to the large distortion in the lattice structure induced by atomic misfit. Our findings may aid future research in mechanical behavior of concentrated alloys and in molecular dynamics simulations of viscoelastic behavior.


Ping Luo, High Performance Research Computing

Open OnDemand, developed at the Ohio Supercomputing Center, is an open source web portal system that makes it easy for users to use the HPC systems and resources. TAMU HPRC has collaborated with OSC to port Open OnDemand on the Ada cluster to produce TAMU HPRC OnDemand. In this poster, we demonstrate the main features of TAMU HPRC OnDemand and show how users are helped with various apps provided in the portal.

CFD-Based Calculation of Rotordynamic Forces in Rotating Equipment

Farzam Mortazavi, Research Assistant, Mechanical Engineering Department, TAMU,
Alan Palazzolo, Professor, Mechanical Engineering Department, TAMU,

Rotating equipment are subject to fluid induced forces which may turn into destructive forces under super-critical operation. The transient dynamic forces keep feeding energy into an eccentric shaft to promote instability. Therefore, the American Petroleum Institute requires advanced computational models to predict the stability characteristics of such equipment. A series of techniques are delivered in order to demonstrate how to use Computational Fluid Dynamics to accurately predict these rotordynamic forces. Several rotating equipment are analyzed including impellers, seals, volutes and diffusers. The problem typically involves solving unsteady Navier-Stokes equations along with appropriate turbulence models and transition models on a fine grid. Since the rotordynamic forces are formed in response to the imposed vibrations, the fluid domain benefits from mesh deformation algorithms to accommodate these coupled motions. As a result, such methodologies are classified as expensive computational problems. Several techniques, such as quasi-steady modeling, multi-frequency approach, periodic mesh motion, and phase modulation are discussed to reduce the computational cost of the problem.

Modelling the Impact of Surface Curvatures of Single-Walled Carbon Nanotubes on the Adsorption of Phage-Displayed Peptides.

Aby Abraham Thyparambil 1,2 and Anthony Guiseppi-Elie 1,2,3,*
1 Center for Bioelectronics, Biosensors and Biochips (C3B), Texas A&M University, College Station, TX 77843, USA
2 Department of Biomedical Engineering, College of Engineering, Texas A&M University, College Station, TX 77843, USA
3 ABTECH Scientific, Inc., Biotechnology Research Park, 800 East Leigh Street, Richmond, VA 23219, USA and

Phage display is widely used to screen specific peptide sequences with high affinity for single walled carbon nanotubes (SWCNTs) with different electronic types and chirality. However, SWCNT substrates for phage-screening are often heterogenous mixtures with varying surface curvatures. The local molecular environment of carbon nanotubes with increasing surface curvatures are different from that of a flatter and less strained surface because of increased bond deformation. The objective of the current study was to quantitatively assess the impact of surface curvature on the affinities of phage display screened peptides to SWCNTs using an molecular dynamics (MD) approach. Two distinct peptides (B3 peptide - HWSAWWIRSNQS, and B3s peptide - HWSAWSIRSNQS) that differed in a single amino acid position were individually tested on four different zig-zag SWCNTs with different surface curvatures (1.28 nm-1, 0.8 nm-1, 0.51 nm-1, 0.36 nm-1) and a flat graphene sheet (0 nm-1). Biased MD simulations were performed in GROMACS (v 5.1) using CHARMM36 forcefield in an explicit solvent model environment until the convergence in potential mean force was obtained. The steady state transition kinetics involved in the adsorption were derived using Markov state models. Results suggest that both the SWCNT curvature and peptide composition influence a peptide’s affinity to SWCNT. Both set of peptides showed a positive correlation between the surface curvature and the free energy of adsorption, implying that the phage-screened peptides bind with higher affinities to zig-zag SWCNTs with higher surface curvatures as opposed to lower surface curvatures. However, the B3 peptides generally had higher affinities to the carbon-based nanomaterial than the B3s peptides. These results suggest that B3 peptides could be used to preferentially disperse SWCNTs' with higher surface curvatures. Furthermore, refinements in phage-display experiments that account for the curvature-dependent effects are recommended when screening for peptides with selective affinity to a surface property of the SWCNT.


TAMU HPRC OnDemand: HPC the Easy Way!

Ping Luo, High Performance Research Computing

Open OnDemand, developed at the Ohio Supercomputing Center, is an open source web portal system that makes it easy for users to use the HPC systems and resources. TAMU HPRC has collaborated with OSC to port Open OnDemand on the Ada cluster to produce TAMU HPRC OnDemand. We will demonstrate the main features of TAMU HPRC OnDemand and show how things become easy and convenient for both new and experienced users.

Interactive VR Simulation of Data Center Security Protocols, Developed for the "CiSE-ProS: Cyberinfrastructure Security Education for Professionals and Students" Project.

Jinsil Hwaryoung Seo, TAMU Department of Visualization

In this prototype interactive virtual environment, the user learns about the security procedures involved in a data center and inspects hardware for signs of tampering. The user must follow the security protocol and replace some equipment.

rSalvador: An R package for studying microbial mutation rates

Qi Zheng, TAMU School of Public Health

The past few years have seen a surge of novel applications of the Luria-Delbruck fluctuation assay protocol in bacterial research. Appropriate analysis of fluctuation assay data often requires computational methods that are unavailable in the popular web tool FALCOR. This paper introduces an R packages named rSalvador to bring improvements to the field. The paper focuses on rSalvador's capabilities to alleviate three kinds of problems found in recent investigations: (i) resorting to partial plating without properly accounting for the effects of partial plating; (ii) conducting attendant fitness assays without incorporating mutants' relative fitness in subsequent data analysis; and (iii) comparing mutation rates using methods that are in general inapplicable to fluctuation assay data. rSalvador was introduced to the genetics research community via a recent paper (G3: Genes|Genomics|Genetics 7 (2017) 3849-3856), which was selected as one of the 2017 Spotlight papers by the editor.

HPRC Genomic Computational Analysis Templates: Jumpstart your HPC Genomics Analysis Project

Michael Dickens, High Performance Research Computing

Getting started analyzing genomics projects on HPC resources requires learning many new tasks such as creating and configuring job scripts that efficiently utilizes HPC resources and properly configuring bioinformatics software to effectively utilize the requested resources. GCATemplates provides example job scripts with test data that allows users to become familiar with bioinformatics software usage on HPC resources.