Current Research
Research at Texas A&M at Qatar contributes to Qatar’s thriving industry and economy. We seek more effective oil and gas production, faster development of alternative energy sources, and better energy efficiency in buildings. We are developing novel methods for hazardous waste water treatment, we propose innovative materials; and we are studying methods for better mobile phone and internet performance. This engineering research is reinforced by fundamental studies in physics, chemistry and mathematics. Overall, we have approximately $70 million in research grants awarded to the faculty by industry, the Qatar Foundation and Qatar government agencies.
A summary of the research, our collaboration with industry, and our partnerships with dozens of universities and research centers around the world, are presented in the web pages of the various programs; to give, however, a flavor of our efforts, we outline some of the recent non-confidential funded projects.
This project concerns the design, manufacture, and testing of a unique solar reactor which is contains a camera-like aperture and a moving-wall cavity. As is well known, “Solar Cracking” offers a CO2 emission free production of hydrogen. But solar cracking reactors have a major problem, namely: there are appreciable intrinsic losses in the overall system energy conversion efficiency as a result of re-radiation losses from the aperture and the transient nature of the input solar energy. Current work focuses on optimal reactor design for steady state efficiency at a fixed aperture, but largely ignores discussions on the transient inefficiencies. We address this latter problem by investigating a unique system design featuring: (1) a camera-like variable aperture, and (2) a moving wall cavity allowing variable cavity volume inside the solar cracking reactor. With this configuration the solar cracking reactor will be in quasi-equilibrium with a semi-constant interior temperature and thus yielding only nominal fluctuations in the efficiency of the natural gas to hydrogen conversion.
Emission free co-production of carbon nanotubes and hydrogen.
This segment of our work investigates simulation models and a validation apparatus for a solar reactor for direct decomposition of natural gas into H2 and carbon. As is known, this technique offers a CO2 emission free hydrogen production, while the carbon is collected in a high-grade and/or nanotube form. It is also realized, however, that hydrogen and carbon producing solar cracking reactors have a major drawback: reactor clogging due to carbon deposition during the course of the two-phase solar thermochemical processing. The solution of this problem requires a thorough examination of carbon particle deposition as a function of flow dynamics and reactor design. Here, we (1) develop two-phase, 3-dimenetional, unsteady computational fluid dynamics (CFD) models which incorporate kinetics, heat transfer, and incoming solar flux for a 1kW solar reactor; (2) design and construct an experimental setup to verify that the flow field of the reactor predicted by the CFD simulations is correct; (3) manufacture and operate a 1kW reactor; (4) improve the design concept of the 1kW solar reactor according to the test results.
The Qatar Sustainable Water and Energy Utilization Initiative (QWE) was established as the center of scientific and technical excellence for high-impact research and development, human capacity building, scientific and technical advising, and public awareness campaigns on sustainable water and energy utilization. The QWE delivers technical solutions to problems of national and regional importance and supports the pillars of Qatar Vision 2030. Partnerships with world-class groups in Qatar, North America, Europe, and Asia ensure that the scientific and technical expertise deployed at QWE is of the highest possible standard. The QWE provides an impartial platform to promote the concerns of stakeholders involved in water and energy, and we cooperate with the stakeholders to help identify and satisfy their immediate and long-term technological goals.
Deep formations present a serious challenge to oil and/or gas well integrity management because of the high-pressure and high-temperature (HPHT) environment. As a result many reservoirs in Qatar are not being worked. A critical issue of a HPHT well is the failure of the casing-cement system that keeps the well intact. This work addresses this problem. Most of the tubulars used for such well completions are tested prior to field application based as possible wellbore conditions as loading parameters. Cement slurries and cement properties are also measured. None, however, of the known methods are evaluating the life of the well based on actual parameters in the wellbore. We apply the Finite Element Analysis (FEA) technique, combined with experimental stress analysis, to help us to develop a realistic fatigue life for a cement sheath under various known harsh operating conditions. By understanding the mechanics of the casing-cement system, a new model will be developed to evaluate and predict the life of the well.
Precision spectroscopy for trace detection and analysis of hydrocarbon well gases.
The ultrasensitive detection of well gases is of major significance. Detecting, for example, the 13C/12C ratio of methane is an important indicator in oil exploration since it characterizes the origin and content of different geological systems. This work develops novel Two-Comb Fourier Transform absorption spectroscopy, with two femtosecond frequency combs operating in the middle infrared wavelength region, as a means to detect and characterize hydrocarbon well gas. The instrument is tailored to obtain spectra around 3 μm over a range of more than 100 nm. Broadband high resolution spectra can be obtained within a few tens of microsecond making time evolution studies possible. This methane sensor has the potential to be turnkey, is portable and will be able to work on-line. The used fiber lasers have several watts of power making frequency conversion schemes very efficient. We aim to demonstrate a sensitivity to 0.01ppb for the various hydrocarbon well gases in a region where an efficient sensor is currently not available. Stabilizing the frequency combs to an optical reference will improve the resolution, thereby making de-convolution of overlapping spectra easier. An algorithm for the quantitative analysis of infrared spectra of multicomponent gas mixtures will be used to interpret the data.
Enhancing Gas-Condensate Well Productivity by Wettability Alteration.
A condensate bank can form in a gas-condensate well if the pressure falls below the dew point. The well’s productivity is thus impaired. Along with other authors, we have argued that wettability alteration (from strong liquid-wetting to intermediate gas-wetting) is an attractive approach to solve this problem. The following issues, however, need to be addressed before this method can be widely applied in practice:
- Understand the stability of the state of gas-wetting in the rock.
- Understand better the wettability alteration in synthetic rocks from strong oil-wetting to intermediate-wetting.
- Investigate the optimum wettability conditions for enhancing productivity.
- Study the effect of various degrees of wettability and fractional wettability.
We address these issues by modeling the interfacial forces and carrying out experimental work under reservoir conditions.
Displacement Efficiency and Reducing Water Production in Carbonate Reservoirs.
This research seeks to develop and validate processes for improving recovery efficiency during oil/gas production. Specific focus is on the water flooding of carbonate reservoirs in the Middle-East that are dominated by natural fractures and have highly heterogeneous anisotropic porosity and permeability distributions. One of the ways to improve oil recovery from such carbonate reservoirs is to use optimized salinity and/or low concentration of surfactants for wettability alteration during water flooding. It is important to understand this process using experimental, theoretical and numerical modeling research. Since such reservoirs are very heterogeneous with complex porosity and permeability distribution at many scales, modeling complex fluid flows in such formations accurately and efficiently is also a challenge. All these theoretical, experimental and mechanistic modeling issues will be addressed during the course of this project.
Single Well Acid Fracturing Simulator: Modeling and Field Studies.
Using hydrochloric acid as a fracturing fluid has long been considered to be a practical technique for stimulating carbonate formations. Yet, because of the complexity of modeling hydraulic fracture growth due to the reaction between acid and formation rock, research in this area is very limited. Specifically, to the best of our knowledge, thermal effects have not been considered. In, however, acidizing/acid fracturing, acid at ambient temperature is injected into a deep formation where the temperature is high, the acid reaction rate depends on temperature; and exothermic reactions can produce heat. Furthermore, the current models cannot capture the real complex geometry of the fractures. In this project, we develop a new acid fracturing model based on the Lattice-Boltzmann method taking into account the influence of temperature and geometry. We will validate the model with relevant experiments. We will then extend the model using parallel computers to make it suitable for application to field-scale acid fracturing. The simulator to model and optimize acid fracturing will be applied in the North Field, Qatar.
Asphaltene deposition during CO2 injection in Qatar’s oil reservoirs.
Carbon dioxide injection is one of the most viable IOR options for Qatar’s oil fields but the procedure raises the possibility of asphaltene precipitation. Among other problems, asphaltene precipitation causes severe permeability reduction and wettability alteration in the reservoir, and can plug the wellbore and surface facilities. Hence a reliable model is needed to be able to predict the phenomenon and quantify its consequences. Yet, to the best of our knowledge, a comprehensive model does not exist. In this project, we propose to develop a field-scale multi-phase multi-component compositional model based on advanced numerical techniques and a novel equation of state to reliably predict asphaltene precipitation (and its consequences) during CO2 injection. We will perform static and dynamic experiments with Qatari core and oil samples to verify our results.
High strength steel for down-hole applications in sour environments.
This research program investigates the design and fabrication of high carbon and nitrogen austenitic stainless steel for down-hole applications required for high production and transport capacities in aggressive and corrosion environments. In particular, the forging quality steel will have high wear resistance and superior corrosion resistance in CO2 and H2S environments. The developed steel shall have good strength and toughness. We will work on two initiatives. The first will be to install the autoclave testing facility to investigate high strength tubular in the aggressive environment of acid gas, sour, CO2, brine, and their combinations at various temperatures, pressures and recirculation modes that simulate the processing environment. The second initiative will be to evaluate various tubular materials in specific corrosive environments. This project will involve graduate students using their PhD thesis as the vehicle to achieve advancement in corrosion studies applicable to oil and gas industry. A systematic analytical work with various types of materials and environment will allow the development of predictive models relating the material composition, microstructure and properties to its performance in a given environment. An additional goal of this work will be to develop new graduate-level projects that will grow into a wide-ranged Texas A&M at Qatar Corrosion Research Program.
The control of distillation column composition usually depends on inferential models that can accurately predict the composition from easily measured process variables, such as temperature and pressure. The success, however, of a model depends on the data fed into it and such data can be erroneous. Data errors, therefore, need to be filtered. In this project, multiscale representation is utilized to improve the prediction accuracy of some of the latent variable regression (LVR) models (which are used in inferential modeling of distillation column compositions), such as Principal Component Regression (PCR) and Partial Least Squares (PLS), by developing a multiscale latent variable regression modeling algorithm. The developed algorithm combines the advantages of the LVR models with those of multiscale representation. Also, most LVR modeling methods assume a linear input-output relationship. Therefore, another objective of this project is to extend the results to nonlinear processes using fuzzy logic, which has been successfully used in nonlinear system identification and control. The advantages of the developed algorithms will be demonstrated and compared to those of the conventional methods using simulated and industrial distillation column data.
Model-free, Data-based Design of Adaptive Control Systems.
The analysis and design of sophisticated engineering systems is largely model based but it is tacitly assumed that increasingly complex models are needed to solve complex problems. This proposal takes a fresh approach. Our work on modeling, engineering design, and control of complex systems is based on two recent fundamental breakthroughs: a) recent results on proportional–integral–derivative controller (PID) controller design and synthesis for plants without analytical models; and b) a measurement-based approach to simplified modeling based on an exploitation of low rank structures. The concept is that one can replace a complex structure by an extremely simple model in terms of the design variables of interest. The number of these variables is generally very small and the functional behavior of the system with respect to these variables can easily be determined from a small number of specific measurements on the system. We propose to apply our results to real systems, specifically to process control and cancer genomics.
Future wireless communication systems including cognitive radio (CR) will be very dependent on a concept called signal intelligence. Signal intelligence is a theoretical framework with which future wireless communication systems achieve adaptation, awareness, and learning. Signal intelligence consists of identifying transmitted waveforms and specific features/parameters of the transmitted signals, extracting propagation channel characteristics from the effective received signal, identifying interference and other impairments coupled to the received signal. This proposal aims at discovering how signal intelligence can be achieved by taking the four paradigms defined above into consideration. Specifically, the research objective of this proposal is to construct a theoretical background and build a framework to achieve signal intelligence in wireless communications systems. The extensive growth of demand for wireless communication services led to new paradigms for optimum satisfaction of user requirements:(i) effective spectrum allocation (ii) adaptive and complex modulation, error recovery, detection, channel estimation, diversity, and code design techniques to allow high data rates while maintaining desired Quality of Service (QoS) (iii) reconfigurable and flexible air interface technologies for better interference and fading management.
Sparsity-Aware Spectrum Cartography for Cognitive Networks.
Wireless cognitive radio (CR) technology holds great promise to address the perceived dilemma of bandwidth under-utilization versus spectrum scarcity, which has rendered fixed-access communication networks inefficient. This proposal aspires to develop an infrastructure for comprehensive situation awareness at the Physical Layer (PHY), and permeate its benefits to the medium access control (MAC) and CR network layers. The key to achieving these goals is the novel notion of RF cartography, which amounts to constructing two families of maps: (m1) global power spectral density (PSD) maps capturing the distribution of power across space, time, and frequency; and (m2) local channel gain (CG) maps providing the propagation medium per frequency from each node to any point in space and time. The vision is to have CR nodes jointly utilize these maps so as to enable: identification of opportunistically available spectrum bands for re-use, and handoff operation; localization, transmit-power estimation, and tracking of primary user activities; and interference control, resource allocation, and routing. Successful completion of this research is of immediate interest to software radio designs with IEEE 802.11 compliant standards. The advances in the foundations of sparsity-aware regressions will benefit a gamut of research areas.Reconfigurable Active-Storage Platform for Data-Intensive Applications.
Solid-state drives (SSDs) are beginning to replace hard disk drives as the mass storage devices of choice in portable, personal, and enterprise computing systems. SSDs have significantly faster access times, which make them particularly useful in applications that require high data processing rates. SSDs are also easy to interface with data processing devices, such as field-programmable gate arrays (FPGAs), to provide flexible, active-storage devices capable of offloading or parallelizing some of the data processing functions from centralized servers. In turn, FPGAs can be easily customized to the needs of specific data-processing queries. In this project we propose to build a distributed, reconfigurable, active-storage platform for various data-intensive applications. Data mining is one example of these applications that typically needs to extract useful information from very large data sets by searching for specific patterns and relationships in the data. Our platform would enable customized, access-time processing of massive data dispersed among distributed storage devices. In addition to exploring suitable reconfigurable active solid state drive (RASSD) architectures, our goal is to develop a layered platform that uses middleware to reconfigure distributed active storage devices for specific queries and provide an abstract, seamless interface to the application layer. We will also compare the performance, cost, and scalability of our platform to existing solutions.
In micro fluidic systems, particle motion, in addition to being simply convected along with the suspending fluid, is subject to random Brownian thermal noise. As the particles move randomly, they can affect a volume of surrounding liquid in a scale larger than the actual particle size. In a micro/nano system the average magnitude of this motion may even exceed fluid channel dimensions; conventional hydrodynamic theory is not able to describe such a system. Here, however, we will apply the Finite Element Technique to elucidate the motion of nano particles in a micro fluidic suspension, using Langevin equation for Brownian particle and Navier-Stokes equations for fluid flow. The finite element models of the coupled equations will be based on the least-squares formulations that guaranty the global differentiability and minimization of error functional due to the approximation of the field variables. We will then investigate the possible measurable effect of nano-scale behavior on the macroscopic properties of the flow, such as viscosity, thermal conductivity, and mixing.
Asphalt pavement is a complex composite that comprises mineral aggregates and a binder which is a by-product from the distillation of crude oil. Its mechanical properties vary significantly depending on the mineral content, the proportions, size, and distribution of aggregates, and the thermophysical and mechanical properties of the constituent materials. The current methods used in the design of asphalt pavements in Qatar are empirical and employ simplistic assumptions in describing material properties and loading conditions that are not appropriate for the current and the estimated high traffic loads in the future. Sophisticated design and characterization tools are needed if long-lasting roads capable of supporting the future transportation infrastructure are to be adequately constructed. Accordingly, this project involves the development of multi-scale computational models for simulating asphalt pavement performance under realistic loading conditions. The asphalt pavement behavior is represented by advanced constitutive models that span over micro, meso, and macro length scales. The computational models will include the application of realistic loading configurations based on experimental measurements of tire-pavement contact stresses and interactions. The model’s parameters will be determined through extensive laboratory testing of asphaltic materials used in Qatar. The developed model will be validated with field data. A feature of our work is the use of nondestructive imaging techniques to describe the microscopic features of asphalt concrete under various loads and external conditions.
Twin-Roll Cast Magnesium Alloys in Automotive Applications.
Magnesium, the lightest of all structural metals, has a density that is less than one quarter of the density of steel, high specific stiffness and strengths, and excellent castability and machineability. It is thus a candidate material of interest to the automotive industry. In fact, the recent advances in twin-roll casting technology of magnesium have demonstrated the feasibility of producing magnesium sheets in the range of widths needed for automotive applications. However, practical challenges regarding manufacturing and corrosion need to be resolved. Research has shown that magnesium has limited formability at room temperature due to its hexagonal close-packed microstructure. However, preliminary studies have shown that the formability of magnesium can be significantly improved by processing the material at elevated temperatures, using processes such as superplastic forming and warm forming. Therefore, we are working at developing an understanding of the superplastic behavior of magnesium through material testing and characterization, and the development of modeling and simulation tools for the manufacture of magnesium-sheet products.
This project is centered on identifying and studying the reactivity of key resting states and intermediates in several reactions using ruthenium centered catalysts. A variety of time-resolved infrared techniques will be applied to obtain important information about the mechanisms of the proposed reactions. The project includes the synthesis of novel catalytic precursors whose efficiency will be measured under catalytic conditions. The identity of the relatively stable catalytic resting states and their kinetic profiles is probed by in-situ high pressure solution phase ATR spectroscopy. In addition, the nature and dynamics of highly reactive intermediates in the catalytic cycles will be probed using nanosecond laser flash photolysis techniques employing infrared detection. Direct observation of such transient species and understanding their reaction dynamics can provide important information about the overall catalytic process. The combination of these IR techniques will allow for the monitoring of the proposed reactions from the hours to nanosecond time scale and is expected to provide a complete mechanistic profile of the catalytic systems. Catalytic processes to be investigated include: CH activation and functionalization, CO2 utilization, and hydroformylation of olefins.
Density functional approximations to predict the activity and selectivity of heterogeneous catalysts.
Heterogeneous catalysis are surfaces (metal oxides, zeolites, etc.) that increase the rate of desired chemical reactions while blocking undesirable side reactions. Petrochemical cracking, natural gas reforming and many fine chemicals processes depend on heterogeneous catalysts. Computational chemistry plays a major role in discovering catalysts with improved activity and selectivity. Calculations on heterogeneous catalysts treat the substrate surface as well as the reacting atoms. To date, essentially all such calculations use approximate density functional theory (DFT). But while DFT works well for many properties, it is often poor for predicting reaction rates. For example, the theory can overestimate gas-phase hydrogen transfer reaction rates by some 5 orders of magnitude! While this error is probably smaller at surfaces, there are no systematic studies of the effect. More importantly, little is known about DFT’s accuracy for the relative reaction rates critical to catalyst selectivity. We perform the first systematic study of DFT for reactions at surfaces, and compare the results with accurate calculations on simple model systems. We also apply our expertise in DFT methods development to design new functionals that can more accurately model surface reaction rates.
Light bullets, fractional vortices, and nonlocal solitons. This project develops novel concepts of nonlinear (NL) photonics for all-optical information technologies. It deals with the:
- propagation and interaction of laser beams in the form of light bullets, vortices with fractional topological charges, spatial solitons, and localized wave packets in photonic crystals, optical lattices, dispersion-managed systems, nematic liquid crystals, and other nonlocal media;
- analytical and numerical treatment of partial differential equations in (3+1)D, describing propagation of above-mentioned beams in above-mentioned media, such as the generalized NL Schrodinger, Gross-Pitaevskii, and Ginzburg-Landau equations with distributed coefficients, and the vectorial coherent and incoherent Manakov systems. Studies of the stability analysis of solutions concerning modulational and structural instabilities as well as the wave collapse;
- theoretical and experimental confinement and manipulation of co-propagating (CO) and counter-propagating (CP) surface waves and modes at the edges, corners, surfaces, defects, and interfaces in 1D, 2D, and 3D composite materials (metamaterials, plasmonic, photorefractive, and photonic crystals);
- transfer of orbital angular momentum and changes in beam vorticity of CO and CP beams with integer and fractional TC, including arrays of optically induced lattice beams.
