Khalifa University
  • Abu Dhabi, United Arab Emirates
Recent publications
The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for robotic machining applications to enable faster and more reliable operation, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece’s pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling end-effector and drills the target holes on the workpiece using a combined position-based and image-based visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with maximum positional errors of less than 0.2 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution.
The conversion of biomass into liquid fuels has gained worldwide attention because of its potential to reduce pollution. Drop-in biofuels are non-oxygenated liquid hydrocarbons that are produced from renewable biomass sources. These biofuels (that consist of biogasoline, bio-jet fuel, and green diesel fractions) hold a lot of promise owing to their properties that are similar to those of fossil fuels. In this work, the use of tantalum phosphate (TaPa) as a hydroprocessing catalyst for the drop-in biofuel production from date palm seed oil under mild experimental conditions was investigated. Mesoporous TaPa was intrinsically synthesized and comprehensively characterized using several analytical techniques. Further, the catalyst performance was investigated for its various dosages (0–15 wt%) under different reaction times (2–4 h) and temperatures (300–450 °C), using 10 bar H2. Infrared spectroscopy and Gas chromatography techniques were used to characterize and quantify the product oil composition, respectively. Optimization of the proposed one-step reaction approach resulted in high yields of deoxygenated hydrocarbons that consisted of 53.6% of bio-jet fuel (C9-C15) and 35.9% of green diesel (C14-C20) fractions. The plausible pathways for the formation reactions of various linear and branched aliphatics and aromatics in the product oil are discussed. Besides, the reusability of the TaPa for continuous hydroprocessing of the bio-oil was demonstrated. Thus, the production of bio-jet fuel and green diesel using the date palm seed biomass-derived oil in the presence of TaPa catalyst is sustainable, efficient, safe, and has great potential for aviation applications.
The purpose of this paper is to investigate cognitive biases among financial planners and, if and how, digital transformation through Artificial Intelligence (AI) can help overcome biases. The literature establishes that investors and financial services clients can exhibit cognitive biases. However, it is not evident whether the financial planners understand and detect cognitive biases among the clients and if they at all 'attempt' to address the biases whilst providing financial planning services. Utilizing the attribution theory, our paper contributes by exploring the gap in research related on cognitive biases among financial planners and provides a future research agenda for addressing the gap, through a qualitative investigation. Our study was designed over two stages, wherein we conducted in-depth interviews in both stages. The first stage included in depth interviews with 21 financial planners and a repeat 10 interviewers with select financial planners, with scenarios in the second stage. In total, we conducted 31 interviews to investigate cognitive biases among financial planners and how Artificial Intelligence can assist. Our findings suggest that cognitive biases exist among financial planners while providing services for the people in need, which is a major challenge for them. Our findings further suggest that digital transformation by using the Artificial Intelligence technologies might help overcome this existing biases, albeit, AI technologies ought to be combined with human intelligence. To the best of our knowledge, there exists no existing research on the association between cognitive biases and artificial intelligence among financial planners.
A new nanocomposite (NC) comprising of ZnO nanoparticles and polyacrylamide (PAM) polymer hybrid agent is investigated for enhanced oil recovery in sandstone and carbonate rocks. A range of measurements are conducted to examine the behavior of NCs including NC synthesis and characterization, NC physical properties, stability analysis, IFT, and wettability as a function of NC concentration (from 100 to 1000 ppm). Furthermore, core flood tests are performed to evaluate oil recovery and relative permeability characteristics and a numerical model is established, and history matched. The zeta potential tests verified the stability of the set NC at 200 ppm concentration in water-based solutions, while a clear reduction in IFT from 29 mN/m to 5.5 mN/m was observed at the same NC concentration. A notable shift is observed in contact angle from 133° to 13° for the sandstone rock at a 1000 ppm NC concentration suggesting a significant shift in wettability from oil-wet to strongly water-wet after NC injection while the optimal contact angle for the sandstone occurred at NC concentration of 200 ppm. Furthermore, the flooding NCs resulted in a 16.05 % increase in oil recovery factor compared to seawater flood for sandstone while a 26 % increase was recorded for carbonate sample. Likewise, the mobility ratio after NC flooding compared to seawater flooding decreased from 8.56 to 1.46. We obtained a good history match of the water cut and oil recovery factor data by creating the core dimension model. In addition, according to the fluid distribution pattern (one of the simulator outputs), the fingering phenomenon was delayed after flooding the NC compared to seawater. This study provides new insights into the impact of nanocomposite on oil recovery enhancement in sandstone and carbonate formations.
Social robotics is undergoing significant transformations and a new class of tools is emerging. Though most technologies related to the physical aspects are becoming well understood, human-robot interaction is still far from human level skills. However, there is a growing pressure from society to absorb these sophisticated technologies. As robots move from factories to home, the study and optimization of Human-Robot Interaction (HRI) becomes an increasingly important factor. The main issue to success is ensuring that users are interested and satisfied by the devices they own. The issue of technological acceptance has been thoroughly studied in the context of human-computer interaction. Robots, on the other hand, can use any natural communication channel employed by their users, resulting in much higher potential for user-adapted behavior. Thus, it becomes interesting to study the phenomenon of user-adaptivity in the context of HRI. User-adaptive systems are based on information from the users, usually (but not necessarily) contained in user models. This model encodes the attributes of the user that are relevant to the operation of the system, such as the user’s expertise level and preferences. This information is used by the system to generate behavior that conforms to the idiosyncrasies of the user, resulting in higher levels of user satisfaction and acceptance. User-adaptive systems have been shown to be easier to accept by end-users than non-adaptive ones. This article gives an overview on the usage of user-adaptive techniques on robotic systems, based on an analysis of a number of recent scientific and technological works.
Low-speed wind tunnel experiments are conducted to study the aerodynamic performance of a half-span delta wing with 45° leading-edge sweep at subsonic flow regime. The experiments are carried out at a Reynolds number of 8.37 × 10⁵, a free-stream Mach number of 0.1 and angles of attack up to 25°, in steps of 5°. The test model was designed with thirty-two pressure taps fixed on its surfaces (sixteen on each side). Multi-tube manometers were connected to these taps using long tubes to enable recording the pressure readings. Surface pressure distributions and aerodynamic characteristics were calculated at different span-wise locations along the non-dimensional chord-wise distance. Results exhibited that most lift on the studied wing is generated in the region close to the leading edge for all the studied incidence angles. Additional lift is created in the region close to the root chord rather than the tip chord, whereas drag forces increases from tip to root. This can be attributed to the formation of trailing edge vortexes due to the flow separation at the wing leading edge that produces more drag, hence suppressing lift. The study showed also that angle of attack increases the drag coefficient from tip to root, especially at high angle of attack, indicating unfavourable behaviour for manoeuvring. Moreover, the angle of attack increased the pitching moment coefficient up to 10° before it drops sharply until it reaches the tip of the wing model.
Innovating next-generation two-dimensional (2D) membranes necessitates overcoming their selectivity/permeability trade-off limitations. Unlike most 2D multi-stacked membranes, where their water permeability always occurred through the single d-spacing channel, we engineered a novel aquaporin-like multi-functionalized holey graphene (HG) membrane with the potential of tripartite nanochannels. Through this strategy, high selectivity, superior permeability, and chemical stability were achieved. The utilized facile in-situ crosslinking methodology enabled the creation of a unique membrane featuring an aquaporin-like wide/tight interlayer d-spacing decorated with multifunctional SiO2 nanoparticles, in addition to direct nanochannels of HG. This unique structure boosted the solvent permeability by more than ten times without sacrificing the selectivity. This distinctive membrane category displayed excellent selectivity with a performance higher than 96% for dye rejection. This novel membrane has great potential to be utilized in various organic solvent nanofiltration and wastewater purification applications with extraordinary ability to surpass the permeance/separation trade-off.
Rock wettability is influenced by several conditions, and one such factor is the potential at the rock/fluid interface. While most studies have reported zeta potential in this context, very few investigations have carried out streaming potential measurements. Further to this, most previous studies have only evaluated zeta potential/streaming potential for NaCl brine-clean sandstone systems, while it is clear that organic matter exists in subsurface rock. However, the effect of these forms of organic matter on streaming potential has not yet been probed. Accordingly, in this study streaming potential measurements were firstly conducted on pristine San Saba sandstone samples with 0.3 mol.dm3 NaCl brine-saturated at (6.895 MPa overburden and 3.447 MPa back-pressure). Secondly, the streaming potential of the aged samples was measured under identical conditions (the cores were saturated with aqueous HA solutions of different concentration; 1–100 mg/L) in 0.3⁻³ NaCl. Thirdly, a comparative analysis of zeta potentials was conducted via electrophoretic and streaming potential. Lastly, the T2 spectrum for the initial water saturation of pristine and aged cores, along with the T2 spectrum of residual water saturation after CO2 flooding for pristine and aged cores, were measured with NMR core flooding measurements. Accordingly, this work analyses the effects of organic acids on wettability alteration in sandstone formation in accordance with these procedures. A strong correlation exists between surface adsorption of organic acid and streaming potential coefficient, where the amount of residual water saturation decreases in humic-acid aged cores – suggesting the presence of organic acid that changes wettability towards CO2 wet in pores, where the CO2 displaces more brine in aged cores compared to pristine cores.
In this paper we revisit the problem of modelling analytically the kinematic interaction between a single pile and its surrounding soil under the action of seismic shear waves, by means of a Tajimi-type continuum elastodynamic model in three dimensions. The model provides the steady-state kinematic response of a cylindrical end-bearing pile embedded in a homogeneous viscoelastic soil layer, subjected to vertically propagating harmonic S waves. Results of the model are first validated against the results of numerical simulations, and the results of an existing, approximate solution. Accordingly, we employ the model in a parametric study, where we investigate the sensitivity of the seismic response of piles to certain key problem parameters, including pile slenderness, soil-pile relative stiffness, excitation frequency and fixity conditions at the pile head. The solution yields closed-form expressions for pile deformations and for the soil resistance developing on the pile, that do not require introducing fitting coefficients.
The impact of heat transfer and cylinder rotation on the induced forces due to vortex shedding is numerically studied in this work. The flow is maintained at Reynolds number of 100. Temperature difference of 300K-900K is used between the cylinder wall and the incoming flow. Transient analysis is conducted to solve URANS using Ansys/Fluent. Rotational oscillations for the cylinder in clockwise and anticlockwise direction are induced through user-defined-function at the maximum angular displacement of π/4 and π/2 radian. The frequency ratio of 0.5, 1, 1.5, and 2 is used. Time history of lift and drag coefficients, rms of lift coefficient, average drag coefficient , frequency spectrum, normalized Nusselt number and Strouhal number, along with vorticity and temperature contours, and pressure plots are presented in this work. The Karman vortex street with 2S vortex shedding pattern is observed for most of the cases, with C(2S) pattern appearing at few cases. It is found that the heat transfer causes damping in the amplitude and frequency of the drag and lift coefficients. Whereas the oscillatory rotation of cylinder results in an increase in the amplitude of the drag and lift coefficients especially at the lock-on condition, and also induces their amplitude modulation.
Tumor vessel co-option, a process in which cancer cells “hijack” pre-existing blood vessels to grow and invade healthy tissue, is poorly understood but is a proposed resistance mechanism against anti-angiogenic therapy (AAT). Here, we describe protocols for establishing murine renal (RENCA) and breast (4T1) cancer lung vessel co-option metastases models. Moreover, we outline a reproducible protocol for single-cell isolation from murine lung metastases using magnetic-activated cell sorting as well as immunohistochemical stainings to distinguish vessel co-option from angiogenesis. For complete details on the use and execution of this protocol, please refer to Teuwen et al. (2021).
The rapid expansion of installed wind energy capacity and the continuous development of wind turbine technology has drawn attention to operation and maintenance issues. In order to keep wind power a competitive energy source, the development of high-reliability and low-maintenance wind turbine systems is imminent, the rise of fault diagnosis provides a guarantee for their satisfactory operation and maintenance. A large number of statistical studies have pointed out that converter fault is the main cause of wind turbine system failure shutdown. Up to now, wind power converters’ fault diagnosis has obtained fruitful results, and those are constantly reported in power system literature. This paper presents a state-of-the-art review on wind power converters’ fault diagnosis for both short-circuit faults and open-circuit faults of power switch, including model-based, signal-based and data-driven methods. It provides a wide range, involving component fault modes, the robustness and reliability issues, algorithm investigation of fault diagnosis, quantitative analysis and qualitative analysis metrics for assessing the advantages of the developed techniques, and challenges in fault diagnosis design. Main purposes of this paper are: (1) Investigating the current research status of fault diagnosis on wind power converters to update the relevant research literature; (2) Discussing the robustness and reliability issues that must be considered in real engineering and safety critical systems; (3) Providing effective performance indices involves both quantitative and qualitative analysis, so that readers can understand the novelty of the proposed method.
Characterization of fluid–rock interactions is essential for a broad range of subsurface applications such as understanding fluid flow in porous medium and enhanced oil recovery predictions. Enhanced oil recovery (EOR) is crucial in oil and gas production operations, it entails injecting fluids into the reservoir to enhance productivity. When fluids are injected, interactions occur between the injected fluids and the reservoir rock/fluids; and the outcomes of fluid–rock interactions critically impact the fluid flow in porous medium and the associated oil recovery. Furthermore, the associated changes in reservoir properties (porosity, permeability etc.) and flow behavior (i.e. wettability alteration and relative permeability changes) demonstrate variability at a range of scales. Thus, it is of great importance to understand these interactions at multiple scales and their ensuing implications on EOR. This study therefore provides a comprehensive review of the types of fluid–rock interactions in both carbonate and sandstone reservoirs. Fluid–rock interactions quantification methods, their applicability and principle of measurements were summarized. The implications of fluid–rock interactions were extensively discussed. Finally, we identified and highlighted some research gaps and provided recommendations for future research directions. The findings of this review show that despite numerous studies on fluid–rock interactions such as adsorption, dissolution/precipitation, clay swelling/fines migration and wetting characteristics in porous media involving EOR fluids, the exact mechanism of action of these fluids during EOR applications in rock/oil/brine system, is still not fully understood. The extent and implications of these fluid–rock interactions on EOR depends on several factors/parameters. Such factors include the injected fluid type and chemical composition, rock type and mineralogical composition, brine pH, salinity and composition. Moreover, the review shows that all the fluid–rock interactions quantification techniques have some limitations either in their applicability, measurement range, or uncertainty level. Therefore, the incorporation of various imaging and characterization tools would be required for improved understanding the fluid–rock interactions. The review, therefore, provides critical insights in the area of fluid–rock interactions and its implications on EOR. Thus, the findings of this review are expected to enhance our knowledge and provide better understanding of fluid–rock interactions and thereby reduce the uncertainties associated with laboratory-scale predictions, reservoir management and enhanced recovery of oil.
Renewable energy-enabled desalination systems are crucial for solving the global water scarcity challenges in an environmentally friendly manner. In this work, we report an air gap membrane distillation (AGMD) process that utilizes polyvinylidene fluoride (PVDF) membranes blended with a photothermally active and relatively inexpensive activated carbon (AC). The composite membrane absorbed the solar radiation and generated local heat at the membrane surface, providing the driving force required in the AGMD process. This strategy overcame one of the key limitations of traditional MD—temperature polarization, and increased the energy efficiency significantly. We show that the blending of 5 to 9 %AC into the PVDF matrix can significantly boost the solar-energy-driven flux of AGMD by 281–1400 %, compared to the pristine membrane. PVDF membrane blended with 9 %AC exhibited an average AGMD permeate flux of 0.31 kg.m⁻².h⁻¹, with a GOR of 0.29 and a photothermal efficiency of 17.64 %. All PVDF-AC membranes showed excellent salt rejection, reaching 99.9 % for PVDF-9%AC. Finally, the AGMD performance of the fabricated membranes was compared by estimating their normalized MD coefficients (B/δ). PVDF-9%AC exhibited a B/δ value of 18.8E-5 kg s⁻¹ m⁻³ Pa⁻¹, as opposed to a meager 0.89E-5 kg s⁻¹ m⁻³ Pa⁻¹ exhibited by its pristine counterpart.
The location of shaded or faulty Photovoltaic modules in the PV array has a negative impact on the harvested power from the entire array. To overcome this significant limitation, PV reconfiguration is a considerable technique developed via interchanging the PV modules’ location physically or electrically. By this inspiration, in this article, the authors propose a novel enhanced heterogeneous hunger games search optimizer (EHHGS) based PV reconfiguration. The innovated EHHGS introduces a modified variant for the basic hunger game search optimizer (HGS) to achieve a high diversity and robust exploitation of the optimal solutions. The EHHGS is applied to identify the optimal relocation for the shaded or faulty modules in two configurations of PV connected array: total-cross-tied array (TCT) and Series–parallel one (S–P). The proposed approach has applied symmetric and asymmetric connected PV arrays with dimensions of 9 × 9 and 10 × 8 throughout five different shade patterns. Moreover, for providing a flexible tool for the user/researcher to detect and observe the benefits achieved via the PV reconfiguration strategy, a simple graphical user interface (GUI) for the PV reconfiguration strategy of TCT or S–P PV connected array using meta-heuristic algorithms is designed. This implemented GUI can extend for any size of PV arrays, different optimization algorithms, and different connection schemes. The proposed EHHGS, HGS, and set of recent optimizers, including harris hawk optimizer (HHO), marine predators algorithm (MPA), and artificial ecosystem-based optimization (AEO), handle a new simplified objective function to boost the optimizer’s ability in catching the optimal modules’ location to alleviate the mismatched power in the studied arrays. Several statistical metrics are computed for providing an unbiased comparison. Through the comparisons, the proposed EHHGS exhibits superior performance. It achieves the best re-design for the considered arrays that helps in avoiding the mismatch losses in the cases of the partial shaded/faulty modules and enhances the power generated profiles. EHHGS enhances the power by percentages of 44.42%, 11.9%, 33.36%, 20. 86% and 13.17% compared to the TCT-connected system. In the case of the S–P connection, the proposed EHHGS generates 47.2% and 10.45%, 30.75%, 17.25%, and 26.27% higher power.
Enhanced oil recovery (EOR) is used to retrieve capillary trapped and bypassed oil in the reservoir. Accordingly, laboratory and field applications of chemical EOR (CEOR) methods have been evaluated with varying degrees of efficiency. Nonetheless, the chemicals tend to precipitate in harsh reservoir conditions, thereby inhibiting the efficiency of the EOR process. Low salinity waterflooding (LSWF) is another EOR technique that has been gaining prodigious attention for recovering additional oil from the reservoir due to its sterling properties. However, LSWF has a low oil recovery efficiency especially in heavy oil reservoirs. Recently, the synergic combination of LSWF with chemical EOR has been exploited, explored, and evaluated. Herein, the type, mechanism, and efficiency of the newly devised hybrid EOR method have been reviewed. Moreover, its application is evaluated for sandstones and carbonates. Experimental and modeling results revealed that the combination of LSWF and chemical EOR yields a higher efficiency compared to the individual EOR method. The interplay of underlying mechanisms during the hybrid process resulting in higher oil recovery efficiency was elucidated. Finally, gaps in research and recommendation for future studies were highlighted.
Development of green, eco-friendly, and efficient adsorbents for wastewater treatment is highly researched to mitigate the alarming rate of water pollution. In this work, a novel biocomposite of biosilica (BS)/silk fibroin (SF)/polyurethane foam (PUF) was prepared and studied for the adsorptive recovery of toxic copper (Cu2+) and chromium (Cr6+) ions from synthetic wastewater. XRD and FTIR studies confirmed the formation of biocomposite with amorphous structure and –OH, –NH, C=O surface functionalities. SEM results confirmed the successful incorporation of SF and PUF into BS. The biocomposite possessed a specific surface area of 751.9 m²/g and a mean pore size of 7.21 nm. Further, effects of pH, adsorbent dose, contact time, and initial metal ion concentration on the adsorptive removal of Cu2+ and Cr6+ metal ions by the BS/SF/PUF biocomposite were studied in detail. Equilibrium and kinetic analysis showed that the metal ions sequestration by the BS/SF/PUF biocomposite followed the Freundlich isotherm and Elovich model, respectively. Maximum adsorption capacities of 331.69 mg/g and 201.56 mg/g were estimated for the Cu2+ and Cr6+ ions, respectively. A plausible mechanism for the adsorption of the metal ions onto BS/SF/PUF biocomposite is postulated. Reusability studies of the biocomposite using EDTA eluent showed that the biocomposite could be efficiently reused up to four consecutive adsorption/desorption cycles. Thus, this study provides comprehensive data for applying the BS/SF/PUF biocomposite to treat Cu2+ and Cr6+ metal ions polluted wastewater streams.
Managed aquifer recharge (MAR) offers a promising strategic management alternative for water storage and subsequent recovery to alleviate water shortage and to protect coastal aquifers from saltwater intrusion. Selecting potentially suitable recharge sites remains challenging, particularly in heterogeneous karst systems. In this study, MAR site suitability in a karst coastal aquifer is examined using a new geospatial approach that accounts for aquifer rechargeability properties, water availability, and economic-environmental attractiveness. For this purpose, multi-criteria decision analysis (MCDA), supported by pairwise comparisons, with an intrinsic karst aquifer rechargeability index is coupled with a raster-based hydrologic model that was forced by remotely-sensed precipitation, temperature, and land use data. The approach was successfully used to define exclusionary zones and to identify sites with high MAR potential that were independently collocated with hydrogeological indicators ascertaining its potential for site suitability mapping in systems with prevailing karstic aquifers.
Solar Photovoltaic (PV) modules provide a reliable and clean electricity source that can suit various applications. Based on the quality and type of crystals, the PV modules are classified as monocrystalline, polycrystalline, and thin film. The improvements on different solar PV modules can be made efficiently with accurate mathematical models, which requires extracting its parameters. This paper suggests a novel enhanced hybrid grey wolf optimizer-sine cosine algorithm (EHGWOSCA) to extract solar PV module parameters. The recommended algorithm is validated on CEC-C06 2019 benchmark functions which contain ten standard functions. The PV module parameter extraction is performed on different kinds of PV modules, namely, monocrystalline type Shell CS6K280M, polycrystalline type Shell S75, and thin-film type Shell ST40. Two well-known models of PV modules are considered with a requirement of 5 parameters and 7 parameters extraction. The proposed algorithm is implemented by minimizing the sum of squared errors at open circuit point, short circuit point, and maximum power point (MPP). The results obtained show that in the Monocrystalline model using the proposed hybrid approach with single diode model, an error is 1.0718E−15, and it is further reduced to 1.3229E−16 with the double diode model. The proposed EHGWOSCA Error in the Polycrystalline model with double diode is 6.1594E−19, and the Thin-film model error is 2.91E−22. The effectiveness of the proposed approach is verified by comparative analysis with other methods available in the literature.
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3,230 members
Wael Zaki
  • Department of Mechanical Engineering
Maguy Abi jaoudé
  • Department of Chemistry - Center for Catalysis and Separation (CeCaS) - Center for Membranes and Advanced Water Technology (CMAT)
Okobi Ekpo
  • College of Medicine and Health Sciences
Abu Dhabi, United Arab Emirates
Head of institution
Dr. Arif Sultan Al Hammadi