Florida Institute of Technology
  • Melbourne, USA, United States
Recent publications
We examined current waveforms for 58 upward flashes occurring in 2006–2014 initiated from the Gaisberg Tower located near Salzburg, Austria. The initial stage (IS) of these flashes comprised of relatively slowly varying “background” current, with faster, more impulsive current variations overlaid on this background current. In 46 of the 58 flashes (79%) the background continuing current was negative, and in the other 12 flashes (21%) it was bipolar. 1180 current pulses occurred during the IS of these 58 flashes, of which 708 (60%) were positive bipolar (positive initial polarity with a negative opposite polarity overshoot), 28 (2.4%) were positive unipolar (positive initial polarity with no opposite polarity overshoot), 440 (37%) were negative unipolar, and four (0.3%) were negative bipolar pulses. We found that bipolar current pulses only occurred in the IS at early times. We divided the IS current into two phases: (1) upward leader initiation and propagation phase (IPP) and (2) upward leader mature phase (MP). 901 or 76% (712 bipolar and 189 unipolar) pulses occurred during the IPP, and 279 or 21% (unipolar) pulses occurred during the MP. The median background-to-peak current was 134 A for IPP pulses and 687 A for MP pulses.
We examined the characteristics of electric field signatures occurring during the initial stage of 58 flashes measured simultaneously at near (170 m) and far (79 or 109 km) distances from the Gaisberg Tower located near Salzburg, Austria. Of the 340 field signatures measured at the near station, 68 (20%) were associated with current pulses occurring during the initiation and propagation phase (IPP) of the upward leader, and 272 (80%) were associated with pulses that occurred during the mature phase (MP) of the upward leader. Of the 68 field signatures of IPP pulses, 40 were associated with bipolar (IPP-B type) current pulses and 28 were associated with unipolar (IPP-U type) current pulses. Field signatures of IPP-B pulses were only detected at the near measurement station and appear to be associated with currents in relatively short (meter-scale) channel segments formed during the upward leader inception. At the far stations, field signatures of 84 IS pulses were recorded and analyzed. There was modest correlation between the background-to-peak current of IS pulses and near and far electric radiation field changes as well as between radiation field changes recorded at near and far distances.
Flip chip has become one of the mainstream technologies in microelectronic packaging. Solder bumps play an important role in the interconnection of flip chips packages. The scanning acoustic microscopy (SAM) technology and a new network model were investigated for intelligent detection of flip chips. A new network model called CBN-S-Net was proposed based on a deep convolution network CBN and an optimized Siamese network. The CBN convolution network was used to extract the deep fusion features of solder bumps, and new triplet sample pairs were designed to measure the similarity between solder bumps. With the strategy of triplet sample pairs, the SAM images of the flip chip were used to verify the effectiveness of the designed network model. The results showed that the improved network has a high detection accuracy of 98.73%, and the proposed method is effective for the intelligent detection of solder bumps in high-density electronic packages.
This paper presents a suite of ice load models using a coupled aero-hydro-servo-elastic numerical model to study dynamic response of floating offshore wind turbines in cold regions that sea ice is present. The coupled aero-hydro-servo-elastic model consists of wind inflow dynamics, rotor aerodynamics, structural multibody dynamics of the system, wave and current kinematics, hydrodynamics, mooring-line dynamics and ice loads. This open-source numerical model provides a modular framework to investigate the common wind turbine configurations, support structure systems and mooring lines for different environmental conditions. The 5 MW National Renewable Energy Laboratory wind turbine is used as a target turbine. Verification studies are performed to show the accuracy of the numerical model. The analyses show that the proposed approach provides reasonable results for the calculation of ice loads on the offshore wind turbine structure as well as the system dynamic response. It is expected that the proposed model would be widely used to study, investigate and analyze the different aspects of floating offshore wind turbine design, leading to the promotion and advancement of science and technology in the offshore renewable energy industry.
This work leverages recent advancements in computer vision and deep learning to detect and track the motion of droplets captured by a camera. While classical computer vision techniques have been employed for detection and tracking, those approaches have limitations and are not trivially extended to droplets. We approach the problems of droplet detection and tracking through a data-driven framework, in which an annotated database of droplet images is built and object detection and tracking models are trained on this database. The accuracy of the model is evaluated and the whole process is discussed. At this point, droplet geometric properties can be extracted. This information is critical in understanding the effectiveness of a system that is spraying the droplets.
The wind farm layout optimization (WFLO) problem is a complex and nonconvex optimization problem. Even though many different heuristic algorithms and mathematical programming methods have been tested and discussed, there is no consensus about which algorithm is the most suitable approach for solving WFLO problems. Every algorithm presents its own advantages and disadvantages in solving different optimization problems; thus, multi-stage approaches may combine the advantages of multiple algorithms and offer superior performance. One multi-stage approach used for solving WFLO problems is to apply an algorithm in the first stage to produce an optimized layout which serves as the initial condition for a second-stage algorithm to perform further refinement. This paper presents a comparison between two types of multi-stage methods: the Heuristic-Gradient-based (H-G) model which consists of a heuristic algorithm in stage 1 and a gradient-based algorithm in stage 2 and the Discrete-Continuous (D-C) model which consists of a heuristic algorithm in the discrete scheme in stage 1 and an algorithm in the continuous scheme in stage 2. Annual energy production (AEP) is used as the objective function while the computational time associated with each approach is documented. Three scenarios are investigated in this paper with different complexity in the wind conditions. It was observed that the D-C models provide the optimal solutions with an average of 0.67% higher AEP and an average of 6.2% lower computational time in comparison with the H-G models. The results from this study provide a basis for selecting a proper optimization algorithm for solving WFLO problems which can lead to a significant increase in the overall annual energy production and a large reduction in computational time.
Xenogeneic sources of collagen type I remain a common choice for regenerative medicine applications due to ease of availability. Human and animal sources have some similarities, but small variations in amino acid composition can influence the physical properties of collagen, cellular response, and tissue remodeling. The goal of this work is to compare human collagen type I‐based hydrogels versus animal‐derived collagen type I‐based hydrogels, generated from commercially available products, for their physico‐chemical properties and for tissue engineering and regenerative medicine applications. Specifically, we evaluated whether the native human skin type I collagen could be used in the three most common research applications of this protein: as a substrate for attachment and proliferation of conventional 2D cell culture; as a source of matrix for a 3D cell culture; and as a source of matrix for tissue engineering. Results showed that species and tissue specific variations of collagen sources significantly impact the physical, chemical, and biological properties of collagen hydrogels including gelation kinetics, swelling ratio, collagen fiber morphology, compressive modulus, stability, and metabolic activity of hMSCs. Tumor constructs formulated with human skin collagen showed a differential response to chemotherapy agents compared to rat tail collagen. Human skin collagen performed comparably to rat tail collagen and enabled assembly of perfused human vessels in vivo. Despite differences in collagen manufacturing methods and supplied forms, the results suggest that commercially available human collagen can be used in lieu of xenogeneic sources to create functional scaffolds, but not all sources of human collagen behave similarly. These factors must be considered in the development of 3D tissues for drug screening and regenerative medicine applications.
The post-release mortality (PRM) rate of sharks can be an important factor in estimating overall mortality in stock assessments. However, the accuracy of PRM rates is dependent on our ability to evaluate mortality under realistic fishing practices and conditions. In the Southeast U.S. recreational rod-and-reel fishery, we worked directly with recreational fishing captains to investigate the PRM of blacknose sharks, Carcharhinus acronotus. These sharks are primarily discarded alive in this fishery, but as PRM for this species has yet to be investigated, it is challenging to address the impact of recreational fishing on blacknose shark populations and assess the effectiveness of management measures. We examined PRM by first capturing blacknose sharks (n = 63) with South Carolina and Florida charter captains. Blood samples were taken immediately after capture to evaluate blood-based indicators of capture stress, and sharks were fit with acceleration data logger tags (measuring tri-axial acceleration, depth, and water temperature) to monitor fine-scale behavior and mortality for up to 136 h post-release. Sharks captured in warmer water temperatures displayed increased lactate and decreased pH, the latter of which was more pronounced in females. Fine-scale behavior profiles revealed survivorship for most animals and impaired swimming and mortality in others. All mortality events were attributed to predation events, as evidenced by marked changes in movement that deviated from recorded blacknose shark swimming behavior. Our estimated PRM rates ranged from 3.7% to 41.5%, depending on the release condition of the animal (i.e., increasing level of swimming impairment). Handling duration negatively influenced release condition, indicating extended handling durations increased the probability of a shark showing impaired swimming. Fishery-scale PRM was estimated as 8.5% when considering the distribution of release conditions observed throughout the study. Tag data showed that surviving sharks took an average of 11.7 h to recover and resume normal swimming behavior, with recovery taking longer in warmer water. The overall post-release mortality rate in this fishery is relatively low but may still impact the stock if the total catch is high enough throughout the fishery. Findings from our study will enhance estimates of fishing mortality in future stock assessments and help formulate best practices to reduce the overall fishing mortality of blacknose sharks.
Power electronics packages typically comprise a dielectric substrate bonded to a metal layer and attached to heat sinks using a low-thermal-conductivity solder. These multiple layers increase the effective thermal resistance of the package and are responsible for package failures under cyclic thermal loads. Bonding the aluminum nitride dielectric layer (AlN) directly to a low coefficient of thermal expansion (CTE) aluminum silicon carbide (AlSiC) cold plate using copper‑aluminum (Cu-Al) transient liquid phase (TLP) bonding has been shown to improve the mechanical reliability of the power electronic packages while making the package more compact by bringing the cooling solutions closer to the devices. This study aims to characterize the Cu-Al bonds formed during the TLP bonding of AlSiC with three different power electronics substrates: pure aluminum nitride (AlN), aluminum (DBA), and copper (DBC). The material compositions and microstructures of the bonds were analyzed using scanning electron microscopy (SEM), x-ray spectroscopy (EDS), confocal scanning acoustic microscopy (C-SAM), and x-ray diffraction (XRD). α-Al solid compound was identified as the dominant phase in AlN-AlSiC and Al-AlSiC, with a notable presence of SiC particles. In contrast, three intermetallic phases – θ-CuAl2, η-CuAl, and γ′- Cu9Al4 – were observed in the Cu-AlSiC bond. A computational solid-state diffusion model was developed to predict the intermetallic compounds (IMCs) formed during Cu-Al TLP bonding in each system, which supported the observation that an initial Cu volume fraction of 20 % in the material layers produced a final bond composition of >95 % Al with minimal IMC growth. However, increased Cu concentrations produced higher concentration gradients, leading to increased growth of IMCs, Kirkendall voids, and interstitial cracks.
This paper presents a physics-guided residual recurrent neural network (PGRRNN) and graphics processing unit (GPU)-accelerated model predictive control (MPC) framework to combat two specific challenges in artificial neural network (ANN)-based nonlinear MPC of high-rate dynamics systems, i.e., low control latency and insufficient model accuracy or generalization ability. Different from traditional ANN models, PGRRNN utilizes approximate physics-based (PB) models (with parameter uncertainty) as a backbone to impose physical constraints/guidance for future state prediction, and reconciles the difference between PB model approximation and data collected from actual systems by propagating their residuals through a multilayer recurrent neural network, hence improving its accuracy and generalization and alleviating data volume requirement. For computing acceleration, both PGRRNN and particle swarm optimization (PSO) are implemented on a GPU platform to make use of its massive parallel processing threads. Numerical experiments for MPC trajectory tracking of a quadcopter are used to examine accuracy and robustness of PGRRNN, and its performance is compared with other ANN models and approximate PB models. PGRRNN outperforms the other models in both ideal and realistic environments, exhibiting 2–3 times lower tracking error than the pure data-driven model. Furthermore, it is demonstrated that GPU-based PSO is able to synthesize control signals at a rate of greater than 50 Hz and can be a promising approach for ANN-based nonlinear MPC.
In this paper, we deal with a mixed reliability system decaying from natural wear, occasional soft and hard shocks that eventually lead the system to failure. The aging process alone is linear and it is escalated through soft shocks such that they lead to the system’s soft failure when the combined damage exceeds a threshold M. The other threat is that posed by occasional hard shocks. When the total number of them identified as critical (each critical shock exceeds a fixed threshold H) reaches N, the system becomes disabled. With N=1, a critical shock is extreme. The arrival stream of shocks is a renewal process marked by soft and hard shocks. We establish a formula for a closed form functional containing system’s time-to-failure, the state of the system upon its failure, and other useful statistical characteristics of the system using and embellishing fluctuation analysis and operational calculus. Special cases provide tame expressions that are computed and validated by simulation.
Tropical and subtropical coastal flats are shallow regions of the marine environment at the intersection of land and sea. These regions provide myriad ecological goods and services, including recreational fisheries focused on flats-inhabiting fishes such as bonefish, tarpon, and permit. The cascading effects of climate change have the potential to negatively impact coastal flats around the globe and to reduce their ecological and economic value. In this paper, we consider how the combined effects of climate change, including extremes in temperature and precipitation regimes, sea level rise, and changes in nutrient dynamics, are causing rapid and potentially permanent changes to the structure and function of tropical and subtropical flats ecosystems. We then apply the available science on recreationally targeted fishes to reveal how these changes can cascade through layers of biological organization—from individuals, to populations, to communities—and ultimately impact the coastal systems that depend on them. We identify critical gaps in knowledge related to the extent and severity of these effects, and how such gaps influence the effectiveness of conservation, management, policy, and grassroots stewardship efforts.
Principles of moving mass control of the orbital parameters of an artificial dumbbell shaped satellite are discussed in the present paper. The rationale is to implement a non-jet principle of actuation by varying the geometry of the satellite through its internal degrees-of-freedom. This can be achieved by spinning the massive parts of the dumbbell and changing their relative distance upon the orbital angle according to the suggested control strategies. The control schemes aim at maintaining a desired satellite size and orientation with respect to the orbital radius in order to take advantage of the variations in the gravitational field along the elliptical orbit. The results demonstrate that the total orbital energy can follow a prescribed temporal profile by controlling the satellite orientation on the orbit to accurately track its desired target. Analytical estimates for the satellite’s energy versus the number of orbital cycles are determined from closed-form solutions. Results from both analytical estimates and numerical integration are in sufficient agreement.
The application of 3D printing technologies fields for biological tissues, organs, and cells in the context of medical and biotechnology applications requires a significant amount of innovation in a narrow printability range. 3D bioprinting is one such way of addressing critical design challenges in tissue engineering. In a more general sense, 3D printing has become essential in customized implant designing, faithful reproduction of microenvironmental niches, sustainable development of implants, in the capacity to address issues of effective cellular integration, and long-term stability of the cellular constructs in tissue engineering. This review covers various aspects of 3D bioprinting, describes the current state-of-the-art solutions for all aforementioned critical issues, and includes various illustrative representations of technologies supporting the development of phases of 3D bioprinting. It also demonstrates several bio-inks and their properties crucial for being used for 3D printing applications. The review focus on bringing together different examples and current trends in tissue engineering applications, including bone, cartilage, muscles, neuron, skin, esophagus, trachea, tympanic membrane, cornea, blood vessel, immune system, and tumor models utilizing 3D printing technology and to provide an outlook of the future potentials and barriers.
Formal verification provides assurance to the modeling and design of robotic applications in executing autonomous operations. With the advancement of technologies, robotic applications have evolved to integrate multiple distributed robots. As a result, the integration of formal verification-based methods to assure the correctness of the interactions between multiple distributed robots has become ever more important. However, going from formally verified models designed in formal environments/software such as UPPAAL to robotic simulation software such as robot operating system (ROS) and Gazebo is time-consuming and prone to human errors. Nonetheless, such a translation from formal to simulation environment is essential for robotic applications that are going to be deployed in the real world, for obvious economical and safety reasons. In this article, we provide our insights into the development of a framework that integrates design and formal verification at a higher level of abstraction and then performing a translation to ROS, focusing on a scenario for distributed drones representing urban air mobility. Through this article, we seek to accelerate the development cycle in transitioning from formally verified systems to simulation.
Sub-Saharan Africa’s economy and livelihood are primarily dependent on agriculture, which makes it highly vulnerable to the impacts of extreme weather events and climate change. Modelling and quantifying extreme rainfall and its temporal changes in such environments could thus provide crucial information for design, insurance, management, ecology and climate adaptation. Rain gauge networks in the area are relatively sparse, and often characterized by missing data, which hamper the use of extreme-value methods for estimating extreme precipitation quantiles. In this study, we adopted the Simplified Metastatistical Extreme Value approach for the estimation of extreme return levels based on ordinary events (i.e., all the independent realizations of the variable of interest), which was shown to be more accurate than traditional extreme-value methods in the presence of short data records. We examined data from 66 rain gauges covering diverse hydro-climatic regions across Ghana with the aim of (i) validating the robustness of the statistical approach, (ii) characterising the climatic and altitudinal controls on the occurrence, frequency and intensity of rainfall extremes, and (iii) quantifying recent changes in the characteristics of extremes. We found that a two-parameter Weibull distribution well approximates the tail of the daily rainfall distribution throughout the area. Our statistical approach can quantify extremes with largely reduced uncertainties (7–17% uncertainty in the 100-year return levels computed using 10 years of data versus 11–62% of extreme-value based methods). Extreme precipitation statistics (daily intensity distribution, number of wet days, extreme rainfall quantiles) are found to significantly depend on latitude, so that the four latitudinally layered hydro-climatic regions typically adopted in the area well represent spatial variations. Elevation significantly affects the tail heaviness of the daily intensity distribution and thus extreme rainfall quantiles. Temporal changes during the period 1978–2018 are found to be non-homogeneous in the area as well as within the four hydro-climatic regions, but are homogeneous in three altitude-based regions. We report contrasting trends in extreme return levels in low-elevation (<200 m a.s.l.) and hilly regions, related to contrasting changes in the daily intensity distribution. Statistically significant positive trends in extreme daily rainfall amounts are observed in the inland low-elevation region of the Volta river basin, which call for further investigation of changes in future precipitation extremes in this extremely important hydrological region in Sub-Saharan Africa.
Traditional approaches to dietary assessment in fish necessitate the collection of stomach contents through either gastric lavage or lethal sampling. The Atlantic bonefish (Albula vulpes) is an economically important sportfish in the western central Atlantic region for which a minimally invasive, non-lethal alternative to morphological dietary assessment would be extremely useful. Here, we compare dietary DNA metabarcoding from cloacal swabs of 16 A. vulpes to dietary composition data obtained using traditional morphological classification techniques and metabarcoding of homogenized stomach contents. Further, we compare the performance of two commonly used barcoding genes (18S rRNA and COI) at inferring A. vulpes diet composition. We found that detection of taxa and the resolution of taxonomic annotation varied between markers, suggesting a multi-marker approach is likely to provide the most complete results. Importantly however, the number of dietary OTUs identified and the taxonomic composition of the core diet were not significantly different between molecular markers. Dietary compositions identified using metabarcoding approaches differed in both diversity and composition from matched dietary data obtained from morphologically analyzed stomach contents; however, the same core prey classes were identified using both methods, suggesting that metabarcoding does indeed offer a viable alternative to morphological dietary assessment. Importantly, dietary compositions identified by metabarcoding of cloacal swabs did not differ significantly from those identified by metabarcoding of stomach contents. Metabarcoding of minimally invasive cloacal swabs should be considered for dietary studies of bonefish and other fish species for which invasive or lethal sampling is problematic.
In this study, a passive tuned mass damper (TMD) is investigated to dampen resonant motions of a submerged floating tunnel (SFT) in waves and earthquakes. TMD is adopted to control resonant lateral motion. A time-domain dynamics simulation model that considers the elasticity of the tunnel and mooring lines is first established, which is based on the lumped mass method and Morison equation, and the SFT-TMD interaction is considered through springs and dampers. Next, by using the harmony search (HS) algorithm, TMD's spring and damping coefficients are optimized; the dynamics simulations under white noise seismic excitations are continuously carried out with updated spring and damping coefficients produced by HS rules until the designed maximum iteration number. The influence of TMD's mass is individually investigated through a parametric study. After the optimization process is completed, the global performances of SFT with and without TMD are systematically evaluated in both waves and earthquakes. It is found that TMD plays a crucial role in controlling SFT vibrations when environmental loads are close to the system's fundamental lateral natural frequency under both wave and seismic excitations. TMD also enhances the comfort of passengers and reduces static and dynamic mooring tensions.
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Efthymios I. Nikolopoulos
  • Department of Mechanical and Civil Engineering
Marco M. Carvalho
  • Department of Computer Science
Thomas C Eskridge
  • Harris Institute for Assured Information
Vipuil Kishore
  • Department of Chemical Engineering
150 W. University Blvd, 32901, Melbourne, USA, United States
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Dr. T. Dwayne McCay
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