VTechWorks Repository :: Browsing by Author "Tarazaga, Pablo Alberto"

Browsing by Author "Tarazaga, Pablo Alberto"

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Acoustic Response Validation of a Finite Cylindrical Shell with Multiple Loading Conditions
Gallagher, Chad Taylor (Virginia Tech, 2018-06-25)
Cylindrical shells are used for a variety of engineering applications such as undersea vehicles and aircraft. The models currently used to determine the vibration characteristics of these shells are often approximated by assuming the shell is infinitely long or has shear-diaphragm boundary conditions. These models also ignore complex loading conditions such as plane waves in favor of point forces or free vibration models. This work expands on the capabilities of these models by examining the acoustic response of a finite length cylinder with flat plate endcaps to multiple types of distributed loading conditions. Starting with the Donnell equations of motion for thin cylinders and the classical plate theory equations of motion for the endcaps, spacial domain displacement field solutions for the shell and plates take an assumed form that includes unknown wave propagation coefficients. These solutions are substituted into stress boundary conditions and continuity equations evaluated at the intersections between the shell and plates. An infinite summation is contained within the boundary conditions and continuity equations which is decoupled, truncated, and compiled in matrix form to allow for the propagation coefficients to be found via a convergent sum of vectors. System responses due to a ring loading and multiple cases of plane waves are studied and validated using a finite element analysis of the system. It is shown that the analytical model matches the finite element model well.
Airflow sensing with arrays of hydrogel supported artificial hair cells
Sarlo, Rodrigo (Virginia Tech, 2015-01-19)
Arrays of fully hydrogel-supported, artificial hair cell (AHC) sensors based on bilayer membrane mechanotransduction are designed and characterized to determine sensitivity to multiple stimuli. The work draws upon key engineering design principles inspired by the characteristics of biological hair cells, primarily the use of slender hair-like structures as flow measurement elements. Many hair cell microelectromechanical (MEMS) devices to sense fluid flow have already been built based on this principle. However, recent developments in lipid bilayer applications, namely physically encapsulated bilayers and hydrogel interface bilayers, have facilitated the development of AHCs made primarily from biomolecular materials. The most current research in this field of "membrane based AHCs," shows promise, yet still lacks the modularity to create large sensor arrays similar to those in nature. This paper presents a novel bilayer based AHC platform, developed for array implementation by applying some of the core design principles of biological hair cells. These principles are translated into key design, fabrication and material considerations toward improved sensor sensitivity and modularity. Single hair cell responses to base excitation and short air pulses are to investigate the dynamic coupling between hair and bilayer membrane transducer. In addition, a spectral analysis of the AHC system under varying voltages and air flow velocities helps to build simple, predictive models for the sensitivity properties of the AHC. And finally, based on these results, we implement a spatial sensing strategy that involves mapping frequency content to stimulus location by "tuning" linear, three-unit arrays of AHCs. Individual AHC sensors characterization results demonstrate peak current outputs in the nanoamp range and measure flow velocities as high as 72 m/s. Characterization of the AHC response to base excitation and air pulses show that membrane current oscillates with the first three bending modes of the hair. Output magnitudes reflect of vibrations near the base of the hair. A 2 degree-of-freedom Rayleigh-Ritz approximation of the system dynamics yields estimates of 19 N/m and 0.0011 Nm/rad for the equivalent linear and torsional stiffness of the hair's hydrogel base, although double modes suggest non-symmetry in the gel's linear stiffness. The sensor output scales linearly with applied voltage (1.79 pA/V), avoiding a higher-order dependence on electrowetting effects. The free vibration amplitude of the sensor also increases in a linear fashion with applied airflow pressure (3.39 pA/m s??). Array sensing tests show that the bilayers' consistent spectral responses allow for an accurate localization of the airflow source. However, temporal variations in bilayer size affect sensitivity properties and make airflow magnitude estimation difficult. The overall successful implementation of the array sensing method validates the sensory capability of the bilayer based AHC.
ANC of UAS Rotor Noise using Virtual Error Sensors
Polen, Melissa Adrienne (Virginia Tech, 2021-03-12)
Traditional active noise control (ANC) systems rely on a physical sensor to measure the error signal at the desired location of attenuation. The error signal is then used to update an adaptive controller, which ultimately attenuates the measured response. However, it is not always practical to use traditional ANC in real-world applications. For example, as small unmanned aerial systems (UAS) become more commonly used, community noise exposure also increases, along with the desire to reduce UAS noise. Traditional ANC systems that rely on physical sensors at observer locations are impractical, since a UAS does not typically have real-time access to the response at an observer's ears, which is realistically in the far-field. Virtual error sensing (VES) can augment an ANC system using near-field measurements to estimate the response at a desired far-field location. In this way, the VES technique effectively shifts the zone of quiet from the location of the physical sensor(s) to a different "virtual" location. This thesis begins by outlining past work that used traditional ANC methods and virtual error sensing techniques. Numerical modeling results showing the predicted spatial change in SPL achieved using a virtual sensor will be presented. Experimental tests used ANC to attenuate the noise from a single UAS rotor at far-field locations using a near-field microphone and the remote microphone technique (RMT) to develop the VES. The results of the VES alone and with an ANC approach at several far-field virtual locations will be presented and discussed.
Animal Motion Analysis and Approximation for Robotics
Liu, Bowei (Virginia Tech, 2022-06-07)
As the robotic industry has matured, the study of animal motion has given rise to many robot designs. Researchers from multiple areas, such as biomechanics, control theory, and machine learning, have spent their energy and efforts making robots more realistic. The intent is that the automatic system can replace real animals and even perform certain tasks in harsh, or even dangerous environments. However, animal motions encompass a wide range of motion that depends on body geometries and various animal behaviors. From human walking to lizards crawling, from dogs running to horses pacing, many studies of motion only focus on one species or a few behaviors. An ever-increasing collection of papers are published that study animal motions for different species and motion regimes, and these are often based on video footage and motion capture data. This is particularly true for human motion research. While there are huge volumes of data acquired from motion capture and video, not many researches as of yet are using dynamical system analysis techniques such as dynamic mode decomposition, extended dynamic mode decomposition, or even Koopman method to understand and compare the motion across different species. Thus, the goal of this thesis is to further develop the methods mentioned above to analyze and characterize animal motion. The algorithms derived should apply regardless the shape of the body or the number of degrees of freedom for the joins. Using strategies from statistical learning theory and Koopman operator theory, several methods are derived and compared. The analysis culminates in a motion approximation, that subsequently could be used in robotic control to emulate an animal motion as much as possible.
Application of Multifunctional Doppler LIDAR for Non-contact Track Speed, Distance, and Curvature Assessment
Munoz, Joshua (Virginia Tech, 2015-12-08)
The primary focus of this research is evaluation of feasibility, applicability, and accuracy of Doppler Light Detection And Ranging (LIDAR) sensors as non-contact means for measuring track speed, distance traveled, and curvature. Speed histories, currently measured with a rotary, wheel-mounted encoder, serve a number of useful purposes, one significant use involving derailment investigations. Distance calculation provides a spatial reference system for operators to locate track sections of interest. Railroad curves, using an IMU to measure curvature, are monitored to maintain track infrastructure within regulations. Speed measured with high accuracy leads to high-fidelity distance and curvature data through utilization of processor clock rate and left-and right-rail speed differentials during curve navigation, respectively. Wheel-mounted encoders, or tachometers, provide a relatively low-resolution speed profile, exhibit increased noise with increasing speed, and are subject to the inertial behavior of the rail car which affects output data. The IMU used to measure curvature is dependent on acceleration and yaw rate sensitivity and experiences difficulty in low-speed conditions. Preliminary system tests onboard a 'Hy-Rail' utility vehicle capable of traveling on rail show speed capture is possible using the rails as the reference moving target and furthermore, obtaining speed profiles from both rails allows for the calculation of speed differentials in curves to estimate degrees curvature. Ground truth distance calibration and curve measurement were also carried out. Distance calibration involved placement of spatial landmarks detected by a sensor to synchronize distance measurements as a pre-processing procedure. Curvature ground truth measurements provided a reference system to confirm measurement results and observe alignment variation throughout a curve. Primary testing occurred onboard a track geometry rail car, measuring rail speed over substantial mileage in various weather conditions, providing high-accuracy data to further calculate distance and curvature along the test routes. Tests results indicate the LIDAR system measures speed at higher accuracy than the encoder, absent of noise influenced by increasing speed. Distance calculation is also high in accuracy, results showing high correlation with encoder and ground truth data. Finally, curvature calculation using speed data is shown to have good correlation with IMU measurements and a resolution capable of revealing localized track alignments. Further investigations involve a curve measurement algorithm and speed calibration method independent from external reference systems, namely encoder and ground truth data. The speed calibration results show a high correlation with speed data from the track geometry vehicle. It is recommended that the study be extended to provide assessment of the LIDAR's sensitivity to car body motion in order to better isolate the embedded behavior in the speed and curvature profiles. Furthermore, in the interest of progressing the system toward a commercially viable unit, methods for self-calibration and pre-processing to allow for fully independent operation is highly encouraged.
Assessing Limb Symmetry using the Clinically Accessible loadsol®
Renner, Kristen Elizaberth (Virginia Tech, 2019-04-23)
Decreased gait symmetry has been correlated with an increased fall risk, abnormal joint loading and decreased functional outcomes. Therefore, symmetry is focused on in the rehabilitation of many patient populations. Currently, load based symmetry is collected using expensive and immobile devices that are not clinically accessible, but there is a clinical need for an objective measure of loading symmetry during daily tasks like walking. Therefore, the purpose of this dissertation was to 1) assess the validity and reliability of the loadsol® to capture ground reaction force data, 2) use the loadsol® to determine the differences in symmetry between adults with a TKA and their healthy peers and 3) explore the potential of a commercially available biofeedback system to acutely improve gait symmetry in adults. The results of this work indicate that the loadsol® is a valid and reliable method of collecting loading measures during walking in both young and older adults. TKA patients who are 12-24 months post-TKA have lower symmetry in the weight acceptance peak force, propulsive peak force and impulse when compared to their healthy peers. Finally, a case study with four asymmetric adults demonstrated that a 10-minute biofeedback intervention with the loadsol® resulted in an acute improvement in symmetry. Future work is needed to determine the potential of this intervention to improve symmetry in patient populations and to determine whether the acute response is retained following the completion of the intervention.
Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning
Bales, Dustin Bennett (Virginia Tech, 2016-06-20)
The ability to classify occupants in a building has far-reaching applications in security, monitoring human health, and managing energy resources effectively. In this work, gender and weight of walkers are classified via machine learning or pattern recognition techniques. Accelerometers mounted beneath the floor of Virginia Tech's Goodwin Hall measured walkers' gait. These acceleration measurements serve as the inputs to machine learning techniques allowing for classification. For this work, the gait of fifteen individual walkers was recorded via fourteen accelerometers as they, alone, walked down the instrumented hallway, in multiple trials. These machine learning algorithms produce an 88 % accurate model for gender classification. The machine learning algorithms included are Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks. Data reduction techniques achieve a higher gender classification accuracy of 93 % and classify weight with 64% accuracy. The data reduction techniques are Discrete Empirical Interpolation Method (DEIM), Q-DEIM, and Projection Coefficients. A two-part methodology is proposed to implement the approach completed in this thesis work. The first step validates the algorithm design choices, i.e. using bagged or boosted decision trees for classification. The second step reduces the walking data measured to truncate accelerometers which do not aid in increasing characteristic classification.
Characterization and Modeling of the Thermal Properties of Photopolymers for Material Jetting Processes
Mikkelson, Emily Cleary (Virginia Tech, 2014-03-25)
One emerging application of additive manufacturing is building parts with embedded electronics, but the thermal management of these assemblies is a potential issue. Electrical components have efficiency losses, and a significant portion of that lost energy is converted into heat. Embedding electronics in PolyJet parts is of particular interest since material jetting additive manufacturing has the ability to deposit multiple, functionally graded materials on a pixel by pixel basis. Although there is existing literature on other PolyJet material properties, there is limited research on their thermal characterization. The goal of this work is to determine the thermal conductivities of select PolyJet photopolymers (VeroWhitePlus, TangoBlackPlus, and Grey60) by using the heat flow meter method. The resulting thermal conductivities are then applied in finite element analysis (FEA) simulations to model the thermal distribution of heated PolyJet parts. Two FEA models of one-dimensional conduction in PolyJet parts are defined and compared to a corresponding physical model to verify the thermal conductivity measurements; one simulation expresses thermal conductivity as a function of temperature and the other uses an average value of thermal conductivity. The thermal conductivities were determined for a range of temperatures, and the average values were 0.2376 W/(m•K), 0.2307 W/(m•K), and 0.2272 W/(m•K) for VeroWhitePlus, TangoBlackPlus, and Grey60, respectively. When applying the thermal conductivity results to an FEA model, it was concluded that defining thermal conductivity as a function of temperature (as opposed to a constant value), reduced the average error in the predicted temperatures by less than 1%.
Characterization of Oscillatory Lift in MFC Airfoils
Lang Jr, Joseph Reagle (Virginia Tech, 2014-11-25)
The purpose of this research is to characterize the response of an airfoil with an oscillatory morphing, Macro-fiber composite (MFC) trailing edge. Correlation of the airfoil lift with the oscillatory input is presented. Modal analysis of the test airfoil and apparatus is used to determine the frequency response function. The effects of static MFC inputs on the FRF are presented and compared to the unactuated airfoil. The transfer function is then used to determine the lift component due to cambering and extract the inertial components from oscillating airfoil. Finally, empirical wind tunnel data is modeled and used to simulate the deflection of airfoil surfaces during dynamic testing conditions. This research serves to combine modal analysis, empirical modeling, and aerodynamic testing of MFC driven, oscillating lift to formulate a model of a dynamic, loaded morphing airfoil.
A Comprehensive Hamiltonian Atmospheric Sound Propagation Model for Prediction of Wind Turbine Noise
McBride, Sterling M. (Virginia Tech, 2017-12-06)
Wind energy is the world´s fastest-growing renewable energy source. Thus, the amount of people exposed to wind farm noise is increasing. Due to its broadband amplitude modulated characteristic, wind turbine noise (WTN) is more annoying than noise produced by other common community/industrial sources. Aerodynamic noise along the blade span is the dominant noise source of modern large wind turbines. This type of noise propagates through the atmosphere in the proximity of wind farms. However, modelling and simulating WTN propagation over large distances is challenging due to the complexity of atmospheric conditions. Real temperature, wind velocity and relative humidity measurements typically show a characteristic nonlinear behavior. A comprehensive propagation model that addresses this problem while maintaining high accuracy and computational efficiency is necessary. A Hamiltonian Ray tracing (HRT) technique coupled to aerodynamically induced WTN is presented in this work. It incorporates acoustic wave refraction due to spatial speed of sound gradients, a full Doppler Effect formulation resulting from wind velocities in any arbitrary direction, proper acoustic energy dissipation during propagation, and ground reflection. The HRT method averts many of the setbacks presented by other common numerical approaches such as fast field program (FFP), parabolic equation methods (PE), and the standard Eikonal ray tracing (ERT) technique. In addition, it is not bounded to the linearity assumptions made for analytic propagation solutions. A wave phase tracking analysis through inhomogeneous and moving media is performed. Curved ray-paths are numerically computed by solving a non-linear system of coupled first order differential equations. Sound pressure levels through the propagation media are then calculated by using standard ray tubes and performing energy analysis along them. The ray model is validated by comparing a monopole’s ray path results against analytically obtained ones. Sound pressure level predictions are also validated against both FFP and ERT methods. Finally, results for a 5MW modern wind turbine over a flat acoustically soft terrain are provided.
Computational Investigation of Strain and Damage Sensing in Carbon Nanotube Reinforced Nanocomposites with Descriptive Statistical Analysis
Talamadupula, Krishna Kiran (Virginia Tech, 2020-09-11)
Polymer bonded explosives (PBXs) are composites comprised of energetic crystals with a very high energy density surrounded by a polymer binder. The formation of hotspots within polymer bonded explosives can lead to the thermal decomposition and initiation of the energetic material. A frictional heating model is applied at the mesoscale to assess the potential for the formation of hotspots under low velocity impact loadings. Monitoring of the formation and growth of damage at the mesoscale is considered through the inclusion of a piezoresistive carbon nanotube network within the energetic binder providing embedded strain and damage sensing. A coupled multiphysics thermo-electro-mechanical peridynamics framework is developed to perform computational simulations on an energetic material microstructure subject to these low velocity impact loads. With increase in impact energy, the model predicts larger amounts of sensing and damage thereby supporting the use of carbon nanotubes to assess damage growth and subsequent formation of hotspots. The framework is also applied to assess the combined effects of thermal loading due to prescribed hotspots with inertial effects due to low velocity impact loading. It has been found that the present model is able to detect the presence of hotspot dominated regions within the energetic material through the piezoresistive sensing mechanism. The influence of prescribed hotspots on the thermo-electro-mechanical response of the energetic material under a combination of thermal and inertial loading was observed to dominate the lower velocity impact response via thermal shock damage. In contrast, the higher velocity impact energies demonstrated an inertially dominated damage response. Quantifying the piezoresistive effect derived from embedding carbon nanotubes in polymers remains a challenge since these nanocomposites exhibit significant variation in their electro-mechanical properties depending upon factors such as CNT volume fraction, CNT dispersion, CNT alignment and properties of the polymer. Of interest is electrical percolation where the electrical conductivity of the CNT/polymer nanocomposite increases through orders of magnitude with increase in CNT volume fraction. Estimates and distributions for the electrical conductivity and piezoresistive coefficients of the CNT/polymer nanocomposite are obtained and analyzed with increasing CNT volume fraction and varying barrier potential, which is a parameter that controls the extent of electron tunneling. The effect of CNT alignment is analyzed by comparing the electro-mechanical properties in the alignment direction versus the transverse direction for different orientation conditions. Estimates of piezoresistive coefficients are converted into gage factors and compared with experimental sources in literature. The methodology for this work uses automated scripts which are used in conjunction with high performance computing to generate several 5 μm ×5 μm realizations for different CNT volume fractions. These realizations are then analyzed using finite elements to obtain volume averaged effective values, which are then subsequently used to generate measures of central tendency (estimated mean) and variability (standard deviation, coefficient of variation, skewness and kurtosis) in a descriptive statistical analysis.
Continual Traveling waves in Finite Structures: Theory, Simulations, and Experiments
Malladi, Vijaya Venkata Narasimha Sriram (Virginia Tech, 2016-07-06)
A mechanical wave is generated as a result of an external force interacting with the well-defined medium and it propagates through that medium transferring energy from one location to another. The ability to generate and control the motion of the mechanical waves through the finite medium opens up the opportunities for creating novel actuation mechanisms not possible before. However, any impedance to the path of these waves, especially in the form of finite boundaries, disperses this energy in the form of reflections. Therefore, it is impractical to achieve steady state traveling waves in finite structures without any reflections. In-spite of all these conditions, is it possible to generate waveforms that travel despite reflections at the boundaries? The work presented in this thesis develops a framework to answer this question by leveraging the dynamics of the finite structures without any active control. Therefore, this work investigates how mechanical waves are developed in finite structures and identifies the factors that influence steady state wave characteristics. Theoretical and experimental analysis is conducted on 1D and 2D structures to realize different type of traveling waves. Owing to the robust characteristics of the piezo-ceramics (PZTs) in vibrational studies, we developed piezo-coupled structures to develop traveling waves through experiments.The results from this study provided the fundamental physics behind the generation of mechanical waves and their propagation through finite mediums. This research will consolidate the outcomes and develop a structural framework that will aid with the design of adaptable structural systems built for the purpose. The present work aims to generate and harness structural traveling waves for various applications.
Creating Complex Hollow Metal Geometries Using Additive Manufacturing and Metal Plating
McCarthy, David Lee (Virginia Tech, 2012-06-25)
Additive manufacturing introduces a new design paradigm that allows the fabrication of geometrically complex parts that cannot be produced by traditional manufacturing and assembly methods. Using a cellular heat exchanger as a motivational example, this thesis investigates the creation of a hybrid manufacturing approach that combines selective laser sintering with an electroforming process to produce complex, hollow, metal geometries. The developed process uses electroless nickel plating on laser sintered parts that then undergo a flash burnout procedure to remove the polymer, leaving a complex, hollow, metal part. The resulting geometries cannot be produced directly with other additive manufacturing systems. Copper electroplating and electroless nickel plating are investigated as metal coating methods. Several parametric parts are tested while developing a manufacturing process. Copper electroplating is determined to be too dependent on the geometry of the part, with large changes in plate thickness between the exterior and interior of the tested parts. Even in relatively basic cellular structures, electroplating does not plate the interior of the part. Two phases of electroless nickel plating combined with a flash burnout procedure produce the desired geometry. The tested part has a density of 3.16g/cm3 and withstands pressures up to 25MPa. The cellular part produced has a nickel plate thickness of 800µm and consists of 35% nickel and 65% air (empty space). Detailed procedures are included for the electroplating and electroless plating processes developed.
Cyber-Physical Security for Additive Manufacturing Systems
Sturm, Logan Daniel (Virginia Tech, 2020-12-16)
Additive manufacturing (AM) is a growing section of the advanced manufacturing field and is being used to fabricate an increasing number of critical components, from aerospace components to medical implants. At the same time, cyber-physical attacks targeting manufacturing systems have continued to rise. For this reason, there is a need to research new techniques and methods to ensure the integrity of parts fabricated on AM systems. This work seeks to address this need by first performing a detailed analysis of vulnerabilities in the AM process chain and how these attack vectors could be used to execute malicious part sabotage attacks. This work demonstrated the ability of an internal void attack on the .STL file to reduce the yield load of a tensile specimen by 14% while escaping detection by operators. To mitigate these vulnerabilities, a new impedance-based approach for in situ monitoring of AM systems was created. Two techniques for implementing this approach were investigated, direct embedding of sensors in AM parts, and the use of an instrumented fixture as a build plate. The ability to detect changes in material as small as 1.38% of the printed volume (53.8 mm3) on a material jetting system was demonstrated. For metal laser powder bed fusion systems, a new method was created for representing side-channel meltpool emissions. This method reduces the quantity of data while remaining sensitive enough to detect changes to the toolpath and process parameters caused by malicious attacks. To enable the SCMS to validate part quality during fabrication required a way to receive baseline part quality information across an air-gap. To accomplish this a new process noise tolerant method of cyber-physical hashing for continuous data sets was presented. This method was coupled with new techniques for the storage, transmission, and reconstructing of the baseline quality data was implemented using stacks of "ghost" QR codes stored in the toolpath to transmit information through the laser position. A technique for storing and transmitting quality information in the toolpath files of parts using acoustic emissions was investigated. The ATTACH (additive toolpath transmission of acoustic cyber-physical hash) method used speed modulation of infill roads in a material extrusion system to generate acoustic tones containing quality information about the part. These modulations were able to be inserted without affecting the build time or requiring additional material and did not affect the quality of the part that contained them. Finally, a framework for the design and implementation of a SCMS for protecting AM systems against malicious cyber-physical part sabotage attacks was created. The IDEAS (Identify, Define, Establish, Aggregate, Secure) framework provides a detailed reference for engineers to use to secure AM systems by leveraging the previous work in vulnerability assessment, creation of new side-channel monitoring techniques, concisely representing quality data, and securely transmitting information to air-gapped systems through physical emissions.
Data Analytics for Statistical Learning
Komolafe, Tomilayo A. (Virginia Tech, 2019-02-05)
The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process
Data-Driven Modeling of Tracked Order Vibration in Turbofan Engine
Krishnan, Manu (Virginia Tech, 2022-01-11)
Aircraft engines are one of the most heavily instrumented parts of an aircraft, and the data from various types of instrumentation across these engines are continuously monitored both offline and online for potential anomalies. Vibration monitoring in aircraft engines is traditionally performed using an order tracking methodology. Currently, there are no representative and efficient physics-based models with the adequate fidelity to perform vibration predictions in aircraft engines, given various parametric dependencies existing among different attributes such as temperature, pressure, and external conditions. This gap in research is primarily attributed to the limited understanding of mutual interactions of different variables and the nonlinear nature of engine vibrations. The objective of the current study is three-fold: (i) to present a preliminary investigation of tracked order vibrations in aircraft engines and statistically analyze them in the context of their operating environment, (ii) to develop data-driven modeling methodology to approximate a dynamical system from input-output data, and (iii) to leverage these data-driven modeling methodologies to develop highly accurate models for tracked order vibration in a turbo-fan engine valid over a wide range of operating conditions. Off-the-shelf data-driven modeling techniques, such as machine learning methods (eg., regression, neural networks), have several drawbacks including lack of interpretability and limited scope, when applying them to a complex multiscale multi-physical dynamical system. Moreover, for dynamical systems with external forcing, the identified model should not only be suitable for a specific forcing function, but should also generally approximate the input-output behavior of the data source. The author proposes a novel methodology known as Wavelet-based Dynamic Mode Decomposition (WDMD). The methodology entails using wavelets in conjunction with input-output dynamic mode decomposition (ioDMD). Similar to time-delay embedded DMD (Delay-DMD), WDMD builds on the ioDMD framework without the restrictive assumption of full state measurements. The author demonstrates the present methodology's applicability by modeling the input-output response of an Euler-Bernoulli finite element beam model, followed by an experimental investigation. As a first step towards modeling the tracked order vibration amplitudes of turbofan engines, the interdependencies and cross-correlation structure between various thermo-mechanical variables and tracked order vibration are analyzed. The order amplitudes are further contextualized in terms of their operating regime, and exploratory data analyses are performed to quantify the variability within each operating condition (OC). The understanding of complex correlation structures is leveraged and subsequently utilized to model tracked order vibrations. Switching linear dynamical system (SLDS) models are developed using individual data-driven models constructed using WDMD, and its performance in approximating the dynamics of the $1^{st}$ order amplitudes are compared with the state-of-the-art time-delay embedded dynamic mode decomposition (Delay-DMD) and Lasso regression. A parametric approach is proposed to improve the model further by leveraging previously developed WDMD and Delay-DMD methods and a parametric interpolation scheme. In particular, a recently developed pole-residue interpolation scheme is adopted to interpolate between several linear, data-driven reduced-order models (ROMs), constructed using WDMD and Delay-DMD surrogates, at known parameter samples. The parametric modeling approach is demonstrated by modeling the transverse vibration of an axially loaded finite element (FE) beam, where the axial loading is the parameter. Finally, a parametric modeling strategy for tracked order amplitudes is presented by constructing locally valid ROMs at different parametric samples corresponding to each pass-off test. The performance of the parametric-ROM is quantified and compared with the previous frameworks. This work was supported by the Rolls-Royce Fellowship, sponsored by the College of Engineering, Virginia Tech.
Design and Characterization of Biomimetic Artificial Hair Cells in an Artificial Cochlear Environment
Travis, Jeffrey Philip (Virginia Tech, 2014-03-11)
This research details the creation and characterization of a new biomimetic artificial inner hair cell sensor in an artificial cochlear environment. Designed to mimic the fluid flows around the inner hair cells of the human cochlea, the artificial cochlear environment produces controlled, linear sinusoidal fluid flows with frequencies between 25 and 400 Hz. The lipid bilayer-based artificial inner hair cell generates current through changes in the bilayer's capacitance. This capacitance change occurs as the sensor's artificial stereocilium transfers the force in the fluid flow to the bilayer. Frequency tuning tests are performed to characterize the artificial inner hair cell's response to a linear chirp signal from 1 to 400 Hz. The artificial inner hair cell's response peaks at a resonant frequency of approximately 83 Hz throughout most of the tests. Modelling the artificial stereocilium as a pinned free beam with a rotational spring at the pinned end yields a rotational spring stiffness of 177*10^-6 Nm/rad. Results with 0 mV potential applied across the bilayer indicate that current generation at 0 mV likely comes from other sources besides the bilayer. Increasing the voltage potential increases the broadband power output of the system, with an approximately linear relationship. A final test keeps the fluid flow frequency constant and varies the fluid velocity and applied voltage potential. Manipulation of the applied voltage potential results in a fluid velocity to RMS current relationship reminiscent of the variable sensitivity of the human cochlea.
Design and implementation of vibration data acquisition in Goodwin Hall for structural health monitoring, human motion, and energy harvesting research
Hamilton, Joseph Marshall (Virginia Tech, 2015-04-28)
From 2012 - 2015 a foundation for future research in Goodwin Hall was designed, tested,developed, and implemented through an instrumentation project supported by the College of Engineering at Virginia Polytechnic Institute and State University. This required the design and implementation of a distributed, networked, and synchronized data acquisition system along with supporting hardware and software capable of measuring 227 accelerometers placed in 129 locations throughout the building. This system will provide a platform for research into a variety of topics, including structural health monitoring, building dynamics, human motion, and energy harvesting. Additionally, the system will be incorporated into the education curriculum by providing real-world data and hardware for students to interact with. This thesis covers the contributions of the author to the project.
Design and Optimization of a Mobile Hybrid Electric System to Reduce Fuel Consumption
Del Barga, Christopher (Virginia Tech, 2015-05-29)
The high costs and high risks of transporting fuel to combat zones make fuel conservation a dire need for the US military. A towable hybrid electric system can help relieve these issues by replacing less fuel efficient standalone diesel generators to deliver power to company encampments. Currently, standalone generators are sized to meet peak demand, even though peak demand only occurs during short intervals each day. The average daily demand is much less, meaning generators will be running inefficiently most of the day. In this thesis, a simulation is created to help determine an optimal system design given a load profile, size and weight constraints, and relocation schedule. This simulation is validated using test data from an existing system. After validation, many hybrid energy components are considered for use in the simulation. The combination of components that yields the lowest fuel consumption is used for the optimal design of the system. After determining the optimal design, a few design parameters are varied to analyze their effect on fuel consumption. The model presented in this thesis agrees with the test data to 7% of the measured fuel consumption. Sixteen system configurations are run through the simulation and their results are compared. The most fuel efficient system is the system that uses a 3.8kW diesel engine generator with a 307.2V, maximum capacity LiFeMgPO? battery pack. This system is estimated to consume 21% less fuel than a stand-alone generator, and up to 28% less when solar power is available.
Design and Testing of a Hydrogel-Based Droplet Interface Lipid Bilayer Array System
Edgerton, Alexander James (Virginia Tech, 2015-10-12)
The research presented in this thesis includes the development of designs, materials, and fabrication processes and the results of characterization experiments for a meso-scale hydrogel-based lipid bilayer array system. Two design concepts are investigated as methods for forming Droplet Interface Bilayer (DIB) arrays. Both concepts use a base of patterned silver with Ag/AgCl electrodes patterned onto a flat polymer substrate. In one technique, photopolymerizable hydrogel is cured through a mask to form an array of individual hydrogels on top of the patterned electrodes. The other technique introduces a second type of polymer substrate that physically supports an array of hydrogels using a set of microchannels. This second substrate is fitted onto the first to contact the hydrogels to the electrodes. The hydrogels are used to support and shape droplets of water containing phospholipids, which self-assemble at the surface of the droplet when submerged in oil. Two opposing substrates can then be pushed together, and a bilayer will form at the point where each pair of monolayers come into contact. The photopatterning technique is used to produce small arrays of hydrogels on top of a simple electrode pattern. Systems utilizing the microchannel substrate are used to create mesoscale hydrogel arrays of up to 3x3 that maintained a low resistance (~50-150 kΩ) connection to the substrate. Up to three bilayers are formed simultaneously and verified through visual observation and by recording the current response behavior. Arrays of varying sizes and dimensions and with different electrode patterns can be produced quickly and inexpensively using basic laboratory techniques. The designs and fabrication processes for both types of arrays are created with an eye toward future development of similar systems at the microscale.

Browsing by Author "Tarazaga, Pablo Alberto"

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