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STEEL STRUCTURE RESPONSE UNDER FIRE CONDITIONS MODEL BASED SIMULATION (MBS)
Title
STEEL STRUCTURE RESPONSE UNDER FIRE CONDITIONS MODEL BASED SIMULATION (MBS)
Creator
Coughlin, Kevin James
Date
2019
Description
This paper addresses the issue of structure design and analysis for conditions of fire loading. It includes an introductory section that...
Show moreThis paper addresses the issue of structure design and analysis for conditions of fire loading. It includes an introductory section that presents the historical and current state of practice using prescriptive methods of design, a qualitative and conceptual development (based on actual field observations) of what is expected to occur in a structure when subjected to fire, and a summary of the current state of research on the subject of structure design for fire loading. Next, a thermo-plastic non-linear finite element shell model was developed for a two member steel beam and column, bolted joint structure used in an actual physical fire test, subjected to beam a bending load and column compressive load, held constant, while the structure was heated up in a furnace. The beam / column bolted joint rotation for the test matched the simulation quite well. Next, further extending this modeling approach, a partial moment frame from the center of a 9 story building designed for dead, live, and seismic loading was modeled with non-linear thermo-plastic shell elements in the fire zone, along with linear elastic beam / line elements for structural components surrounding the fire zone. For this model, the gravity loading (no seismic loading included) was fully applied, and then a thermal load corresponding to the ASTM E119 fire test load was applied to the structure in the fire zone. Simulation of lateral torsional buckling, flange local buckling, web local buckling, and finally overall global buckling of the columns was accomplished in this effort, increasing confidence that complex thermo-plastic structural behavior can be modeled with advanced non-linear finite element technology. Boundary conditions on this model from the floor system had a significant impact on the mode of global buckling (strong axis or weak axis), warranting further investigation and possibly a 3-D frame with a floor system included in future work. Also, extending this modeling approach even further, in future work, using the entire 9 story moment frame, with shell elements in the fire zone and non-linear moment-curvature beam / line elements for surrounding members, is contemplated, the objective being to numerically model a progressive collapse event in a planar frame. Finally, an actual 10 story structure, converted to and industrial open floor structure, based on current design codes and standards, was modeled thermally using the industry standard Hydrocarbon (HC) Temperature vs time curve, and structurally using non-linear thermoplastic shell elements in the “fire room” (to better capture local buckling and overall structure collapse behavior), and thermoplastic beam elements for the rest of the structure. The thermal modeling was performed for steel members both without insulation (bare steel) and with minimal insulation (1/4” coated thickness), and these “decoupled” results then applied to the structural model. The use of even a small layer of insulation demonstrated the dramatic effect of such, insofar as the collapse time of the structure is concerned.
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EVALUATING INTEGRITY FOR MOBILE ROBOT LOCALIZATION SAFETY
Title
EVALUATING INTEGRITY FOR MOBILE ROBOT LOCALIZATION SAFETY
Creator
Duenas Arana, Guillermo
Date
2019
Description
Precise localization is paramount for autonomous navigation. Localization errors are not only dangerous by themselves, but can also mislead...
Show morePrecise localization is paramount for autonomous navigation. Localization errors are not only dangerous by themselves, but can also mislead other dependent systems into moving to a hazardous location. Unfortunately, the problem of quantifying robot localization safety is only sparsely addressed in the robotics literature, and most robotics algorithms still quantify pose estimation performance using a covariance matrix or particle spread, which only accounts for nominal sensor errors. This is insufficient for life- and mission-critical applications, such as autonomous vehicles and other co-robots, where ignoring sensor or sensor or processing faults can lead to catastrophic localization errors. Thus, other methods must be employed to ensure safety.In response, this research leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in mobile robots. In contrast to aviation applications, that heavily rely on the Global Navigation Satellite System (GNSS) for localization, robots often operate in complex, GNSS-denied environments that require a more sophisticated sensor suite to ensure localization safety. Localization integrity risk is the probability that a robot's pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this work, the integrity risk is rigorously upper bounded by accounting for both nominal noise and other non-nominal sensor faults, resulting in a safe upper bound on the localization integrity risk.The main contribution of this dissertation is the design and evaluation of a sequential integrity monitoring methodology applicable to mobile robot localization algorithms that use feature extraction and data association. First, faults introduced during the feature extraction and data association processes are distinguished, and the probability of the latter is rigorously upper bounded using analytical methods. The impact of faults in the estimate error's and fault detector's distributions is then determined to quantify integrity risk, which is evaluated under the worst-possible fault combination. To determine the impact of previous faults without a boundlessly growing number of fault hypotheses, this dissertation presents a novel method that uses a preceding time window to build a limited set of hypotheses and a prior estimate bias to account for faults occurring before the start of the time window. The proposed methodology is applicable to Kalman Filter and fixed-lag smoothing localization. Simulated and experimental results are presented to validate the methodology.
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Capillary Rise of Common Liquids and Nanofluids: Experiments and Modeling
Title
Capillary Rise of Common Liquids and Nanofluids: Experiments and Modeling
Creator
Wu, Pingkeng
Date
2018
Description
Capillary dynamics of common liquids and nanofluids is a ubiquitous everyday phenomenon. It has practical applications in diverse fields,...
Show moreCapillary dynamics of common liquids and nanofluids is a ubiquitous everyday phenomenon. It has practical applications in diverse fields, including ink-jet printing, lab-on-a-chip, biotechnology, and coating. Important as it is, this phenomenon has not been fully understood and requires tremendous effort in theoretical analysis and experimental investigations to gain further knowledge and guide the design of practical precesses whenever capillarity is essential.The rise of the main meniscus in rectangular capillaries is important in interpreting the phenomenon of fluid flow in porous media. This thesis presents an experimental study on the rise of the main meniscus in rectangular borosilicate glass and plastic (polystyrene) capillaries using three different liquids (water, ethanol, and hexadecane). A universal model (an extended two-wall model) based on the Laplace equation was developed to predict the equilibrium height of the main meniscus in rectangular capillaries. In capillary dynamics, it is crucial to understand the interaction between fluid molecules and a solid substrate (the wall) in molecular scale. Recent studies reveal that a layered molecularly thin wetting film (LMTWF) will develop ahead of the apparent three-phase contact line for the spreading of a wetting liquid on solid surfaces. Based on this fact, a novel molecular self-layering model is proposed to explain the dynamic wetting considering the role of the molecular shape on self-layering and its effect on the molecularly thin film viscosity in regards to the advancing (dynamic) contact angle. The proposed molecular self-layering model is then incorporated into the Lucas-Washburn-Rideal (LWR) equation to explain the capillary rise dynamics of fluids of spherical, cylindrical, and disk shape molecules in borosilicate glass capillaries. The abilities of the other popular dynamic contact angle models to correct the dynamic contact angle effect in the capillary rise process were also investigated. The LWR equation modified by molecular self-layering model predicts well the capillary rise of carbon tetrachloride, octamethylcyclotetrasiloxane and n-alkanes with the molecular diameter or measured solvation force data. The molecular self-layering model modified LWR equation also has good predictions on the capillary rise of silicone oils covering a wide range of bulk viscosities with the same key parameter W(0), which results from the molecular self-layering. Besides the open capillaries, the proposed molecular self-layering model is applied to explain the spontaneous rise of Newtonian liquids in closed-end capillaries. Contribution of the compressed air inside the closed capillaries is also modeled and experimentally verified. Finally, the research is extended to a liquid phase displacing another immiscible liquid in capillaries with the focus on surfactant solutions containing polymeric nanoparticles (nanofluids), which have been shown to have an improved wetting and spreading on solid surfaces. The polymeric nanoparticles can reduce the frictional coefficient by as much as four times by forming structured layers in the confined wedge film. The role of the interfacial tension on the frictional coefficient is also demonstrated.In summary, this thesis presents the physics of liquid rise in rectangular capillaries, effect of molecular self-layering in capillary dynamics in open and closed-end capillaries, and the contribution of nanofluids in the two-phase displacement dynamics.
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Developing Adaptive and Predictive Modules for the Second Generation of Multivariable Insulin Delivery System for People with Type-1 Diabetes
Title
Developing Adaptive and Predictive Modules for the Second Generation of Multivariable Insulin Delivery System for People with Type-1 Diabetes
Creator
Askari, Mohammad Reza
Date
2023
Description
In this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1...
Show moreIn this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1 diabetes (T1D). AID system is improved by integrating missing data from sensors into the system, reconciling outliers in the data, and eliminating the effects of artifacts in signals from wearable devices. Behavioral patterns of individuals with T1D are captured by data-driven models. The model predictive control algorithm of the mvAID uses these patterns for making decisions and predicting glucose concentrations in the future more accurately. A pipeline algorithm is developed for removing noise and motion artifacts from wristband signals. Then, energy expenditure, physical activity, and acute psychological stress (APS) are estimated from wearable device signals to detect and quantify disturbances affecting the concentration of blood glucose concentration. Additionally, different modules were designed for predicting risky glycemic episodes and are used to build the second generation of the mvAID system. The techniques developed are tested with historical data sets from various clinical experiments and free-living data, and with simulations made by using our multivariable glucose, insulin and physiological variables simulator (mGIPsim).
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Melt Growth of Indium-Iodide on Earth and in Microgravity
Title
Melt Growth of Indium-Iodide on Earth and in Microgravity
Creator
Riabov, Vladimir
Date
2023
Description
Indium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray...
Show moreIndium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray detectors. Its physical properties are similar to those of other materials used as room temperature radiation detectors. Over the years the technology of purification and crystal growth of InI was developed. Significant advances were made to improve purity, crystal structure and resulting electronic properties of the material. Nevertheless, the desired detector performance has not been achieved yet. Stress-induced crystal lattice defects resulting from solidification in contact with crucible are suspected to be responsible for the limited performance. Microgravity environment was previously used to study its effects on the process of crystal growth from the melt applied to semiconductors. It was observed that unlike on Earth materials can solidify without contact with the wall, when the sample is confined by the crucible. It was also shown that such detached solidification can drastically reduce stress-induced defects of the crystal lattice and improve electronic properties of the material. In this study crystal growth of InI was studied in microgravity, attempting to achieve detached solidification, and observe it in a transparent zone of a furnace. Partially detached solidification (a large free surface) has occurred in one of the samples. The resulting crystals were characterized by measuring their electronic properties and estimating the radiation detector performance of the devices manufactured using the crystals.
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DEEP LEARNING AND COMPUTER VISION FOR INDUSTRIAL APPLICATIONS: CELLULAR MICROSCOPIC IMAGE ANALYSIS AND ULTRASOUND NONDESTRUCTIVE TESTING
Title
DEEP LEARNING AND COMPUTER VISION FOR INDUSTRIAL APPLICATIONS: CELLULAR MICROSCOPIC IMAGE ANALYSIS AND ULTRASOUND NONDESTRUCTIVE TESTING
Creator
Yuan, Yu
Date
2022
Description
For decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data...
Show moreFor decades, researchers have sought to develop artificial intelligence (AI) systems that can help human beings on decision making, data analysis and pattern recognition applications where analytical methods are ineffective. In recent years, Deep Learning (DL) has been proven to be an effective AI technique that can outperform other methods in applications such as computer vision, natural language processing, autonomous driving. Realizing the potential of deep learning techniques, researchers have also started to apply deep learning on other industrial applications. Today, deep learning based models are used to innovate and accelerate automation, guidance, and decision making in various industries including automotive industry, pharmaceutical industry, finance, agriculture and more. In this research, several important industrial applications (on Biomedicine and Non-Destructive Testing) utilizing deep learning algorithms will be introduced and analyzed. The first biopharmaceutical application focuses on developing a deep learning based model to automate the visual inspection process in Median Tissue Culture Infectious Dose(TCID50). TCID50 is one of the most popular methods for viral quantification. An important step of TCID50 is to visually inspect the sample and decide if it exhibits cytopathic effect(CPE) or not. Two novel models have been developed to detect CPE in microscopic images of cell culture in 96 well-plates. The first model consists of a convolutional neural network (CNN) and support vector machine(SVM). The second model is a fully convolutional network (FCN) followed by morphological post-processing steps. The models are tested on 4 cell lines and achieve very high accuracy. Another biopharmaceutical application developed for cellular microscopic images is the clonal selection. Clonal selection is one of the mandatory steps in cell line development process. It focuses on verifying the clonality of the cell culture. The researchers used to visually inspect the microscopic images to verify the clonality. In this work, a novel deep learning based model and a workflow is developed to accelerate the process. This algorithm consists of multiple steps, including image analysis after incubation to detect the cell colonies, and verify its clonality in day0 image. The results and common mis-classification cases are shown in this thesis. Image analysis method is not the only technology that has been advancing for cellular image analysis in biopharmaceutical industry. A new class of instruments are currently used in biopharmaceutical industry which enable more opportunities for image analysis. To make the most of these new instruments, a convolutional neural network based architecture is used to perform accurate cell counting and cell morphology based segmentation. This analysis can provide more insight of the cells at very early stage in characterization process of cell line development. The architecture and the testing results are presented in this work. The proposed algorithm has achieved very high accuracy on both applications, and the cell morphology based segmentation enables a brand new feature for scientists to predict the potential productivity of the cells. Next part of this dissertation is focused on hardware implementation of Ultrasonic Non-Destructive Testing (NDT) methods based on deep learning, which can be highly useful in flaw detection and classification applications. With the help of a smart and mobile Non-Destructive Testing device, engineers can accurately detect and locate the flaws inside the materials without reliance on high performance computation resources. The first NDT application presents a hardware implementation of a deep learning algorithm on Field-programmable gate array(FPGA) for Ultrasound flaw detection. The Ultrasound flaw detection algorithm consists of a wavelet transform followed by a LeNet inspired convolutional neural network called Ultra-LeNet. This work is focused on implementing the computationally difficult part of this algorithm: Ultra-LeNet, so that it can be used in the field where high performance computation resources (e.g., AWS) are not accessible. The implementation uses resource partitioning to design two dedicated pipelined accelerators for convolutional layers and fully connected layers respectively. Both accelerators utilize loop unrolling, loop pipelining and batch processing techniques to maximize the throughput. The comparison to other work has shown that the implementation has achieved higher hardware utilization efficiency. The second NDT application is also focused on implementing a deep learning based algorithm for Ultrasound flaw detection on a FPGA. Instead of implementing the Ultra-LeNet, the deep learning model used in this application is Meta-learning based Siamese Network, which is capable for multi-class classification and it can also classify a new class even if it does not appear in the training dataset with the help of automated learning features. The hardware implementation is significantly different than the previous algorithm. In order to improve the inference operation efficiency, the model is compressed with both pruning and quantization, and the FPGA implementation is specifically designed to accelerate the compressed CNN with high efficiency. The CNN model compression method and hardware design are novel methods introduced in this work. Comparison against other compressed CNN accelerators is also presented.
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