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- All Subjects: Mechanics
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- Creators: Liu, Yongming
- Member of: Theses and Dissertations
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- Status: Published
Description
Microbial fuel cells (MFCs) promote the sustainable conversion of organic matter in black water to electrical current, enabling the production of hydrogen peroxide (H2O2) while making waste water treatment energy neutral or positive. H2O2 is useful in remote locations such as U.S. military forward operating bases (FOBs) for on-site tertiary water treatment or as a medical disinfectant, among many other uses. Various carbon-based catalysts and binders for use at the cathode of a an MFC for H2O2 production are explored using linear sweep voltammetry (LSV) and rotating ring-disk electrode (RRDE) techniques. The oxygen reduction reaction (ORR) at the cathode has slow kinetics at conditions present in the MFC, making it important to find a catalyst type and loading which promote a 2e- (rather than 4e-) reaction to maximize H2O2 formation. Using LSV methods, I compared the cathodic overpotentials associated with graphite and Vulcan carbon catalysts as well as Nafion and AS-4 binders. Vulcan carbon catalyst with Nafion binder produced the lowest overpotentials of any binder/catalyst combinations. Additionally, I determined that pH control may be required at the cathode due to large potential losses caused by hydroxide (OH-) concentration gradients. Furthermore, RRDE tests indicate that Vulcan carbon catalyst with a Nafion binder has a higher H2O2 production efficiency at lower catalyst loadings, but the trade-off is a greater potential loss due to higher activation energy. Therefore, an intermediate catalyst loading of 0.5 mg/cm2 Vulcan carbon with Nafion binder is recommended for the final MFC design. The chosen catalyst, binder, and loading will maximize H2O2 production, optimize MFC performance, and minimize the need for additional energy input into the system.
ContributorsStadie, Mikaela Johanna (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
In our modern world the source of for many chemicals is to acquire and refine oil. This process is becoming an expensive to the environment and to human health. Alternative processes for acquiring the final product have been developed but still need work. One product that is valuable is butanol. The normal process for butanol production is very intensive but there is a method to produce butanol from bacteria. This process is better because it is more environmentally safe than using oil. One problem however is that when the bacteria produce too much butanol it reaches the toxicity limit and stops the production of butanol. In order to keep butanol from reaching the toxicity limit an adsorbent is used to remove the butanol without harming the bacteria. The adsorbent is a mesoporous carbon powder that allows the butanol to be adsorbed on it. This thesis explores different designs for a magnetic separation process to extract the carbon powder from the culture.
ContributorsChabra, Rohin (Author) / Nielsen, David (Thesis director) / Torres, Cesar (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
Optimizing cathodes for microbial fuel cells is important to maximize energy harvested from wastewater. Cathodes were made by modifying a recipe from previous literature and testing the current of the cathode using linear sweep voltammetry. The cathodes contained an Fe-N-C catalyst combined with a Polytetrafluoroethylene binder. Optimizing the power resulting from the microbial fuel cells will help MFCs be an alternative energy source to fossil fuels. The new cathodes did improve in current production from −16 𝐴/𝑚 to −37 𝐴/𝑚 at -0.4 V. When fitted using a Butler-Volmer model, the cathode linear-sweep voltammograms did not follow the expected exponential trend. These results show a need for more research on the cathodes and the Butler-Volmer model, and they also show that the cathode is ready for further and longer application in a microbial fuel cell.
ContributorsRussell, Andrea Christine (Author) / Torres, Cesar (Thesis director) / Young, Michelle (Committee member) / School of Sustainable Engineering & Built Envirnmt (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Alternative ion exchange membranes for implementation in a peroxide production microbial electrochemical cel (PP-MEC) are explored through membrane stability tests with NaCl electrolyte and stabilizer EDTA at varying operational pHs. PP-MEC performance parameters \u2014 H2O2 concentration, current density, coulombic efficiency and power input required \u2014 are optimized over a 7 month continuous operation period based on their response to changes in HRT, EDTA concentration, air flow rate and electrolyte. I found that EDTA was compatible for use with the membranes. I also determined that AMI membranes were preferable to CMI and FAA because it was consistently stable and maintained its structural integrity. Still, I suggest testing more membranes because the AMI degraded in continuous operation. The PP-MEC produced up to 0.38 wt% H2O2, enough to perform water treatment through the Fenton process and significantly greater than the 0.13 wt% batch PP-MEC tests by previous researchers. It ran at > 0.20 W-hr/g H2O2 power input, ~ three orders of magnitude less than what is required for the anthraquinone process. I recommend high HRT and EDTA concentration while running the PP- MEC to increase H2O2 concentration, but low HRT and low EDTA concentration to decrease power input required. I recommend NaCl electrolyte but suggest testing new electrolytes that may control pH without degrading H2O2. I determined that air flow rate has no effect on PP-MEC operation. These recommendations should optimize PP-MEC operation based on its application.
ContributorsChowdhury, Nadratun Naeem (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Microbial fuel cells (MFCs) facilitate the conversion of organic matter to electrical current to make the total energy in black water treatment neutral or positive and produce hydrogen peroxide to assist the reuse of gray water. This research focuses on wastewater treatment at the U.S. military forward operating bases (FOBs). FOBs experience significant challenges with their wastewater treatment due to their isolation and dangers in transporting waste water and fresh water to and from the bases. Even though it is theoretically favorable to produce power in a MFC while treating black water, producing H2O2 is more useful and practical because it is a powerful cleaning agent that can reduce odor, disinfect, and aid in the treatment of gray water. Various acid forms of buffers were tested in the anode and cathode chamber to determine if the pH would lower in the cathode chamber while maintaining H2O2 efficiency, as well as to determine ion diffusion from the anode to the cathode via the membrane. For the catholyte experiments, phosphate and bicarbonate were tested as buffers while sodium chloride was the control. These experiments determined that the two buffers did not lower the pH. It was seen that the phosphate buffer reduced the H2O2 efficiency significantly while still staying at a high pH, while the bicarbonate buffer had the same efficiency as the NaCl control. For the anolyte experiments, it was shown that there was no diffusion of the buffers or MFC media across the membrane that would cause a decrease in the H2O2 production efficiency.
ContributorsThompson, Julia (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
The study aims to develop and evaluate failure prediction models that accurately predict crack initiation sites, fatigue life in additively manufactured Ti-6Al-4V, and burst pressure in relevant applications.The first part proposes a classification model to identify crack initiation sites in AM-built Ti-6Al-4V alloy. The model utilizes surface and pore-related parameters and achieves high accuracy (0.97) and robustness (F1 score of 0.98). Leveraging CT images for characterization and data extraction from the CT-images built STL files, the model effectively detects crack initiation sites while minimizing false positives and negatives. Data augmentation techniques, including SMOTE+Tomek Links, are employed to address imbalanced data distributions and improve model performance.
This study proposes the Probabilistic Physics-guided Neural Network 2.0 (PPgNN) for probabilistic fatigue life estimation. The presented approach overcomes the limitations of classical regression machine models commonly used to analyze fatigue data. One key advantage of the proposed method is incorporating known physics constraints, resulting in accurate and physically consistent predictions.
The efficacy of the model is demonstrated by training the model with multiple fatigue S-N curve data sets from open literature with relevant morphological data and tested using the data extracted from CT-built STL files. The results illustrate that PPgNN 2.0 is a flexible and robust model for predicting fatigue life and quantifying uncertainties by estimating the mean and standard deviation of the fatigue life. The loss function that trains the proposed model can capture the underlying distribution and reduce the prediction error.
A comparison study between the performance of neural network models highlights the benefits of physics-guided learning for fatigue data analysis. The proposed model demonstrates satisfactory learning capacity and generalization, providing accurate fatigue life predictions to unseen examples.
An elastic-plastic Finite Element Model (FEM) is developed in the second part to assess pipeline integrity, focusing on burst pressure estimation in high-pressure gas pipelines with interactive corrosion defects. The FEM accurately predicts burst pressure and evaluates the remaining useful life by considering the interaction between corrosion defects and neighboring pits. The FEM outperforms the well-known ASME-B31G method in handling interactive corrosion threats.
ContributorsBalamurugan, Rakesh (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Bhate, Dhruv (Committee member) / Arizona State University (Publisher)
Created2023
Description
Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst devices, magnetic shielding, etc. For the engineering of the cellular foam architectures, closed-form models that can be used to predict the mechanical and thermal properties of foams are highly desired especially for the recently developed ultralight weight shellular architectures. Herein, for the first time, a novel packing three-dimensional (3D) hollow pentagonal dodecahedron (HPD) model is proposed to simulate the cellular architecture with hollow struts. An electrochemical deposition process is utilized to manufacture the metallic hollow foam architecture. Mechanical and thermal testing of the as-manufactured foams are carried out to compare with the HPD model. Timoshenko beam theory is utilized to verify and explain the derived power coefficient relation. Our HPD model is proved to accurately capture both the topology and the physical properties of hollow stochastic foam. Understanding how the novel HPD model packing helps break the conventional impression that 3D pentagonal topology cannot fulfill the space as a representative volume element. Moreover, the developed HPD model can predict the mechanical and thermal properties of the manufactured hollow metallic foams and elucidating of how the inevitable manufacturing defects affect the physical properties of the hollow metallic foams. Despite of the macro-scale stochastic foam architecture, nano gradient gyroid lattices are studied using Molecular Dynamics (MD) simulation. The simulation result reveals that, unlike homogeneous architecture, gradient gyroid not only shows novel layer-by-layer deformation behavior, but also processes significantly better energy absorption ability. The deformation behavior and energy absorption are predictable and designable, which demonstrate its highly programmable potential.
ContributorsDai, Rui (Author) / Nian, Qiong (Thesis advisor) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Liu, Yongming (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2021
Description
Advanced Polymer and Ceramic Matrix Composites (PMCs and CMCs) are currently employed in a variety of airframe and engine applications. This includes PMC jet engine fan cases and CMC hot gas path turbine components. In an impact event, such as a jet engine fan blade-out, PMCs exhibit significant deformation-induced temperature rises in addition to strain rate, temperature, and pressure dependence. CMC turbine components experience elevated temperatures, large thermal gradients, and sustained loading for long time periods in service, where creep is a major issue. However, the complex nature of woven and braided composites presents significant challenges for deformation, progressive damage, and failure prediction, particularly under extreme service conditions where global response is heavily driven by competing time and temperature dependent phenomena at the constituent level. In service, the constituents in these advanced composites experience history-dependent inelastic deformation, progressive damage, and failure, which drive global nonlinear constitutive behavior. In the case of PMCs, deformation-induced heating under impact conditions is heavily influenced by the matrix. The creep behavior of CMCs is a complex manifestation of time-dependent load transfer due to the differing creep rates of the constituents; simultaneous creep and relaxation at the constituent level govern macroscopic CMC creep. The disparity in length scales associated with the constituent materials, woven and braided tow architectures, and composite structural components therefore necessitates the development of robust multiscale computational tools. In this work, multiscale computational tools are developed to gain insight into the deformation, progressive damage, and failure of advanced PMCs and CMCs. This includes multiscale modeling of the impact response of PMCs, including adiabatic heating due to the conversion of plastic work to heat at the constituent level, as well as elevated temperature creep in CMCs as a result of time-dependent constituent load transfer. It is expected that the developed models and methods will provide valuable insight into the challenges associated with the design and certification of these advanced material systems.
ContributorsSorini, Christopher (Author) / Chattopadhyay, Adit (Thesis advisor) / Goldberg, Robert K (Committee member) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Yekani-Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2021
Description
Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities.
In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing.
The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.
ContributorsLee, Hyunseong (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Fard, Masoud Yekani (Committee member) / Tang, Pingbo (Committee member) / Campbell, Angela (Committee member) / Arizona State University (Publisher)
Created2021