Matching Items (30)
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- All Subjects: engineering
- All Subjects: Finite Element Analysis
- Creators: Liu, Yongming
- Member of: Theses and Dissertations
- Status: Published
Description
Dwindling energy resources and associated environmental costs have resulted in a serious need to design and construct energy efficient buildings. One of the strategies to develop energy efficient structural materials is through the incorporation of phase change materials (PCM) in the host matrix. This research work presents details of a finite element-based framework that is used to study the thermal performance of structural precast concrete wall elements with and without a layer of phase change material. The simulation platform developed can be implemented for a wide variety of input parameters. In this study, two different locations in the continental United States, representing different ambient temperature conditions (corresponding to hot, cold and typical days of the year) are studied. Two different types of concrete - normal weight and lightweight, different PCM types, gypsum wallboard's with varying PCM percentages and different PCM layer thicknesses are also considered with an aim of understanding the energy flow across the wall member. Effect of changing PCM location and prolonged thermal loading are also studied. The temperature of the inside face of the wall and energy flow through the inside face of the wall, which determines the indoor HVAC energy consumption are used as the defining parameters. An ad-hoc optimization scheme is also implemented where the PCM thickness is fixed but its location and properties are varied. Numerical results show that energy savings are possible with small changes in baseline values, facilitating appropriate material design for desired characteristics.
ContributorsHembade, Lavannya Babanrao (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alkali-activated aluminosilicates, commonly known as "geopolymers", are being increasingly studied as a potential replacement for Portland cement. These binders use an alkaline activator, typically alkali silicates, alkali hydroxides or a combination of both along with a silica-and-alumina rich material, such as fly ash or slag, to form a final product with properties comparable to or better than those of ordinary Portland cement. The kinetics of alkali activation is highly dependent on the chemical composition of the binder material and the activator concentration. The influence of binder composition (slag, fly ash or both), different levels of alkalinity, expressed using the ratios of Na2O-to-binders (n) and activator SiO2-to-Na2O ratios (Ms), on the early age behavior in sodium silicate solution (waterglass) activated fly ash-slag blended systems is discussed in this thesis. Optimal binder composition and the n values are selected based on the setting times. Higher activator alkalinity (n value) is required when the amount of slag in the fly ash-slag blended mixtures is reduced. Isothermal calorimetry is performed to evaluate the early age hydration process and to understand the reaction kinetics of the alkali activated systems. The differences in the calorimetric signatures between waterglass activated slag and fly ash-slag blends facilitate an understanding of the impact of the binder composition on the reaction rates. Kinetic modeling is used to quantify the differences in reaction kinetics using the Exponential as well as the Knudsen method. The influence of temperature on the reaction kinetics of activated slag and fly ash-slag blends based on the hydration parameters are discussed. Very high compressive strengths can be obtained both at early ages as well as later ages (more than 70 MPa) with waterglass activated slag mortars. Compressive strength decreases with the increase in the fly ash content. A qualitative evidence of leaching is presented through the electrical conductivity changes in the saturating solution. The impact of leaching and the strength loss is found to be generally higher for the mixtures made using a higher activator Ms and a higher n value. Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) is used to obtain information about the reaction products.
ContributorsChithiraputhiran, Sundara Raman (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniyam D (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012
Description
The alkali activation of aluminosilicate materials as binder systems derived from industrial byproducts have been extensively studied due to the advantages they offer in terms enhanced material properties, while increasing sustainability by the reuse of industrial waste and byproducts and reducing the adverse impacts of OPC production. Fly ash and ground granulated blast furnace slag are commonly used for their content of soluble silica and aluminate species that can undergo dissolution, polymerization with the alkali, condensation on particle surfaces and solidification. The following topics are the focus of this thesis: (i) the use of microwave assisted thermal processing, in addition to heat-curing as a means of alkali activation and (ii) the relative effects of alkali cations (K or Na) in the activator (powder activators) on the mechanical properties and chemical structure of these systems. Unsuitable curing conditions instigate carbonation, which in turn lowers the pH of the system causing significant reductions in the rate of fly ash activation and mechanical strength development. This study explores the effects of sealing the samples during the curing process, which effectively traps the free water in the system, and allows for increased aluminosilicate activation. The use of microwave-curing in lieu of thermal-curing is also studied in order to reduce energy consumption and for its ability to provide fast volumetric heating. Potassium-based powder activators dry blended into the slag binder system is shown to be effective in obtaining very high compressive strengths under moist curing conditions (greater than 70 MPa), whereas sodium-based powder activation is much weaker (around 25 MPa). Compressive strength decreases when fly ash is introduced into the system. Isothermal calorimetry is used to evaluate the early hydration process, and to understand the reaction kinetics of the alkali powder activated systems. A qualitative evidence of the alkali-hydroxide concentration of the paste pore solution through the use of electrical conductivity measurements is also presented, with the results indicating the ion concentration of alkali is more prevalent in the pore solution of potassium-based systems. The use of advanced spectroscopic and thermal analysis techniques to distinguish the influence of studied parameters is also discussed.
ContributorsChowdhury, Ussala (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramanium D. (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that are performed to obtain probability of failure and reliability of structures. Next, decoupled RBDO procedure is proposed with a new reliability analysis formulation with sensitivity analysis, which is performed to remove the highly reliable constraints in the RBDO, thereby reducing the computational time and function evaluations. Followed by implementation of the reliability analysis concepts and RBDO in finite element 2D truss problems and a planar beam problem are presented and discussed.
ContributorsDeivanayagam, Arumugam (Author) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2012
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
Ultrasound has become one of the most popular non-destructive characterization tools for soft materials. Compared to conventional ultrasound imaging, quantitative ultrasound has the potential of analyzing detailed microstructural variation through spectral analysis. Because of having a better axial and lateral resolution, and high attenuation coefficient, quantitative high-frequency ultrasound analysis (HFUA) is a very effective tool for small-scale penetration depth application. One of the QUS parameters, peak density had recently shown a promising response with the variation in the soft material microstructure. Acoustic scattering is arguably the most important factor behind different parametric responses in ultrasound spectra. Therefore, to evaluate peak density, acoustic scattering at different frequency levels was investigated. Analytical, computational, and experimental analysis was conducted to observe both single and multiple scattering in different microstructural setups. It was observed that peak density was an effective tool to express different levels of acoustic scattering that occurred through microstructural variation. The feasibility of the peak density parameter was further evaluated in ultrasound C-scan imaging. The study was also extended to detect the relative position of the imaged structure in the direction of wave propagation. For this purpose, a derivative parameter of peak density named mean peak to valley distance (MPVD) was developed to address the limitations of peak density. The study was then focused on detecting soft tissue malignancy. The histology-based computational study of HFUA was conducted to detect various breast tumor (soft tissue) grades. It was observed that both peak density and MPVD parameters could identify tumor grades at a certain level. Finally, the study was focused on evaluating the feasibility of ultrasound parameters to detect asymptotic breast carcinoma i.e., ductal carcinoma in situ (DCIS) in the surgical margin of the breast tumor. In that computational study, breast pathologies were modeled by including all the phases of DCIS. From the similar analysis mentioned above, it was understood that both peak density and MPVD parameters could detect various breast pathologies like ductal hyperplasia, DCIS, and calcification during intraoperative margin analysis. Furthermore, the spectral features of the frequency spectrums from various pathologies also provided significant information to identify them conclusively.
ContributorsPaul, Koushik (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Holloway, Julianne (Committee member) / Li, Xiangjia (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2022
Description
High-temperature mechanical behaviors of metal alloys and underlying microstructural variations responsible for such behaviors are essential areas of interest for many industries, particularly for applications such as jet engines. Anisotropic grain structures, change of preferred grain orientation, and other transformations of grains occur both during metal powder bed fusion additive manufacturing processes, due to variation of thermal gradient and cooling rates, and afterward during different thermomechanical loads, which parts experience in their specific applications, could also impact its mechanical properties both at room and high temperatures. In this study, an in-depth analysis of how different microstructural features, such as crystallographic texture, grain size, grain boundary misorientation angles, and inherent defects, as byproducts of electron beam powder bed fusion (EB-PBF) AM process, impact its anisotropic mechanical behaviors and softening behaviors due to interacting mechanisms. Mechanical testing is conducted for EB-PBF Ti6Al4V parts made at different build orientations up to 600°C temperature. Microstructural analysis using electron backscattered diffraction (EBSD) is conducted on samples before and after mechanical testing to understand the interacting impact that temperature and mechanical load have on the activation of certain mechanisms. The vertical samples showed larger grain sizes, with an average of 6.6 µm, a lower average misorientation angle, and subsequently lower strength values than the other two horizontal samples. Among the three strong preferred grain orientations of the α phases, <1 1 2 ̅ 1> and <1 1 2 ̅ 0> were dominant in horizontally built samples, whereas the <0 0 0 1> was dominant in vertically built samples. Thus, strong microstructural variation, as observed among different EB-PBF Ti6Al4V samples, mainly resulted in anisotropic behaviors. Furthermore, alpha grain showed a significant increase in average grain size for all samples with the increasing test temperature, especially from 400°C to 600°C, indicating grain growth and coarsening as potential softening mechanisms along with temperature-induced possible dislocation motion. The severity of internal and external defects on fatigue strength has been evaluated non-destructively using quantitative methods, i.e., Murakami’s square root of area parameter model and Basquin’s model, and the external surface defects were rendered to be more critical as potential crack initiation sites.
ContributorsMian, Md Jamal (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Shuaib, Abdelrahman (Committee member) / Mobasher, Barzin (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2022
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
Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023