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Description
In this dissertation, two types of passive air freshener products from Henkel, the wick-based air freshener and gel-based air freshener, are studied for their wicking mechanisms and evaporation performances.The fibrous pad of the wick-based air freshener is a porous medium that absorbs fragrance by capillary force and releases the fragrance into the ambient air. To investigate the wicking process, a two-dimensional multiphase flow numerical model using COMSOL Multiphysics is built. Saturation and liquid pressure inside the pad are solved. Comparison between the simulation results and experiments shows that evaporation occurs simultaneously with the wicking process. The evaporation performance on the surface of the wicking pad is analyzed based on the kinetic theory, from which the mass flow rate of molecules passing the interface of each pore of the porous medium is obtained. A 3D model coupling the evaporation model and dynamic wicking on the evaporation pad is built to simulate the entire performance of the air freshener to the environment for a long period of time. Diffusion and natural convection effects are included in the simulation. The simulation results match well with the experiments for both the air fresheners placed in a chamber and in the absent of a chamber, the latter of which is subject to indoor airflow.
The gel-based air freshener can be constructed as a porous medium in which the solid network of particles spans the volume of the fragrance liquid. To predict the evaporation performance of the gel, two approaches are tested for gel samples in hemispheric shape. The first approach is the sessile drop model commonly used for the drying process of a pure liquid droplet. It can be used to estimate the weight loss rate and time duration of the evaporation. Another approach is to simulate the concentration profile outside the gel and estimate the evaporation rate from the surface of the gel using the kinetic theory. The evaporation area is updated based on the change of pore size. A 3D simulation using the same analysis is further applied to the cylindrical gel sample. The simulation results match the experimental data well.
ContributorsYuan, Jing (Author) / Chen, Kangping (Thesis advisor) / Herrmann, Marcus (Committee member) / Huang, Huei-Ping (Committee member) / Wang, Liping (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the age of 5th and upcoming 6th generation fighter aircraft one key proponent of these impressive machines is the inclusion of stealth. This inclusion is demonstrated by thoughtful design pertaining to the shape of the aircraft and rigorous material selection. Both criteria aim to minimize the radar cross section of these aircraft over a wide bandwidth of frequencies corresponding to an ever-evolving field of radar technology. Stealth is both an offensive and defensive capability meaning that service men and women depend on this feature to carry out their missions, and to return home safely. The goal of this paper is to introduce a novel method to designing disordered two-phase composites with desired electromagnetic properties. This task is accomplished by employing the spatial point correlation function, specifically at the two-point level. Effective at describing the dispersion of phases within a two-phase system, the two-point correlation function serves as a statistical function that becomes a realizable target for heterogeneous composites. Simulated annealing is exercised to reconstruct two-phase composite microstructures that initially do not match their target function, followed by two separate experiments aimed at studying the impact of the provided inputs on its outcome. Once conditions for reconstructing highly accurate microstructures are identified, modifications are made to the target function to extract and compare dielectric constants associated with each microstructure. Both the real and imaginary components, which respectively affect wave propagation and attenuation, of the dielectric constants are plotted to illustrate their behavior with increasing wavenumber. Conclusions suggest that favorable values of the complex dielectric constant can be reverse-engineered via careful consideration of the two-point correlation function. Subsequently, corresponding microstructures of the composite can be simulated and then produced through 3-D printing for testing and practical applications.
ContributorsPlantz, Alex Chadewick (Author) / Jiao, Yang (Thesis advisor) / Zhuang, Houlong (Committee member) / Yang, Sui (Committee member) / Arizona State University (Publisher)
Created2024
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
Description
High-entropy alloys (HEAs) is a new class of materials which have been studied heavily due to their special mechanical properties. HEAs refers to alloys with multiple equimolar or nearly equimolar elements. HEAs show exceptional and attractive properties currently absent from conventional alloys, which make them the center of intense investigation. HEAs obtain their properties from four core effects that they exhibit and most of the work on them have been dedicated to study their mechanical properties. In contrast, little or no research have gone into studying the functional or even thermal properties of HEAs. Some HEAs have also shown exceptional or very high melting points. According to the definition of HEAs, Si-Ge-Sn alloys with equal or comparable concentrations of the three group IV elements belong to the category of HEAs. Thus, the equimolar components of Si-Ge-Sn alloys probably allow their atomic structures to display the same fundamental effects of metallic HEAs. The experimental fabrication of such alloys has been proven to be very difficult, which is mainly due to differences between the properties of their constituent elements, as indicated from their binary phase diagrams. However, previous computational studies have shown that SiGeSn HEAs have some very interesting properties, such as high electrical conductivity, low thermal conductivity and semiconducting properties. In this work, going for a complete characterization of the SiGeSn HEA properties, the melting point of this alloy is studied using classical molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The aim is to investigate the effects of high Sn content in this alloy on the melting point compared with the traditional SiGe alloys. Classical MD simulations results strongly indicates that none of the available empirical potentials is able to predict accurate or reasonable melting points for SiGeSn HEAs and most of its subsystems. DFT calculations results show that SiGeSn HEA have a melting point which represent the mean value of its constituent elements and that no special deviations are found. This work contributes to the study of SiGeSn HEA properties, which can serve as guidance before the successful experimental fabrication of this alloy.
ContributorsAlqaisi, Ahmad Madhat Odeh (Author) / Hong, Qi-Jun (Thesis advisor) / Zhuang, Houlong (Thesis advisor) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2023
Description
This paper presents a comprehensive review of current advances and challenges in the field of bone tissue engineering. A systematic review of the literature was conducted to identify recent developments in biomaterials, scaffold design, cell sources, and growth factors for bone tissue engineering applications. Based on this review, an experimental proposal is presented for the development of porous composite biomaterials that may enhance bone regeneration, which consist of hybrid amyloid/spidroin fibers combined with a bioactive ceramic matrix. An iterative design process of modeling and simulation, production, and characterization of both the fibers and the composite material is proposed. A modeling and simulation approach is also presented for unidirectional fiber composite biomaterials using 2-point correlation functions, finite element simulations, and machine learning. This approach was demonstrated to enable the efficient and accurate prediction of the effective Young’s modulus of candidate composite biomaterials, which can inform the design of optimized materials for bone tissue engineering applications. The proposed experimental and simulation approaches have the potential to address current challenges and lead to the development of novel composite biomaterials that can augment the current technologies in the field of bone tissue engineering.
ContributorsThornton, Bryce (Author) / Hartwell, Leland (Thesis director) / Jiao, Yang (Committee member) / Susarla, Sandhya (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor)
Created2023-05
Description
Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing for accuracy. Although recently developed surrogate microstructure models employ deep-learning techniques and reconstruction of microstructures from lower-dimensional data, their accuracy is fairly limited as spatio-temporal information is lost in the pursuit of dimensional reduction. Given these limitations, a novel data-driven emulator (DDE) for extrapolation prediction of microstructural evolution is presented, which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. To assess the robustness of DDE, the emulation sequence and the scaling behavior with phase-field simulations for several noisy initial states are compared. In conclusion, the effectiveness of the microstructure emulation technique is explored in the context of accelerating runtime, along with an emphasis on its trade-off with accuracy.Meanwhile, an interpolation DDE has also been tested, which is based on obtaining
a low-dimensional representation of the microstructures via tensor decomposition and subsequently predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once the microstructure predictions are obtained in the low-dimensional space, a hybrid input-output phase retrieval algorithm will be employed to reconstruct the microstructures. As proof of concept, the results on microstructure prediction for spinodal decomposition are presented, although the method itself is agnostic of the material parameters. Results show that GPR-based DDE model are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of 6.78 × 10−7) at time scales half of that employed in obtaining training data. This data-driven
microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity.
Future work will also be discussed and demonstrate the intended utilization of these two approaches for 3D microstructure prediction through their combined application.
ContributorsWu, Peichen (Author) / Ankit, Kumar (Thesis advisor) / Iquebal, Ashif (Committee member) / Jiao, Yang (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2024
Description
Fatigue is a degradation process of materials that would lead to failure when materials are subjected to cyclic loadings. During past centuries, various of approaches have been proposed and utilized to help researchers understand the underlying theories of fatigue behavior of materials, as well as design engineering structures so that catastrophic disasters that arise from fatigue failure could be avoided. The stress-life approach is the most classical way that academia applies to analyze fatigue data, which correlates the fatigue lifetime with stress amplitudes during cyclic loadings. Fracture mechanics approach is another well-established way, by which people regard the cyclic stress intensity factor as the driving force during fatigue crack nucleation and propagation, and numerous models (such as the well-known Paris’ law) are developed by researchers.
The significant drawback of currently widely-used fatigue analysis approaches, nevertheless, is that they are all cycle-based, limiting researchers from digging into sub-cycle regime and acquiring real-time fatigue behavior data. The missing of such data further impedes academia from validating hypotheses that are related to real-time observations of fatigue crack nucleation and growth, thus the existence of various phenomena, such as crack closure, remains controversial.
In this thesis, both classical stress-life approach and fracture-mechanics-based approach are utilized to study the fatigue behavior of alloys. Distinctive material characterization instruments are harnessed to help collect and interpret key data during fatigue crack growth. Specifically, an investigation on the sub-cycle fatigue crack growth behavior is enabled by in-situ SEM mechanical testing, and a non-uniform growth mechanism within one loading cycle is confirmed by direct observation as well as image interpretation. Predictions based on proposed experimental procedure and observations show good match with cycle-based data from references, which indicates the credibility of proposed methodology and model, as well as their capability of being applied to a wide range of materials.
The significant drawback of currently widely-used fatigue analysis approaches, nevertheless, is that they are all cycle-based, limiting researchers from digging into sub-cycle regime and acquiring real-time fatigue behavior data. The missing of such data further impedes academia from validating hypotheses that are related to real-time observations of fatigue crack nucleation and growth, thus the existence of various phenomena, such as crack closure, remains controversial.
In this thesis, both classical stress-life approach and fracture-mechanics-based approach are utilized to study the fatigue behavior of alloys. Distinctive material characterization instruments are harnessed to help collect and interpret key data during fatigue crack growth. Specifically, an investigation on the sub-cycle fatigue crack growth behavior is enabled by in-situ SEM mechanical testing, and a non-uniform growth mechanism within one loading cycle is confirmed by direct observation as well as image interpretation. Predictions based on proposed experimental procedure and observations show good match with cycle-based data from references, which indicates the credibility of proposed methodology and model, as well as their capability of being applied to a wide range of materials.
ContributorsLiu, Siying (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2018
Description
The large-scale anthropogenic emission of carbon dioxide into the atmosphere leads to many unintended consequences, from rising sea levels to ocean acidification. While a clean energy infrastructure is growing, mid-term strategies that are compatible with the current infrastructure should be developed. Carbon capture and storage in fossil-fuel power plants is one way to avoid our current gigaton-scale emission of carbon dioxide into the atmosphere. However, for this to be possible, separation techniques are necessary to remove the nitrogen from air before combustion or from the flue gas after combustion. Metal-organic frameworks (MOFs) are a relatively new class of porous material that show great promise for adsorptive separation processes. Here, potential mechanisms of O2/N2 separation and CO2/N2 separation are explored.
First, a logical categorization of potential adsorptive separation mechanisms in MOFs is outlined by comparing existing data with previously studied materials. Size-selective adsorptive separation is investigated for both gas systems using molecular simulations. A correlation between size-selective equilibrium adsorptive separation capabilities and pore diameter is established in materials with complex pore distributions. A method of generating mobile extra-framework cations which drastically increase adsorptive selectivity toward nitrogen over oxygen via electrostatic interactions is explored through experiments and simulations. Finally, deposition of redox-active ferrocene molecules into systematically generated defects is shown to be an effective method of increasing selectivity towards oxygen.
First, a logical categorization of potential adsorptive separation mechanisms in MOFs is outlined by comparing existing data with previously studied materials. Size-selective adsorptive separation is investigated for both gas systems using molecular simulations. A correlation between size-selective equilibrium adsorptive separation capabilities and pore diameter is established in materials with complex pore distributions. A method of generating mobile extra-framework cations which drastically increase adsorptive selectivity toward nitrogen over oxygen via electrostatic interactions is explored through experiments and simulations. Finally, deposition of redox-active ferrocene molecules into systematically generated defects is shown to be an effective method of increasing selectivity towards oxygen.
ContributorsMcIntyre, Sean (Author) / Mu, Bin (Thesis advisor) / Green, Matthew (Committee member) / Lind, Marylaura (Committee member) / Arizona State University (Publisher)
Created2019
Description
Rapid development of new technology has significantly disrupted the way radiotherapy is planned and delivered. These processes involve delivering high radiation doses to the target tumor while minimizing dose to the surrounding healthy tissue. However, with rapid implementation of these new technologies, there is a need for the detection of prescribed ionizing radiation for radioprotection of the patient and quality assurance of the technique employed. Most available clinical sensors are subjected to various limitations including requirement of extensive training, loss of readout with sequential measurements, sensitivity to light and post-irradiation wait time prior to analysis. Considering these disadvantages, there is still a need for a sensor that can be fabricated with ease and still operate effectively in predicting the delivered radiation dose.
The dissertation discusses the development of a sensor that changes color upon exposure to therapeutic levels of ionizing radiation used during routine radiotherapy. The underlying principle behind the sensor is based on the formation of gold nanoparticles from its colorless precursor salt solution upon exposure to ionizing radiation. Exposure to ionizing radiation generates free radicals which reduce ionic gold to its zerovalent gold form which further nucleate and mature into nanoparticles. The generation of these nanoparticles render a change in color from colorless to a maroon/pink depending on the intensity of incident ionizing radiation. The shade and the intensity of the color developed is used to quantitatively and qualitatively predict the prescribed radiation dose.
The dissertation further describes the applicability of sensor to detect a wide range of ionizing radiation including high energy photons, protons, electrons and emissions from radioactive isotopes while remaining insensitive to non-ionizing radiation. The sensor was further augmented with a capability to differentiate regions that are irradiated and non-irradiated in two dimensions. The dissertation further describes the ability of the sensor to predict dose deposition in all three dimensions. The efficacy of the sensor to predict the prescribed dose delivered to canine patients undergoing radiotherapy was also demonstrated. All these taken together demonstrate the potential of this technology to be translatable to the clinic to ensure patient safety during routine radiotherapy.
The dissertation discusses the development of a sensor that changes color upon exposure to therapeutic levels of ionizing radiation used during routine radiotherapy. The underlying principle behind the sensor is based on the formation of gold nanoparticles from its colorless precursor salt solution upon exposure to ionizing radiation. Exposure to ionizing radiation generates free radicals which reduce ionic gold to its zerovalent gold form which further nucleate and mature into nanoparticles. The generation of these nanoparticles render a change in color from colorless to a maroon/pink depending on the intensity of incident ionizing radiation. The shade and the intensity of the color developed is used to quantitatively and qualitatively predict the prescribed radiation dose.
The dissertation further describes the applicability of sensor to detect a wide range of ionizing radiation including high energy photons, protons, electrons and emissions from radioactive isotopes while remaining insensitive to non-ionizing radiation. The sensor was further augmented with a capability to differentiate regions that are irradiated and non-irradiated in two dimensions. The dissertation further describes the ability of the sensor to predict dose deposition in all three dimensions. The efficacy of the sensor to predict the prescribed dose delivered to canine patients undergoing radiotherapy was also demonstrated. All these taken together demonstrate the potential of this technology to be translatable to the clinic to ensure patient safety during routine radiotherapy.
ContributorsSubramaniam Pushpavanam, Karthik (Author) / Rege, Kaushal (Thesis advisor) / Sapareto, Stephen (Committee member) / Nannenga, Brent (Committee member) / Green, Matthew (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.
ContributorsCang, Ruijin (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018