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Description
Droplet-structure interactions play a pivotal role in many engineering applications as droplet-based solutions are evolving. This work explores the physical understanding of these interactions through systematic research leading to improvements in thermal management via dropwise condensation (DWC), and breathable protective wearables against chemical aerosols for better thermoregulation.
In DWC, the heat transfer rate can be further increased by increasing the nucleation and by optimally ‘refreshing’ the surface via droplet shedding. Softening of surfaces favor the former while having an adverse effect on the latter. This optimization problem is addressed by investigating how mechanical properties of a substrate impact relevant droplet-surface interactions and DWC heat transfer rate. The results obtained by combining droplet induced surface deformation with finite element model show that softening of the substrates below a shear modulus of 500 kPa results in a significant reduction in the condensation heat transfer rate.
On the other hand, interactions between droplet and polymer leading to polymer swelling can be used to develop breathable wearables for use in chemically harsh environments. Chemical aerosols are hazardous and conventional protective measures include impermeable barriers which limit the thermoregulation. To solve this, a solution is proposed consisting of a superabsorbent polymer developed to selectively absorb these chemicals and closing the pores in the fabric. Starting from understanding and modeling the droplet induced swelling in elastomers, the extent and topological characteristic of swelling is shown to depend on the relative comparison of the polymer and aerosol geometries. Then, this modeling is extended to a customized polymer, through a simplified characterization paradigm. In that, a new method is proposed to measure the swelling parameters of the polymer-solvent pair and develop a validated model for swelling. Through this study, it is shown that for this polymer, the concentration-dependent diffusion coefficient can be measured through gravimetry and Poroelastic Relaxation Indentation, simplifying the characterization effort. Finally, this model is used to design composite fabric. Specifically, using model results, the SAP geometry, base fabric design, method of composition is optimized, and the effectiveness of the composite fabric highlighted in moderate-to-high concentrations over short durations.
In DWC, the heat transfer rate can be further increased by increasing the nucleation and by optimally ‘refreshing’ the surface via droplet shedding. Softening of surfaces favor the former while having an adverse effect on the latter. This optimization problem is addressed by investigating how mechanical properties of a substrate impact relevant droplet-surface interactions and DWC heat transfer rate. The results obtained by combining droplet induced surface deformation with finite element model show that softening of the substrates below a shear modulus of 500 kPa results in a significant reduction in the condensation heat transfer rate.
On the other hand, interactions between droplet and polymer leading to polymer swelling can be used to develop breathable wearables for use in chemically harsh environments. Chemical aerosols are hazardous and conventional protective measures include impermeable barriers which limit the thermoregulation. To solve this, a solution is proposed consisting of a superabsorbent polymer developed to selectively absorb these chemicals and closing the pores in the fabric. Starting from understanding and modeling the droplet induced swelling in elastomers, the extent and topological characteristic of swelling is shown to depend on the relative comparison of the polymer and aerosol geometries. Then, this modeling is extended to a customized polymer, through a simplified characterization paradigm. In that, a new method is proposed to measure the swelling parameters of the polymer-solvent pair and develop a validated model for swelling. Through this study, it is shown that for this polymer, the concentration-dependent diffusion coefficient can be measured through gravimetry and Poroelastic Relaxation Indentation, simplifying the characterization effort. Finally, this model is used to design composite fabric. Specifically, using model results, the SAP geometry, base fabric design, method of composition is optimized, and the effectiveness of the composite fabric highlighted in moderate-to-high concentrations over short durations.
ContributorsPhadnis, Akshay (Author) / Rykaczewski, Konrad (Thesis advisor) / Wang, Robert (Committee member) / Wang, Liping (Committee member) / Oswald, Jay (Committee member) / Burgin, Timothy (Committee member) / Arizona State University (Publisher)
Created2019
Description
Corrosion fatigue has been of prime concern in railways, aerospace, construction industries and so on. Even in the case of many medical equipment, corrosion fatigue is considered to be a major challenge. The fact that even high strength materials have lower resistance to corrosion fatigue makes it an interesting area for research. The analysis of propagation of fatigue crack growth under environmental interaction and the life prediction is significant to reduce the maintenance costs and assure structural integrity. Without proper investigation of the crack extension under corrosion fatigue, the scenario can lead to catastrophic disasters due to premature failure of a structure. An attempt has been made in this study to predict the corrosion fatigue crack growth with reasonable accuracy. Models that have been developed so far predict the crack propagation for constant amplitude loading (CAL). However, most of the industrial applications encounter random loading. Hence there is a need to develop models based on time scale. An existing time scale model that can predict the fatigue crack growth for constant and variable amplitude loading (VAL) in the Paris region is initially modified to extend the prediction to near threshold and unstable crack growth region. Extensive data collection was carried out to calibrate the model for corrosion fatigue crack growth (CFCG) based on the experimental data. The time scale model is improved to incorporate the effect of corrosive environments such as NaCl and dry hydrogen in the fatigue crack growth (FCG) by investigation of the trend in change of the crack growth. The time scale model gives the advantage of coupling the time phenomenon stress corrosion cracking which is suggested as a future work in this paper.
ContributorsKurian, Bianca (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Shock loading produces a compressive stress pulse with steep gradients in density, temperature, and pressure that are also often modeled as discontinuities. When a material is subject to these dynamic (shock) loading conditions, fracture and deformation patterns due to spall damage can arise. Spallation is a dynamic material failure that is caused by the nucleation, growth, and coalescence of voids, with possible ejection of the surface of the material. Intrinsic defects, such as grain boundaries are the preferred initiation sites of spall damage in high purity materials. The focus of this research is to study the phenomena that cause void nucleation and growth at a particular grain boundary (GB), chosen to maximize spall damage localization.
Bicrystal samples were shock loaded using flyer-plates via light gas gun and direct laser ablation. Stress, pulse duration, and crystal orientation along the shock direction were varied for a fixed boundary misorientation to determine thresholds for void nucleation and coalescence as functions of these parameters. Pressures for gas gun experiments ranged from 2 to 5 GPa, while pressures for laser ablation experiments varied from 17 to 25 GPa. Samples were soft recovered to perform damage characterization using electron backscattering diffraction (EBSD) and Scanning Electron Microscopy (SEM). Results showed a 14% difference in the thresholds for void nucleation and coalescence between samples with different orientations along the shock direction, which were affected by pulse duration and stress level. Fractography on boundaries with strong damage localization showed many small voids, indicating they experience rapid nucleation, causing early coalescence. Composition analysis was also performed to determine the effect of impurities on damage evolution. Results showed that higher levels of impurities led to more damage. ABAQUS/Explicit models were developed to simulate flyer-plate impact and void growth with the same crystal orientations and experimental conditions. Results are able to match the damage seen in each grain of the target experimentally. The Taylor Factor mismatch at the boundary can also be observed in the model with the higher Taylor Factor grain exhibiting more damage.
Bicrystal samples were shock loaded using flyer-plates via light gas gun and direct laser ablation. Stress, pulse duration, and crystal orientation along the shock direction were varied for a fixed boundary misorientation to determine thresholds for void nucleation and coalescence as functions of these parameters. Pressures for gas gun experiments ranged from 2 to 5 GPa, while pressures for laser ablation experiments varied from 17 to 25 GPa. Samples were soft recovered to perform damage characterization using electron backscattering diffraction (EBSD) and Scanning Electron Microscopy (SEM). Results showed a 14% difference in the thresholds for void nucleation and coalescence between samples with different orientations along the shock direction, which were affected by pulse duration and stress level. Fractography on boundaries with strong damage localization showed many small voids, indicating they experience rapid nucleation, causing early coalescence. Composition analysis was also performed to determine the effect of impurities on damage evolution. Results showed that higher levels of impurities led to more damage. ABAQUS/Explicit models were developed to simulate flyer-plate impact and void growth with the same crystal orientations and experimental conditions. Results are able to match the damage seen in each grain of the target experimentally. The Taylor Factor mismatch at the boundary can also be observed in the model with the higher Taylor Factor grain exhibiting more damage.
ContributorsFortin, Elizabeth Victoria (Author) / Peralta, Pedro (Thesis advisor) / Mignolet, Marc (Committee member) / Loomis, Eric (Committee member) / Oswald, Jay (Committee member) / Solanki, Kiran (Committee member) / Arizona State University (Publisher)
Created2020
Description
Phononic crystals are artificially engineered materials that can forbid phonon propagation in a specific frequency range that is referred to as a “phononic band gap.” Phononic crystals that have band gaps in the GHz to THz range can potentially enable sophisticated control over thermal transport with “phononic devices”. Calculations of the phononic band diagram are the standard method of determining if a given phononic crystal structure has a band gap. However, calculating the phononic band diagram is a computationally expensive and time-consuming process that can require sophisticated modeling and coding. In addition to this computational burden, the inverse process of designing a phononic crystal with a specific band gap center frequency and width is a challenging problem that requires extensive trial-and-error work.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
ContributorsSadat, Seid Mohamadali (Author) / Wang, Robert Y (Thesis advisor) / Huang, Huei-Ping (Committee member) / Ankit, Kumar (Committee member) / Wang, Liping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation aimed to evaluate the effectiveness and drawbacks of promising fall prevention strategies in individuals with stroke by rigorously analyzing the biomechanics of laboratory falls and compensatory movements required to prevent a fall. Ankle-foot-orthoses (AFOs) and functional electrical stimulators (FESs) are commonly prescribed to treat foot drop. Despite well-established positive impacts of AFOs and FES devices on balance and gait, AFO and FES users fall at a high rate. In chapter 2 (as a preliminary study), solely mechanical impacts of a semi-rigid AFO on the compensatory stepping response of young healthy individuals following trip-like treadmill perturbations were evaluated. It was found that a semi-rigid AFO on the stepping leg diminished the propulsive impulse of the compensatory step which led to decreased trunk movement control, shorter step length, and reduced center of mass (COM) stability. These results highlight the critical role of plantarflexors in generating an effective compensatory stepping response. In chapter 3, the underlying biomechanical mechanisms leading to high fall risk in long-term AFO and FES users with chronic stroke were studied. It was found that AFO and FES users fall more than Non-users because they have a more impaired lower limb that is not fully addressed by AFO/FES, therefore leading to a more impaired compensatory stepping response characterized by increased inability to generate a compensatory step with paretic leg and decreased trunk movement control. An ideal future AFO that provides dorsiflexion assistance during the swing phase and plantarflexion assistance during the push-off phase of gait is suggested to enhance the compensatory stepping response and reduce more falls. In chapter 4, the effects of a single-session trip-specific training on the compensatory stepping response of individuals with stroke were evaluated. Trunk movement control was improved after a single session of training suggesting that this type of training is a viable option to enhance compensatory stepping response and reduce falls in individuals with stroke. Finally, a future powered AFO with plantarflexion assistance complemented by a trip-specific training program is suggested to enhance the compensatory stepping response and decrease falls in individuals with stroke.
ContributorsNevisipour, Masood (Author) / Honeycutt, Claire (Thesis advisor) / Sugar, Thomas (Thesis advisor) / Artemiadis, Panagiotis (Committee member) / Abbas, James (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2019
Description
Polyurea is a highly versatile material used in coatings and armor systems to protect against extreme conditions such as ballistic impact, cavitation erosion, and blast loading. However, the relationships between microstructurally-dependent deformation mechanisms and the mechanical properties of polyurea are not yet fully understood, especially under extreme conditions. In this work, multi-scale coarse-grained models are developed to probe molecular dynamics across the wide range of time and length scales that these fundamental deformation mechanisms operate. In the first of these models, a high-resolution coarse-grained model of polyurea is developed, where similar to united-atom models, hydrogen atoms are modeled implicitly. This model was trained using a modified iterative Boltzmann inversion method that dramatically reduces the number of iterations required. Coarse-grained simulations using this model demonstrate that multiblock systems evolve to form a more interconnected hard phase, compared to the more interrupted hard phase composed of distinct ribbon-shaped domains found in diblock systems. Next, a reactive coarse-grained model is developed to simulate the influence of the difference in time scales for step-growth polymerization and phase segregation in polyurea. Analysis of the simulated cured polyurea systems reveals that more rapid reaction rates produce a smaller diameter ligaments in the gyroidal hard phase as well as increased covalent bonding connecting the hard domain ligaments as evidenced by a larger fraction of bridging segments and larger mean radius of gyration of the copolymer chains. The effect that these processing-induced structural variations have on the mechanical properties of the polymer was tested by simulating uniaxial compression, which revealed that the higher degree of hard domain connectivity leads to a 20% increase in the flow stress. A hierarchical multiresolution framework is proposed to fully link coarse-grained molecular simulations across a broader range of time scales, in which a family of coarse-grained models are developed. The models are connected using an incremental reverse–mapping scheme allowing for long time scale dynamics simulated at a highly coarsened resolution to be passed all the way to an atomistic representation.
ContributorsLiu, Minghao (Author) / Oswald, Jay (Thesis advisor) / Muhich, Christopher (Committee member) / Jiang, Hanqing (Committee member) / Peralta, Pedro (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2020
Description
Information exists in various forms and a better utilization of the available information can benefit the system awareness and response predictions. The focus of this dissertation is on the fusion of different types of information using Bayesian-Entropy method. The Maximum Entropy method in information theory introduces a unique way of handling information in the form of constraints. The Bayesian-Entropy (BE) principle is proposed to integrate the Bayes’ theorem and Maximum Entropy method to encode extra information. The posterior distribution in Bayesian-Entropy method has a Bayesian part to handle point observation data, and an Entropy part that encodes constraints, such as statistical moment information, range information and general function between variables. The proposed method is then extended to its network format as Bayesian Entropy Network (BEN), which serves as a generalized information fusion tool for diagnostics, prognostics, and surrogate modeling.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
ContributorsWang, Yuhao (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Yan, Hao (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2020
Description
Almost all mechanical and electro-mechanical products are assemblies of multiple parts, either because of requirements for relative motion, or use of different materials, shape/size differences. Thus, assembly design is the very crux of engineering design. In addition to nominal design of an assembly, there is also tolerance design to determine allowable manufacturing variations to ensure proper functioning and assemblability. Most of the flexible assemblies are made by stamping sheet metal. Sheet metal stamping process involves plastically deforming sheet metals using dies. Sub-assemblies of two or more components are made with either spot-welding or riveting operations. Various sub-assemblies are finally joined, using spot-welds or rivets, to create the desired assembly. When two components are brought together for assembly, they do not align exactly; this causes gaps and irregularities in assemblies. As multiple parts are stacked, errors accumulate further. Stamping leads to variable deformations due to residual stresses and elastic recovery from plastic strain of metals; this is called as the ‘spring-back’ effect. When multiple components are stacked or assembled using spot welds, input parameters variations, such as sheet metal thickness, number and order of spot welds, cause variations in the exact shape of the final assembly in its free state. It is essential to understand the influence of these input parameters on the geometric variations of both the individual components and the assembly created using these components. Design of Experiment is used to generate principal effect study which evaluates the influence of input parameters on output parameters. The scope of this study is to quantify the geometric variations for a flexible assembly and evaluate their dependence on specific input variables. The 3 input variables considered are the thickness of the sheet material, the number of spot welds used and the spot-welding order to create the assembly. To quantify the geometric variations, sprung-back nodal points along lines, circular arcs, a combination of these, and a specific profile are reduced to metrologically simulated features.
ContributorsJoshi, Abhishek (Author) / Ren, Yi (Thesis advisor) / Davidson, Joseph (Committee member) / Shah, Jami (Committee member) / Arizona State University (Publisher)
Created2020
Description
Thermal Energy Storage (TES) is of great significance for many engineering applications as it allows surplus thermal energy to be stored and reused later, bridging the gap between requirement and energy use. Phase change materials (PCMs) are latent heat-based TES which have the ability to store and release heat through phase transition processes over a relatively narrow temperature range. PCMs have a wide range of operating temperatures and therefore can be used in various applications such as stand-alone heat storage in a renewable energy system, thermal storage in buildings, water heating systems, etc. In this dissertation, various PCMs are incorporated and investigated numerically and experimentally with different applications namely a thermochemical metal hydride (MH) storage system and thermal storage in buildings. In the second chapter, a new design consisting of an MH reactor encircled by a cylindrical sandwich bed packed with PCM is proposed. The role of the PCM is to store the heat released by the MH reactor during the hydrogenation process and reuse it later in the subsequent dehydrogenation process. In such a system, the exothermic and endothermic processes of the MH reactor can be utilized effectively by enhancing the thermal exchange between the MH reactor and the PCM bed. Similarly, in the third chapter, a novel design that integrates the MH reactor with cascaded PCM beds is proposed. In this design, two different types of PCMs with different melting temperatures and enthalpies are arranged in series to improve the heat transfer rate and consequently shorten the time duration of the hydrogenation and dehydrogenation processes. The performance of the new designs (in chapters 2 and 3) is investigated numerically and compared with the conventional designs in the literature. The results indicate that the new designs can significantly enhance the time duration of MH reaction (up to 87%). In the fourth chapter, organic coconut oil PCM (co-oil PCM) is explored experimentally and numerically for the first time as a thermal management tool in building applications. The results show that co-oil PCM can be a promising solution to improve the indoor thermal environment in semi-arid regions.
ContributorsAlqahtani, Talal (Author) / Phelan, Patrick E (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Mellouli, Sofiene (Committee member) / Wang, Robert (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2020
Description
Water desalination has become one of the viable solutions to provide drinking water in regions with limited natural resources. This is particularly true in small communities in arid regions, which suffer from low rainfall, declining surface water and increasing salinity of groundwater. Yet, current desalination methods are difficult to be implemented in these areas due to their centralized large-scale design. In addition, these methods require intensive maintenance, and sometimes do not operate in high salinity feedwater. Membrane distillation (MD) is one technology that can potentially overcome these challenges and has received increasing attention in the last 15 years. The driving force of MD is the difference in vapor pressure across a microporous hydrophobic membrane. Compared to conventional membrane-based technologies, MD can treat high concentration feedwater, does not need intensive pretreatment, and has better fouling resistance. More importantly, MD operates at low feed temperatures and so it can utilize low–grade heat sources such as solar energy for its operation. While the integration of solar energy and MD was conventionally indirect (i.e. by having two separate systems: a solar collector and an MD module), recent efforts were focused on direct integration where the membrane itself is integrated within a solar collector aiming to have a more compact, standalone design suitable for small-scale applications. In this dissertation, a comprehensive review of these efforts is discussed in Chapter 2. Two novel direct solar-powered MD systems were proposed and investigated experimentally: firstly, a direct contact MD (DCMD) system was designed by placing capillary membranes within an evacuated tube solar collector (ETC) (Chapter 3), and secondly, a submerged vacuum MD (S-VMD) system that uses circulation and aeration as agitation techniques was investigated (Chapter 4). A maximum water production per absorbing area of 0.96 kg·m–2·h–1 and a thermal efficiency of 0.51 were achieved. A final study was conducted to investigate the effect of ultrasound in an S-VMD unit (Chapter 5), which significantly enhanced the permeate flux (up to 24%) and reduced the specific energy consumption (up to 14%). The results add substantially to the understanding of integrating ultrasound with different MD processes.
ContributorsBamasag, Ahmad (Author) / Phelan, Patrick E (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Wang, Liping (Committee member) / Bocanegra, Luis (Committee member) / Roedel, Ronald (Committee member) / Arizona State University (Publisher)
Created2020