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Description
The operating temperature of photovoltaic (PV) modules has a strong impact on the expected performance of said modules in photovoltaic arrays. As the install capacity of PV arrays grows throughout the world, improved accuracy in modeling of the expected module temperature, particularly at finer time scales, requires improvements in the existing photovoltaic temperature models. This thesis work details the investigation, motivation, development, validation, and implementation of a transient photovoltaic module temperature model based on a weighted moving-average of steady-state temperature predictions.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
This thesis work first details the literature review of steady-state and transient models that are commonly used by PV investigators in performance modeling. Attempts to develop models capable of accounting for the inherent transient thermal behavior of PV modules are shown to improve on the accuracy of the steady-state models while also significantly increasing the computational complexity and the number of input parameters needed to perform the model calculations.
The transient thermal model development presented in this thesis begins with an investigation of module thermal behavior performed through finite-element analysis (FEA) in a computer-aided design (CAD) software package. This FEA was used to discover trends in transient thermal behavior for a representative PV module in a timely manner. The FEA simulations were based on heat transfer principles and were validated against steady-state temperature model predictions. The dynamic thermal behavior of PV modules was determined to be exponential, with the shape of the exponential being dependent on the wind speed and mass per unit area of the module.
The results and subsequent discussion provided in this thesis link the thermal behavior observed in the FEA simulations to existing steady-state temperature models in order to create an exponential weighting function. This function can perform a weighted average of steady-state temperature predictions within 20 minutes of the time in question to generate a module temperature prediction that accounts for the inherent thermal mass of the module while requiring only simple input parameters. Validation of the modeling method presented here shows performance modeling accuracy improvement of 0.58%, or 1.45°C, over performance models relying on steady-state models at narrow data intervals.
ContributorsPrilliman, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020
Description
Precursors of carbon fibers include rayon, pitch, and polyacrylonitrile fibers that can be heat-treated for high-strength or high-modulus carbon fibers. Among them, polyacrylonitrile has been used most frequently due to its low viscosity for easy processing and excellent performance for high-end applications. To further explore polyacrylonitrile-based fibers for better precursors, in this study, carbon nanofillers were introduced in the polymer matrix to examine their reinforcement effects and influences on carbon fiber performance. Two-dimensional graphene nanoplatelets were mainly used for the polymer reinforcement and one-dimensional carbon nanotubes were also incorporated in polyacrylonitrile as a comparison. Dry-jet wet spinning was used to fabricate the composite fibers. Hot-stage drawing and heat-treatment were used to evolve the physical microstructures and molecular morphologies of precursor and carbon fibers. As compared to traditionally used random dispersions, selective placement of nanofillers was effective in improving composite fiber properties and enhancing mechanical and functional behaviors of carbon fibers. The particular position of reinforcement fillers with polymer layers was enabled by the in-house developed spinneret used for fiber spinning. The preferential alignment of graphitic planes contributed to the enhanced mechanical and functional behaviors than those of dispersed nanoparticles in polyacrylonitrile composites. The high in-plane modulus of graphene and the induction to polyacrylonitrile molecular carbonization/graphitization were the motivation for selectively placing graphene nanoplatelets between polyacrylonitrile layers. Mechanical tests, scanning electron microscopy, thermal, and electrical properties were characterized. Applications such as volatile organic compound sensing and pressure sensing were demonstrated.
ContributorsFranklin, Rahul Joseph (Author) / Song, Kenan (Thesis advisor) / Jiao, Yang (Thesis advisor) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2020
Description
Multi-material fabrication allows for the creation of individual parts composed of several materials with distinct properties, providing opportunities for integrating mechanisms into monolithic components. Components produced in this manner will have material boundaries which may be points of failure. However, the unique capabilities of multi-material fabrication allow for the use of graded material transitions at these boundaries to mitigate the impact of abrupt material property changes.
The goal of this work is to identify methods of creating graded material transitions that can improve the ultimate tensile strength of a multi-material component while maintaining other model properties. Particular focus is given towards transitions that can be produced using low cost manufacturing equipment. This work presents a series of methods for creating graded material transitions which include previously established transition types as well as several novel techniques. Test samples of each transition type were produced using additive manufacturing and their performance was measured. It is shown that some types of transitions can increase the ultimate strength of a part, while others may introduce new stress concentrations that reduce performance. This work then presents a method for adjusting the elastic modulus of a component to which graded material transitions have been added to allow the original design properties to be met.
The goal of this work is to identify methods of creating graded material transitions that can improve the ultimate tensile strength of a multi-material component while maintaining other model properties. Particular focus is given towards transitions that can be produced using low cost manufacturing equipment. This work presents a series of methods for creating graded material transitions which include previously established transition types as well as several novel techniques. Test samples of each transition type were produced using additive manufacturing and their performance was measured. It is shown that some types of transitions can increase the ultimate strength of a part, while others may introduce new stress concentrations that reduce performance. This work then presents a method for adjusting the elastic modulus of a component to which graded material transitions have been added to allow the original design properties to be met.
ContributorsBrauer, Cole (Author) / Aukes, Daniel (Thesis advisor) / Chen, Xiangfan (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2020
Description
Compressible fluid flows involving multiple physical states of matter occur in both nature and technical applications such as underwater explosions and implosions, cavitation-induced bubble collapse in naval applications and Richtmyer-Meshkov type instabilities in inertial confinement fusion. Of particular interest is the atomization of fuels that enable shock-induced mixing of fuel and oxidizer in supersonic combustors. Due to low residence times and varying length scales, providing insight through physical experiments is both technically challenging and sometimes unfeasible. Numerical simulations can help provide detailed insight and aid in the engineering design of devices that can harness these physical phenomena.
In this research, computational methods were developed to accurately simulate phase interfaces in compressible fluid flows with a focus on targeting primary atomization. Novel numerical methods which treat the phase interface as a discontinuity, and as a smeared region were developed using low-dissipation, high-order schemes. The resulting methods account for the effects of compressibility, surface tension and viscosity. To aid with the varying length scales and high-resolution requirements found in atomization applications, an adaptive mesh refinement (AMR) framework is used to provide high-resolution only in regions of interest. The developed methods were verified with test cases involving strong shocks, high density ratios, surface tension effects and jumps in the equations of state, in one-, two- and three dimensions, obtaining good agreement with theoretical and experimental results. An application case of the primary atomization of a liquid jet injected into a Mach 2 supersonic crossflow of air is performed with the methods developed.
In this research, computational methods were developed to accurately simulate phase interfaces in compressible fluid flows with a focus on targeting primary atomization. Novel numerical methods which treat the phase interface as a discontinuity, and as a smeared region were developed using low-dissipation, high-order schemes. The resulting methods account for the effects of compressibility, surface tension and viscosity. To aid with the varying length scales and high-resolution requirements found in atomization applications, an adaptive mesh refinement (AMR) framework is used to provide high-resolution only in regions of interest. The developed methods were verified with test cases involving strong shocks, high density ratios, surface tension effects and jumps in the equations of state, in one-, two- and three dimensions, obtaining good agreement with theoretical and experimental results. An application case of the primary atomization of a liquid jet injected into a Mach 2 supersonic crossflow of air is performed with the methods developed.
ContributorsKannan, Karthik (Author) / Herrmann, Marcus (Thesis advisor) / Huang, Huei-Ping (Committee member) / Lopez, Juan (Committee member) / Peet, Yulia (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2020
Description
Unmanned subsurface investigation technologies for the Moon are of special significance for future exploration when considering the renewed interest of the international community for this interplanetary destination. In precision agriculture, farmers demand quasi-real-time sensors and instruments with remote crop and soil detection properties to meet sustainability goals and achieve healthier and higher crop yields. Hence, there is the need for a robot that will be able to travel through the soil and conduct sampling or in-situ analysis of the subsurface materials on earth and in space. This thesis presents the design, fabrication, and characterization of a robot that can travel through the soil. The robot consists of a helical screw design coupled with a fin that acts as an anchor. The fin design is an integral part of the robot, allowing it to travel up and down the medium unaided. Experiments were performed to characterize different designs. It was concluded that the most energy-efficient speed from traveling down the medium is 20 rpm, while 60 rpm was the efficient speed for traveling up the medium. This research provides vital insight into developing subsurface robots enabling us to unearth the valuable knowledge that subsurface environment holds to help the agricultural, construction, and exploration communities.
ContributorsOkwae, Nana Kwame Kwame (Author) / Marvi, Hamidreza (Thesis advisor) / Tao, Jungliang (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2020
Description
Autonomous vehicle technology has been evolving for years since the Automated Highway System Project. However, this technology has been under increased scrutiny ever since an autonomous vehicle killed Elaine Herzberg, who was crossing the street in Tempe, Arizona in March 2018. Recent tests of autonomous vehicles on public roads have faced opposition from nearby residents. Before these vehicles are widely deployed, it is imperative that the general public trusts them. For this, the vehicles must be able to identify objects in their surroundings and demonstrate the ability to follow traffic rules while making decisions with human-like moral integrity when confronted with an ethical dilemma, such as an unavoidable crash that will injure either a pedestrian or the passenger.
Testing autonomous vehicles in real-world scenarios would pose a threat to people and property alike. A safe alternative is to simulate these scenarios and test to ensure that the resulting programs can work in real-world scenarios. Moreover, in order to detect a moral dilemma situation quickly, the vehicle should be able to identify objects in real-time while driving. Toward this end, this thesis investigates the use of cross-platform training for neural networks that perform visual identification of common objects in driving scenarios. Here, the object detection algorithm Faster R-CNN is used. The hypothesis is that it is possible to train a neural network model to detect objects from two different domains, simulated or physical, using transfer learning. As a proof of concept, an object detection model is trained on image datasets extracted from CARLA, a virtual driving environment, via transfer learning. After bringing the total loss factor to 0.4, the model is evaluated with an IoU metric. It is determined that the model has a precision of 100% and 75% for vehicles and traffic lights respectively. The recall is found to be 84.62% and 75% for the same. It is also shown that this model can detect the same classes of objects from other virtual environments and real-world images. Further modifications to the algorithm that may be required to improve performance are discussed as future work.
Testing autonomous vehicles in real-world scenarios would pose a threat to people and property alike. A safe alternative is to simulate these scenarios and test to ensure that the resulting programs can work in real-world scenarios. Moreover, in order to detect a moral dilemma situation quickly, the vehicle should be able to identify objects in real-time while driving. Toward this end, this thesis investigates the use of cross-platform training for neural networks that perform visual identification of common objects in driving scenarios. Here, the object detection algorithm Faster R-CNN is used. The hypothesis is that it is possible to train a neural network model to detect objects from two different domains, simulated or physical, using transfer learning. As a proof of concept, an object detection model is trained on image datasets extracted from CARLA, a virtual driving environment, via transfer learning. After bringing the total loss factor to 0.4, the model is evaluated with an IoU metric. It is determined that the model has a precision of 100% and 75% for vehicles and traffic lights respectively. The recall is found to be 84.62% and 75% for the same. It is also shown that this model can detect the same classes of objects from other virtual environments and real-world images. Further modifications to the algorithm that may be required to improve performance are discussed as future work.
ContributorsSankaramangalam Ulhas, Sangeet (Author) / Berman, Spring (Thesis advisor) / Johnson, Kathryn (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Created2019
Description
Essential knowledge of Co-continuous composite material properties are explored in this thesis. Mechanical characterization of these materials gives a detailed outlook to use them in design, manufacture and tailor make the products.
Soft and hard polymer materials have extensive properties individually, but when combined to make a single structure, they give an exceptional combination of properties. In this study, Polymer materials used are in the form of Co-Continuous structures (i.e., both soft and hard polymers are continuous throughout the microstructure) fabricated into several microstructures namely, Simple Cubic (SC), Body-Centered Cubic (BCC) and Face Centered Cubic (FCC) shapes. An experimental process is designed and fine-tuned from existing methods to understand and record the mechanical response of these co-continuous polymers. Experimental testing is used to gather detailed information about several constituencies namely stress behavior and damage progression. A 3D imaging technique, Microtomography is used to visualize damage initiation and progression in the sample. Variations in energy absorption, fracture initiation and damage propagation in samples are observed and correlated analysis is performed to provide a logical explanation. Comparative studies are performed as well for different structures.
Based on the Knowledge gained from the above study on co-continuous polymer composites, several conclusions are drawn, and future work directions are suggested.
Soft and hard polymer materials have extensive properties individually, but when combined to make a single structure, they give an exceptional combination of properties. In this study, Polymer materials used are in the form of Co-Continuous structures (i.e., both soft and hard polymers are continuous throughout the microstructure) fabricated into several microstructures namely, Simple Cubic (SC), Body-Centered Cubic (BCC) and Face Centered Cubic (FCC) shapes. An experimental process is designed and fine-tuned from existing methods to understand and record the mechanical response of these co-continuous polymers. Experimental testing is used to gather detailed information about several constituencies namely stress behavior and damage progression. A 3D imaging technique, Microtomography is used to visualize damage initiation and progression in the sample. Variations in energy absorption, fracture initiation and damage propagation in samples are observed and correlated analysis is performed to provide a logical explanation. Comparative studies are performed as well for different structures.
Based on the Knowledge gained from the above study on co-continuous polymer composites, several conclusions are drawn, and future work directions are suggested.
ContributorsVARAKANTHAM, MADHAVA REDDY (Author) / Yongming, Liu (Thesis advisor) / Patel, Jay (Committee member) / Hanqing, Jiang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Demand for green energy alternatives to provide stable and reliable energy
solutions has increased over the years which has led to the rapid expansion of global
markets in renewable energy sources such as solar photovoltaic (PV) technology. Newest
amongst these technologies is the Bifacial PV modules, which harvests incident radiation
from both sides of the module. The overall power generation can be significantly increased
by using these bifacial modules. The purpose of this research is to investigate and maximize
the effect of back reflectors, designed to increase the efficiency of the module by utilizing
the intercell light passing through the module to increase the incident irradiance, on the
energy output using different profiles placed at varied distances from the plane of the array
(POA). The optimum reflector profile and displacement of the reflector from the module
are determined experimentally.
Theoretically, a 60-cell bifacial module can produce 26% additional energy in
comparison to a 48-cell bifacial module due to the 12 excess cells found in the 60-cell
module. It was determined that bifacial modules have the capacity to produce additional
energy when optimized back reflectors are utilized. The inverted U reflector produced
higher energy gain when placed at farther distances from the module, indicating direct
dependent proportionality between the placement distance of the reflector from the module
and the output energy gain. It performed the best out of all current construction geometries
with reflective coatings, generating more than half of the additional energy produced by a
densely-spaced 60-cell benchmark module compared to a sparsely-spaced 48-cell reference
module.ii
A gain of 11 and 14% was recorded on cloudy and sunny days respectively for the
inverted U reflector. This implies a reduction in the additional cells of the 60-cell module
by 50% can produce the same amount of energy of the 60-cell module by a 48-cell module
with an inverted U reflector. The use of the back reflectors does not only affect the
additional energy gain but structural and land costs. Row to row spacing for bifacial
systems(arrays) is reduced nearly by half as the ground height clearance is largely
minimized, thus almost 50% of height constraints for mounting bifacial modules, using
back reflectors resulting in reduced structural costs for mounting of bifacial modules
solutions has increased over the years which has led to the rapid expansion of global
markets in renewable energy sources such as solar photovoltaic (PV) technology. Newest
amongst these technologies is the Bifacial PV modules, which harvests incident radiation
from both sides of the module. The overall power generation can be significantly increased
by using these bifacial modules. The purpose of this research is to investigate and maximize
the effect of back reflectors, designed to increase the efficiency of the module by utilizing
the intercell light passing through the module to increase the incident irradiance, on the
energy output using different profiles placed at varied distances from the plane of the array
(POA). The optimum reflector profile and displacement of the reflector from the module
are determined experimentally.
Theoretically, a 60-cell bifacial module can produce 26% additional energy in
comparison to a 48-cell bifacial module due to the 12 excess cells found in the 60-cell
module. It was determined that bifacial modules have the capacity to produce additional
energy when optimized back reflectors are utilized. The inverted U reflector produced
higher energy gain when placed at farther distances from the module, indicating direct
dependent proportionality between the placement distance of the reflector from the module
and the output energy gain. It performed the best out of all current construction geometries
with reflective coatings, generating more than half of the additional energy produced by a
densely-spaced 60-cell benchmark module compared to a sparsely-spaced 48-cell reference
module.ii
A gain of 11 and 14% was recorded on cloudy and sunny days respectively for the
inverted U reflector. This implies a reduction in the additional cells of the 60-cell module
by 50% can produce the same amount of energy of the 60-cell module by a 48-cell module
with an inverted U reflector. The use of the back reflectors does not only affect the
additional energy gain but structural and land costs. Row to row spacing for bifacial
systems(arrays) is reduced nearly by half as the ground height clearance is largely
minimized, thus almost 50% of height constraints for mounting bifacial modules, using
back reflectors resulting in reduced structural costs for mounting of bifacial modules
ContributorsMARTIN, PEDRO JESSE (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2019
Description
Mechanical fatigue has been a research topic since quite a long time. It is a complex phenomenon at molecular level. The fact that fatigue failure occurs much below material’s yield point, made it much interesting area for research. So, to understand the physics behind fatigue failure became an important research topic. Fatigue failure is characterized by crack initiation and then crack propagation to finally fracture the material. If this could be modelled mathematically, then it would save lot of resources and would assure the structural integrity of given component. Many such mathematical models were published to calculate fatigue crack growth for Constant Amplitude Loading, but most of the time the applied loads are variable in nature. So, to address this problem a mathematical model which will predict fatigue life in terms of time history is needed. This research study focuses on improving previously developed subcycle fatigue crack growth model also known as small time scale model which works well in Paris regime. In the first part, focus has been given on estimating threshold point using subcycle model by applying load shedding techniques. Later subcycle model has been modified to include fatigue crack growth in threshold region. In the second part of this research study, the concept of Equivalent Initial Flaw Size (EIFS) and fracture mechanics approach has been used to compute fatigue life for Constant as well as Random Amplitude Loading. Further the model has been extended to compute the fatigue life under Mixed Mode Loading (Mode I & Mode II). Standard material properties are used to calibrate the model parameters. The fatigue life results were validated using available open literature data as well as experimental testing data. The subcycle model can be used to calculate fatigue life in case of HCF and LCF, which is suggested as a future work for this research study.
ContributorsShivankar, Sushant (Author) / Liu, Yongming YL (Thesis advisor) / Nian, Qiong QN (Committee member) / Jiao, Yang YJ (Committee member) / Arizona State University (Publisher)
Created2021
Description
The tire blowout is potentially one of the most critical accidents that may occur on the road. Following a tire blowout, the mechanical behavior of the tire is extremely affected and the forces generating from the interaction of the tire and the ground are redistributed. This severe change in the mechanism of tire force generation influences the dynamic characteristics of the vehicle significantly. Thus, the vehicle loses its directional stability and has a risk of departing its lane and colliding with other vehicles or the guardrail. This work aims to further broaden our current knowledge of the vehicle dynamic response to a blowout scenario during both rectilinear and curvilinear motions. To that end, a fourteen degrees of freedom full vehicle model combined with the well-grounded Dugoff’s tire models is developed and validated using the high fidelity MSC Adams package. To examine the effect of the tire blowout on the dynamic behavior of the vehicle, a series of tests incorporating a tire blowout is conducted in both rectilinear and curvilinear maneuvers with different tire burst locations. It is observed that the reconstruction of the tire forces resulting from blowout leads to a substantial change in the dynamics of the vehicle as well as a severe directional instability and possibly a rollover accident. Consequently, a corrective safety control system utilizing a braking/traction torque actuation mechanism is designed. The basic idea of the stability controller is to produce a regulated amount of input torque on one or more wheels apart from the blown tire. The proposed novel control-oriented model eliminates the simplifying assumptions used in the design of such controllers. Furthermore, a double integrator was augmented to enhance the steady-state performance of the sliding mode closed-loop system. The chattering problem stemmed by the switching nature of the controller is diminished through tuning the slope of saturation function. Different apparatuses are used in terms of actuation, using an individual front actuator, utilizing multi-actuator, and using two-wheel braking torques successively. It is found that the proposed controllers are perfectly capable of stabilizing the vehicle and robustly track the desired trajectory in straight-line and cornering maneuvers.
ContributorsAl-Quran, Mahdi (Author) / Mayyas, Abdel Ra'Ouf (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Chen, Yan (Committee member) / Ren, Yi (Committee member) / Yong, Sze (Committee member) / Arizona State University (Publisher)
Created2021