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- All Subjects: engineering
- All Subjects: Aerospace Engineering
- Creators: Liu, Yongming
- Creators: Phelan, Patrick
Description
District heating plays an important role in improving energy efficiency and providing thermal heat to buildings. Instead of using water as an energy carrier to transport sensible heat, this dissertation explores the use of liquid-phase thermochemical reactions for district heating as well as thermal storage. Chapters 2 and 3 present thermodynamic and design analyses for the proposed district heating system. Chapter 4 models the use of liquid-phase thermochemical reactions for on-site solar thermal storage. In brief, the proposed district heating system uses liquid-phase thermochemical reactions to transport thermal energy from a heat source to a heat sink. The separation ensures that the stored thermochemical heat can be stored indefinitely and/or transported long distances. The reactant molecules are then pumped over long distances to the heat sink, where they are combined in an exothermic reaction to provide heat. The product of the exothermic reaction is then pumped back to the heat source for re-use. The key evaluation parameter is the system efficiency.
The results demonstrate that with heat recovery, the system efficiency can be up to 77% when the sink temperature equals 25 C. The results also indicate that the appropriate chemical reaction candidates should have large reaction enthalpy and small reaction entropy. Further, the design analyses of two district heating systems, Direct District Heating (DDH) system and Indirect District Heating (IDH) system using the solvated case shows that the critical distance is 106m. When the distance is shorter than 1000,000m, the factors related to the chemical reaction at the user side and factors related to the separation process are important for the DDH system. When the distance is longer than 106m, the factors related to the fluid mechanic become more important. Because the substation of the IDH system degrades the quality of the energy, when the distance is shorter than 106m, the efficiency of the substation is significant. Lastly, I create models for on-site solar thermal storage systems using liquid-phase thermochemical reactions and hot water. The analysis shows that the thermochemical reaction is more competitive for long-duration storage applications. However, the heat recovery added to the thermochemical thermal storage system cannot help improving solar radiation absorption with high inlet temperature of the solar panel.
ContributorsZhang, Yanan (Author) / Wang, Robert (Thesis advisor) / Milcarek, Ryan (Committee member) / Parrish, Kristen (Committee member) / Phelan, Patrick (Committee member) / Rykaczewski, Konrad (Committee member) / Arizona State University (Publisher)
Created2022
Description
The technology and science capabilities of SmallSats continue to grow with the increase of capabilities in commercial off the shelf components. However, the maturation of SmallSat hardware has also led to an increase in component power consumption, this poses an issue with using traditional passive thermal management systems (radiators, thermal straps, etc.) to regulate high-power components. High power output becomes limited in order to maintain components within their allowable temperature ranges. The aim of this study is to explore new methods of using additive manufacturing to enable the usage of heat pipe structures on SmallSat platforms up to 3U’s in size. This analysis shows that these novel structures can increase the capabilities of SmallSat platforms by allowing for larger in-use heat loads from a nominal power density of 4.7 x 10^3 W/m3 to a higher 1.0 x 10^4 W/m3 , an order of magnitude increase. In addition, the mechanical properties of the SmallSat structure are also explored to characterize effects to the mechanical integrity of the spacecraft. The results show that the advent of heat pipe integration to the structures of SmallSats will lead to an increase in thermal management capabilities compared to the current state-of-the-art systems, while not reducing the structural integrity of the spacecraft. In turn, this will lead to larger science and technology capabilities for a field that is growing in both the education and private sectors.
ContributorsAcuna, Antonio (Author) / Das, Jnaneshwar (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Mignolet, Marc (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
Description
This study introduces a new outdoor accelerated testing method called “Field Accelerated Stress Testing (FAST)” for photovoltaic (PV) modules performed at two
different climatic sites in Arizona (hot-dry) and Florida (hot-humid). FAST is a combined
accelerated test methodology that simultaneously accounts for all the field-specific stresses
and accelerates only key stresses, such as temperature, to forecast the failure modes by 2-
7 times in advance depending on the activation energy of the degradation mechanism (i.e.,
10th year reliability issues can potentially be predicted in the 2nd year itself for an
acceleration factor of 5). In this outdoor combined accelerated stress study, the
temperatures of test modules were increased (by 16-19℃ compared to control modules)
using thermal insulations on the back of the modules. All other conditions (ambient
temperature, humidity, natural sunlight, wind speed, wind direction, and tilt angle) were
left constant for both test modules (with back thermal insulation) and control modules
(without thermal insulation). In this study, a total of sixteen 4-cell modules with two different construction types (glass/glass [GG] and glass/backsheet [GB]) and two different encapsulant types (ethylene
vinyl acetate [EVA] and polyolefin elastomer [POE]), were investigated at both sites with
eight modules at each site (four insulated and four non-insulated modules at each site). All
the modules were extensively characterized before installation in the field and after field
exposure over two years. The methods used for characterizing the devices included I-V
(current-voltage curves), EL (electroluminescence), UVF (ultraviolet fluorescence), and
reflectance. The key findings of this study are: i) the GG modules tend to operate at a higher temperature (1-3℃) than the GB modules at both sites of Arizona and Florida (a lower
lifetime is expected for GG modules compared to GB modules); ii) the GG modules tend
to experience a higher level of encapsulant discoloration and grid finger degradation than
the GB modules at both sites (a higher level of the degradation rate is expected in GG
modules compared to GB modules); and, iii) the EVA-based modules tend to have a higher
level of discoloration and finger degradation compared to the POE-based modules at both
sites.
ContributorsThayumanavan, Rishi Gokul (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2023
Description
Uncertainty quantification is critical for engineering design and analysis. Determining appropriate ways of dealing with uncertainties has been a constant challenge in engineering. Statistical methods provide a powerful aid to describe and understand uncertainties. This work focuses on applying Bayesian methods and machine learning in uncertainty quantification and prognostics among all the statistical methods. This study focuses on the mechanical properties of materials, both static and fatigue, the main engineering field on which this study focuses. This work can be summarized in the following items: First, maintaining the safety of vintage pipelines requires accurately estimating the strength. The objective is to predict the reliability-based strength using nondestructive multimodality surface information. Bayesian model averaging (BMA) is implemented for fusing multimodality non-destructive testing results for gas pipeline strength estimation. Several incremental improvements are proposed in the algorithm implementation. Second, the objective is to develop a statistical uncertainty quantification method for fatigue stress-life (S-N) curves with sparse data.Hierarchical Bayesian data augmentation (HBDA) is proposed to integrate hierarchical Bayesian modeling (HBM) and Bayesian data augmentation (BDA) to deal with sparse data problems for fatigue S-N curves. The third objective is to develop a physics-guided machine learning model to overcome limitations in parametric regression models and classical machine learning models for fatigue data analysis. A Probabilistic Physics-guided Neural Network (PPgNN) is proposed for probabilistic fatigue S-N curve estimation. This model is further developed for missing data and arbitrary output distribution problems. Fourth, multi-fidelity modeling combines the advantages of low- and high-fidelity models to achieve a required accuracy at a reasonable computation cost. The fourth objective is to develop a neural network approach for multi-fidelity modeling by learning the correlation between low- and high-fidelity models. Finally, conclusions are drawn, and future work is outlined based on the current study.
ContributorsChen, Jie (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Ren, Yi (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2022
Description
This study investigates the energy saving potential of high albedo roof coatings which are designed to reflect a large proportion of solar radiation compared to traditional roofing materials. Using EnergyPlus simulations, the efficacy of silicone, acrylic, and aluminum roof coatings is assessed across two prototype commercial buildings—a standalone retail (2,294 m2 or 24,692 ft2) and a strip-mall (2,090 m2 or 22,500 ft2)—located in four cities: Phoenix, Houston, Los Angeles, and Miami. The performance of reflective coatings was compared with respect to a black roof having a solar reflectance of 5% and a thermal emittance of 90%. A sensitivity analysis was done to assess the impact of solar reflectance and thermal emittance on the ability of roof coatings to reduce surface temperatures, a key factor behind energy savings. This factor plays a crucial role in all three heat transfer mechanisms: conduction, convection, and radiation. The rooftop surface temperature exhibits considerable variation depending on the solar reflectance and thermal emittance attributes of the roof. A contour plot between these properties reveals that high values of both result in reduced cooling needs and a heating penalty which is insignificant when compared with cooling savings for cooling-dominant climates like Phoenix where the cooling demand significantly outweighs the heating demand, yielding significant energy savings. Furthermore, the study also investigates the effects of reflective coatings on buildings that have photovoltaic solar panels installed on them. This includes exploring their impact on building HVAC loads, as well as the performance improvement due to the reduced temperatures beneath them.
ContributorsSharma, Ajay Kumar (Author) / Phelan, Patrick (Thesis advisor) / Neithalath, Narayanan (Committee member) / Milcarek, Ryan (Committee member) / Arizona State University (Publisher)
Created2024
Description
Buildings continue to take up a significant portion of the global energy consumption, meaning there are significant research opportunities in reducing the energy consumption of the building sector. One widely studied area is waste heat recovery. The purpose of this research is to test a prototype thermogalvanic cell in the form factor of a UK metric brick sized at 215 mm × 102.5 mm × 65 mm for the experimental power output using a copper/copper(II) (Cu/Cu2+) based aqueous electrode. In this study the thermogalvanic brick uses a 0.7 M CuSO4 + 0.1 M H2SO4 aqueous electrolyte with copper electrodes as two of the walls. The other walls of the thermogalvanic brick are made of 5.588 mm (0.22 in) thick acrylic sheet. Internal to the brick, a 0.2 volume fraction minimal surface Schwartz diamond (Schwartz D) structure made of ABS, Polycarbonate-ABS (PCABS), and Polycarbonate-Carbon Fiber (PCCF) was tested to see the effects on the power output of the thermogalvanic brick. By changing the size of the thermogalvanic cell into that of a brick will allow this thermogalvanic cell to become the literal building blocks of green buildings. The thermogalvanic brick was tested by applying a constant power to the strip heater attached to the hot side of the brick, resulting in various ∆T values between 8◦C and 15◦C depending on the material of Schwartz D inside. From this, it was found that a single Cu/Cu2+ thermogalvanic brick containing the PCCF or PCABS Schwartz D performed equivalently well at a 163.8% or 164.9%, respectively, higher normalized power density output than the control brick containing only electrolyte solution.
ContributorsLee, William J. (Author) / Phelan, Patrick (Thesis advisor) / El Asmar, Mounir (Committee member) / Milcarek, Ryan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Phase change materials (PCMs) are combined sensible-and-latent thermal energy storage materials that can be used to store and dissipate energy in the form of heat. PCMs incorporated into wall-element systems have been well-studied with respect to energy efficiency of building envelopes. New applications of PCMs in infrastructural concrete, e.g., for mitigating early-age cracking and freeze-and-thaw induced damage, have also been proposed. Hence, the focus of this dissertation is to develop a detailed understanding of the physic-chemical and thermo-mechanical characteristics of cementitious systems and novel coating systems for wall-elements containing PCM. The initial phase of this work assesses the influence of interface properties and inter-inclusion interactions between microencapsulated PCM, macroencapsulated PCM, and the cementitious matrix. The fact that these inclusions within the composites are by themselves heterogeneous, and contain multiple components necessitate careful application of models to predict the thermal properties. The next phase observes the influence of PCM inclusions on the fracture and fatigue behavior of PCM-cementitious composites. The compliant nature of the inclusion creates less variability in the fatigue life for these composites subjected to cyclic loading. The incorporation of small amounts of PCM is found to slightly improve the fracture properties compared to PCM free cementitious composites. Inelastic deformations at the crack-tip in the direction of crack opening are influenced by the microscale PCM inclusions. After initial laboratory characterization of the microstructure and evaluation of the thermo-mechanical performance of these systems, field scale applicability and performance were evaluated. Wireless temperature and strain sensors for smart monitoring were embedded within a conventional portland cement concrete pavement (PCCP) and a thermal control smart concrete pavement (TCSCP) containing PCM. The TCSCP exhibited enhanced thermal performance over multiple heating and cooling cycles. PCCP showed significant shrinkage behavior as a result of compressive strains in the reinforcement that were twice that of the TCSCP. For building applications, novel PCM-composites coatings were developed to improve and extend the thermal efficiency. These coatings demonstrated a delay in temperature by up to four hours and were found to be more cost-effective than traditional building insulating materials.
The results of this work prove the feasibility of PCMs as a temperature-regulating technology. Not only do PCMs reduce and control the temperature within cementitious systems without affecting the rate of early property development but they can also be used as an auto-adaptive technology capable of improving the thermal performance of building envelopes.
The results of this work prove the feasibility of PCMs as a temperature-regulating technology. Not only do PCMs reduce and control the temperature within cementitious systems without affecting the rate of early property development but they can also be used as an auto-adaptive technology capable of improving the thermal performance of building envelopes.
ContributorsAguayo, Matthew Joseph (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Underwood, Benjamin (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2018