Author: Vishal S Sivasankar
Title: Simulation of Polymeric Drop Dynamics: Effect of Photopolymerization, Impact Velocity, and Multi-material Coalescence
Day/Time: March 29, 11:00 am | Location: EGR 2164 (DeWalt Seminar Room
Committee Members:
Dr. Siddhartha Das, Chair/Advisor
Dr. Abhijit Dasgupta
Dr. Amir Riaz
Dr. Eleonora Tubaldi
Dr. Peter Kofinas, Dean’s Representative
Author: Xiaotian Xu
Date: Tuesday, March 28th, 2023, at 1:00 pm
Location: EGR-1131B
Zoom (for the Public Presentation):https://umd.zoom.us/j/7739971299?pwd=L3Z4T1dNTlVUT2kxU241ekwxS2RpZz09
Advisory Committee:
Professor Yancy Diaz-Mercado, Chair/Advisor
Professor Nikhil Chopra
Professor Hosam K. Fathy
Professor Jin-Oh Hahn
Professor Erick Rodriguez-Seda, Special Member
Professor Derek A. Paley, Dean’s Representative
Title of Dissertation: Multi-Agent Spatial Coordination Via Time-Variations In Coverage Control
Abstract:
Coverage control of multi-agent systems (MASs) spatially spreads out a group of agents to form a configuration over a domain of interest. This research investigates the two fundamental elements embedded in coverage control, i.e., the time-varying density function and the time-varying domain, and how these can be leveraged to achieve collaborative controls of MASs. We focus on three problems: first, we abstract a robotic swarm, so it can be controlled as a whole where the robotic team adaptively finds the suitable spatial configuration; second, such abstraction of a MAS is extended to a higher-dimensional embedding for an interactive multi-agent aerial cinematography application; and third, a multi-objective formulation is developed to spatially distribute a MAS and take advantage of its collective effort to persistently cover a space.
In contrast to the coverage with time-varying densities which has been actively studied, we address the coverage control over time-varying domains in the first problem, so the control of a MAS, in terms of its position, scale, shape, etc., is enabled and is simplified into manipulating the domain to be covered directly. The agents coordinate themselves to accommodate the evolution of the domain, even when the domain is evolving fast. A MAS control algorithm, named Swarm Herding, which is built upon the proposed control mechanism is implemented. In pursuit of this approach, contributions are made to the problem of coverage control over time-varying convex and non-convex domains for abstracting the swarm and synthesizing the specialized controllers for every agent in the swarm.
In the second, the abstraction is extended to a hemispherical manifold under the geodesic metric, and it is employed to enable an interactive motion coordinator for multi-robot aerial cinematography. The emphases are on collaborative behavior for multiple unmanned aerial vehicles (UAVs), tracking of a dynamic target, and real-time interaction for aesthetic cinematography objectives. Contributions are made in the design of a distributed interactive framework to provide high-level position instructions for a group of UAVs which addresses the gap in the “one-pilot-many-robot” feature.
In the third problem, a multi-objective coverage control of MASs is formulated to take advantage of the collective effort of a team of mobile sensors to persistently explore a domain of interest. In addition to the standard locational coverage objective, a new perceptional coverage objective is introduced to drive agents around in the domain to gain information. The collaboration between agents is defined not only in terms of exchanging the knowledge of the domain as in previous work but also in terms of inter-agent motion coordination which reduces redundant visits to certain locations by agents. Contributions are made with respect to information exchange with performance guarantees, multi-objective coverage control of MASs with time-varying state-dependent density functions, and analysis of the effects of the multiple objectives.
Author: Sai Etha
Dissertation title: Molecular-scale exploration of interactions between drops and particles with a polymeric layer
Date, time and venue: 3/16/23 from 3-5pm in EGR 2164
Committee Members:
Dr. Siddhartha Das (Chair)
Dr. Taylor J Woehl (Dean’s Representative)
Dr. Pratyush Tiwary
Dr. Amir Riaz
Dr. Avik Dutt
Abstract: Surface-grafted polymer molecules have been extensively employed for surface modifications as they ensure changes to the inherent physical/chemical properties of surface. Bottom-up surface processing with well-defined polymeric structures becomes increasingly important in many current technologies. Polymer brushes, which are polymer molecules grafted to a substrate by its one end at close enough proximity (thereby ensuring that they stretch out like the “bristles” of a toothbrush), provide an exemplary system of materials capable of achieving such a goal. In particular, producing functional polymer brushes with well-defined chemical configurations, densities, architectures, and thicknesses on a material surface has become increasingly important in many fields.
In my dissertation, I employ Molecular Dynamics (MD) simulations to study the interplay of interactions between nanoparticles (NPs), solvent drops and polymer grafted surfaces under various system conditions. This study will help us to understand (1) the wetting dynamics of brush grafted surfaces and the associated brush conformational changes, (2) polymer-insoluble solvophilic NP assembly in brush grafted surfaces and the steric interactions driven establishment of direct contacts between a NP and a polymer layer (highly phobic to the NP), and (3) microphase separation and distillation-like behavior of grafted polymer bilayers interacting with a binary liquid mixture, and the resulting nanofluidic valving behavior of swollen polymer bilayers in a weak interpenetration regime.
Author: Namkyoung Lee
Date: Friday, March 17th, 2023, at 10:30 am
Location: EGR-2164
Committee Members:
Dr. Michael Pecht, Chair / Advisor
Dr. Michael H. Azarian, Co-chair / Co-advisor
Dr. Yunfeng Zhang, Dean’s Representative
Dr. Balakumar Balachandran
Dr. Mark Fuge
Dr. Gregory W. Vogl
Title of Thesis: Interpretable and Speed Adaptive Convolutional Neural Network for Prognostics and Health Management of Rotating Machinery
Abstract:
Faulty rotating machines exhibit vibrational characteristics that can be distinguished from healthy machines using prognostics and health management methods. These characteristics can be extracted using signal processing techniques. However, these techniques require certain inputs, or parameters, before the desired characteristics can be extracted. Setting the parameters requires skill and knowledge, as they should reflect the component geometries and the operational conditions. Using convolutional neural networks for diagnosing faults on a rotating machine eliminates the need for parameter setting by replacing signal processing with mathematical operations in the networks. The parameters that affect the outcomes of the operations are learned from data during the training of the neural networks. The networks can capture characteristics that are related to the health state of a machine, but their operations are not interpretable. Unlike signal processing, the internal operations of the networks have no constraints that guide the networks to transform vibrations into certain information, that is, vibrational characteristics. Without the constraints, there is no basis for understanding the characteristics in terms that can be associated with the physics of failure. The lack of interpretability impedes the physical validation of vibrational characteristics captured by the networks.
This dissertation presents a method for changing the internal operations of a convolutional neural network to emulate a specific type of signal processing known as envelope analysis. Envelope analysis demodulates vibrations to extract vibrational signatures associated with mechanical impact on a defective rolling component. An understanding of envelope analysis, along with knowledge of the geometries of machine components and operational speeds, allows for a physical interpretation of the signatures. The dissertation develops speed adaptive convolutional layers and a rotational speed estimation algorithm to identify defect signatures whose frequency components change as the speed changes. The characteristics that are captured by the developed convolutional neural network are verified through a feature selection process that is designed to filter out physically implausible features. Case studies on three different systems demonstrate the feasibility of using the developed convolutional neural network for the diagnosis.
Author: Tanjebul Alam
Date: Wednesday, January 11, 2023, at 9:30 am
Location: Room EGR-2164
Committee Members:
Research Professor Vikrant Aute, Mechanical Engineering, Chair/Advisor
Professor Bao Yang, Mechanical Engineering
Associate Professor Damena Agonafer, Mechanical Engineering
Title of Thesis: DEVELOPMENT, VALIDATION AND APPLICATION OF RESISTANCE -CAPACITANCE BASED MODELS (RCM) FOR PHASE CHANGE MATERIAL HEAT EXCHANGERS
Abstract:
Latent thermal energy storage use Phase Change Material (PCM) because of its ability to absorb or release a large amount of latent heat within a narrow temperature range. The low conductive heat transfer in PCMs can be improved with thermal enhancement techniques such as the addition of highly conductive metal foams and extended surfaces like fins or periodic metal structures within the PCM domain. High-order modeling tools like Computational Fluid Dynamics (CFD) are widely used for the simulation of different types of PCM heat exchangers (HXs). High computing costs are typically associated with CFD, particularly for the complex transient phase-change processes. This becomes restrictive in some applications such as PCMHX optimization where the conventional process is limited by the computational cost of the high-order physics models. A simulation tool with a faster turnaround is necessary for such cases, even if it comes with a small accuracy penalty. Resistance-capacitance based model (RCM) can be a suitable solution for this type of problem as the model is computationally inexpensive. RCM does not solve for the mass and momentum governing equations as in CFD, but can still predict the PCMHX characteristics with reasonable accuracy, especially for configurations where conduction is the dominant heat transfer mechanism.
This work presents the development of RCMs for four types of PCMHXs which are a rectangular PCM enclosure enhanced with copper foam subject to constant heat flux, a geometry enhanced with 3D lattice structures subject to constant heat flux, a cylindrical PCMHX with annular fins and tube in conjugate heat transfer with single-phase heat transfer fluid and a cylindrical PCMHX with annular fin and tube enhanced with copper foam. In all these geometries the effect due to the flow of molten PCM can be considered negligible and the geometries are regarded as structured. The models were validated against experimental data and compared against CFD models for computational cost and prediction accuracy. Both of the models predicted the energy storage within 0.8% of the experimental data for the rectangular PCM enclosure enhanced with copper foam. For the cylindrical PCMHX with annular fins, the maximum RMSE for average PCM temperature prediction was found to be 0.62K for CFD and 0.7K for RCM. These results show that RCM can predict the average temperature profile and energy storage up to 5 orders of magnitude faster than CFD while having negligible prediction deviation. The validated model for annular finned PCMHX is used with a multi-objective genetic algorithm to optimize a PCMHX integrated with a domestic water heater. Additionally, thermal Ragone plots were generated to compare different designs at various operating conditions which can be used for optimal design selection.
Author: Mohammad Rubyet Islam
Date: 13th of December 2022, @12:00 PM to 2 PM
Location: DeWalt Seminar Room (EGR-2164), Building 088, 2164 Martin Hall
Committee Members:
Dr. Peter A Sandborn, Advisor and Chair
Dr. William Regli, Dean’s Representative
Dr. Mohammad Modarres
Dr. F. Patrick McCluskey
Dr. Abhijit Dasgupta
Title of Paper: “DYNAMIC PROGNOSTIC HEALTH MANAGEMENT FOR RESPONSE TIME BASED REMAINING USEFUL LIFE PREDICTION OF SOFTWARE SYSTEMS”
Abstract:
Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the future point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). PHM has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software applications. While software does not decay over time, it can degrade over release cycles. Software degradation is a common problem faced by legacy systems. Today, software health management is confined to diagnostic assessments that identify problems. In contrast, prognostic assessment potentially indicates what problems will become detrimental to the operation of the system in the future. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data – none of which can predict an RUL for software.
This dissertation addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this dissertation addresses how PHM can be used to make decisions for software systems such as version update/upgrade, module changes, rejuvenation, maintenance schedules, and abandonment. This dissertation presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed in this dissertation has been validated by comparing actual data generated using test beds. Statistical validation (regression validation) has also been carried out. A case study is presented based on publicly available data for the Bugzilla application. Controlled test beds for multiple Bugzilla releases are prepared to formulate standard staging environments to populate relevant data. This case study demonstrates that PHM concepts can be applied to software systems, and RUL can be calculated to make decisions on software management.
Author: Abhishek Ram
Title: An Application-Specific Approach for Qualification Procedure Development
Date & Time: Friday, December 02, 2022 at 9:00 AM
Location: EGR-2164 (Martin Hall, DeWALT Seminar Room)
Committee Members:
Dr. Diganta Das (Chair)
Dr. Abhijit Dasgupta
Dr. Peter Sandborn
Dr. Michael Azarian
Abstract:
Qualification is a process that demonstrates whether a product meets or exceeds specified requirements. Testing and data analysis performed within a qualification procedure should verify that products satisfy those requirements, including reliability requirements. Most of the electronics industry qualifies products using procedures dictated within qualification standards. A review of common qualification standards reveals that those standards do not consider customer requirements or the product physics-of-failure in that intended application. As a result, qualification, as represented in the reviewed qualification standards, would not meet our definition of qualification for reliability assessment.
This thesis provides an application-specific approach for developing a qualification procedure that accounts for customer requirements, product physics-of-failure, and knowledge of product behavior under loading. This thesis provides a revamped approach for developing a life cycle profile that accounts for loading throughout manufacturing/assembly, storage and transportation, and operation. The thesis also discusses identifying variations in the life cycle profile that may arise throughout the product lifetime and methods for estimating loads. This updated approach for developing a life cycle profile supports better failure prioritization, test selection, and test condition and duration requirement estimation.
Additionally, this thesis introduces the application of diagnostics and prognostics techniques to analyze real-time data trends while conducting qualification tests. Diagnostics techniques identify anomalous behavior exhibited by the product, and prognostics techniques forecast how the product will behave during the remainder of the qualification test and how the product would have behaved if the test continued. As a result, combining diagnostics and prognostics techniques can enable the prediction of the remaining time-to-failure for the product undergoing qualification, reducing the time required to determine if the product would fail the qualification test. Several ancillary benefits related to an improved testing strategy and support of a prognostics and health management system in operation also arise from applying prognostics and diagnostics techniques to qualification.
Author: Timothy Kim
Date: Thursday, November 17, 2022 at 2:00 pm
Location: Martin Hall, Room EGR-4164B (CEEE Meeting Room)
Committee Members:
Research Professor Yunho Hwang, Chair
Professor Bao Yang
Professor Jungho Kim
Title of Paper: “ENABLING CO2 ISOTHERMAL COMPRESSION USING PISTON AND INTEGRATED GAS COOLER”
Abstract:
New avenues of decreasing environmental impacts and increasing the efficiency of HVAC systems are constantly being explored in the race to reduce carbon emissions and global warming. These new avenues have led to the exploration of the use of carbon dioxide as a refrigerant in refrigeration applications. Many researchers have also investigated ways to reduce the power consumption of compressors, which is typically the main source of power draw for HVAC systems. One theoretical process to achieve this is through isothermal compression.
This thesis explores the idea of isothermally compressing CO2 by using a liquid piston and integrated gas cooler to achieve higher efficiencies with this trans-critical cycle. A test facility was designed, sized, constructed and calibrated to emulate suction and discharge conditions of a typical CO2 system for air conditioning applications. A prototype of the liquid piston and integrated gas cooler chamber was designed and constructed as well. A simulation model was built in Engineering Equation Solver in order to properly design the gas cooler chamber. Other critical components have been carefully chosen to ensure smooth operation of the system.
Results show isothermal efficiencies up to 82.7% during steady state operation and an isothermal efficiency of 91.2% during steady state operation with the additional help of evaporative cooling. Comparing this to other conventional compressors give up to 34.2% improvement in the isothermal compressor efficiency. These results show sufficient performance to warrant the design of a fully working prototype despite efficiency/capacity tradeoffs in the system. Challenges had been encountered such as the loss of refrigerant through the liquid piston, which will be accounted for in the next prototype. Discussion of the next prototype include the use of a double acting piston and smaller tubed fractal heat exchanger design.
Author: Zachary Grant
Date: Thursday, November 17, 2022 at 3:30 pm
Location: Martin Hall, Room EGR-4164B (CEEE Meeting Room)
Committee Members:
Research Professor Yunho Hwang, Chair
Professor Bao Yang
Professor Michael Ohadi
Title of Paper:“MODELING OF HVAC CONFIGURATIONS FOR DE-CARBONIZATION IN A MID-SIZE HOSPITAL”
Abstract:
As the threat of climate change becomes more imminent, there has been increasing emphasis on technologies that reduce carbon emissions in the HVAC sector. The clear path forward given existing technologies is electrification since electricity production has future potential to become cleaner. In terms of building type, high ventilation requirements and near continuous occupancy make healthcare facilities some of the highest energy users. In these facilities, HVAC equipment is running all day and night with little change. Conventional HVAC equipment such as a boiler are proven to consume more energy than heat pump systems. More specifically, the Variable Refrigerant Flow (VRF) heat pump and the Ground Source Heat Pump (GSHP) are areas of ongoing research. This analysis included the creation of whole-building energy models using EnergyPlus and OpenStudio to compare the energy consumption for these heat pump configurations as well as some cheaper electrification alternatives. The results suggested that the GSHP system possessed the greatest potential for energy savings and thus decarbonization given its higher efficiency during times of extreme ambient temperatures compared to other options.
Author: Rishi Roy
Date: Monday, November 7th, 2022 at 10 am.
Location: Martin Hall, Room EGR 2164
Committee Members:
Professor Ashwani K. Gupta, Chair
Professor Stanislav I. Stoliarov
Professor Bao Yang
Professor Nikhil Chopra
Professor Kenneth H. Yu, Dean’s Representative
Title of Paper: “Investigation of Swirl Distributed Combustion with Experimental Diagnostics and Artificial Intelligence Approach”.
Abstract:
Swirl Distributed Combustion was fundamentally investigated with experimental diagnostics and predictive analysis using machine learning and computer vision techniques. Ultra-low pollutants emission, stable operation, improved pattern factor, and fuel flexibility make distributed combustion an attractive technology for potential applications in high-intensity stationary gas turbines. Proper mixing of inlet fresh air and hot products for creating a hot and low-oxygen environment results in a distributed thick reaction zone without hotspots found in conventional diffusion flames (thin reaction front) leading to reduced NOx and CO emissions. The focus of this dissertation is to develop a detailed fundamental understanding of distributed combustion in a lab-based swirl combustor mimicking gas turbine can combustor at moderate heat release intensities in the range 5.72- 9.53 MW/m3-atm using various low-carbon gaseous fuels such as methane, propane, hydrogen-enriched fuels. Moderate thermal intensities helped to understand the fundamental aspects such as reduction of flame fluctuation, mitigation of thermo-acoustic instability, flame shape evolution, flow field behavior, turbulence characteristics, variation of Damkӧhler number, vortex propagation, flame blowoff, and pollutant and CO2 emission reduction with gradual mixture preparation. Efforts were made to obtain the volumetric distribution ratio, evolution of flame shape in terms of OH* radical imaging, variation of flame standoff, thermal field uniformity, and NO and CO emissions when the flame transitions to distributed reaction zone. Further investigation was performed to study the mitigation of flame thermo-acoustics and precession vortex core (PVC) instabilities in hydrogen-enriched methane fueled (with H2 = 0, 10, 20, 40%vol.) swirl distributed combustion compared to swirl air combustion using the acoustic pressure and qualitative heat release fluctuation data at different CO2 dilution levels with and without air preheats. Proper orthogonal decomposition (POD) technique was utilized to visualize the appearance of dynamic coherent structures in reactive flow fields and reduction of fluctuation energy. Vortex shedding was found responsible for the fluctuation in swirl air combustion while no significant flame fluctuation was observed in distributed combustion. The study of lean blowoff in distributed combustion showed a higher lean blowoff equivalence ratio with gradual increase in heat release intensity, which was attributed to higher instability due to enhanced inlet turbulence. Extension of lean blowoff (ϕLBO) was observed with gradual %H2 which showed decrease of lean blowoff equivalence ratio in distributed reaction zones. Examination of non-reactive flow fields with particle image velocimetry (PIV) demonstrated higher RMS velocity fluctuation leading to healthy turbulence and higher Reynolds stress found in distributed reaction flow cases signifying enhanced mixing characteristics. Measurement of NO and CO emission at different mixture preparation levels (with CO2/ N2 dilutions) exhibited significant reduction in NO (single digit only) and CO emissions compared to swirl air combustion due to mitigation of spatial hotspots and temperature peaks, and uniform stoichiometry. Finally, the use of machine learning and computer vision techniques was investigated for software-based prediction of combustion parameters (pollutants and flame temperature) and feature-based recognition of distributed combustion regimes. The primary goal of using artificial intelligence is to reduce the time of experimentation and frequent manual interference during experiments in order to enhance overall accuracy by reducing human errors. These results will help in developing data-driven smart-sensing of combustion parameters for advanced gas turbine applications and reduce the dependence on rigorous experimental trials.