Announcements

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.

Author: Beihan Zhao
Date: Friday, November 4th, 2022 at 1:00pm
Location: Martin Hall, Room EGR-2162

Committee Members:

Professor Siddhartha Das, Chair/Advisor
Professor Abhijit Dasgupta, Co-Chair/Advisor
Professor Hugh Alan Bruck
Professor Miao Yu
Professor Taylor J Woehl
Professor Peter Kofinas, Dean’s Representative

Title of Paper: “Explorations of carbon-nanotube-graphene oxide inks: printability, radio-frequency and sensor applications, and reliability”

Abstract:

Carbon-Nanotube (CNT) is a novel functional material with outstanding electrical and mechanical properties, with excellent potential for various kinds of industrial applications. Additive manufacturing or 3D printing of CNT-based materials or inks has been studied extensively, and it is vital to have a thorough understanding of the fluid mechanics and colloidal science of CNT-based inks for ensuring optimum printability and the desired functionality of such CNT-based materials.

In this dissertation, a custom-developed syringe-printable CNT-GO ink (GO: Graphene Oxide) is introduced and the fluid mechanics and colloidal science of this ink as well as the different devices (e.g., temperature sensor, humidity sensor, and RF antenna) fabricated with this ink are studied. The following topics are discussed in this dissertation: (1) the application and printability (in terms of the appropriate fluid mechanics and colloidal science) of CNT-based inks; (2) development of temperature sensors with CNT-GO inks; (3) development of humidity sensors with CNT-GO inks; (4) development of RF patch antenna with CNT-GO inks; and (5) evaporation-driven size-dependent nano-microparticulate three-dimensional deposits (CNTs serve as one type of nanoparticle examined in this part of the study). In Chapter 1 of this dissertation, a literature review is conducted on the application of CNT-based inks and the fluid mechanics and colloidal science issues dictating the printability and performance of such CNT-based inks. The problem statement and overall research plan are also introduced in this chapter. In Chapter 2, the development of our custom CNT-GO ink is introduced. Detailed material selection and the mechanism of shape-dependent arrest of coffee-stain effect, which ensured that the printable ink led to uniform deposition, are discussed in this chapter. Temperature sensor prototypes printed with the CNT-GO inks are also presented in Chapter 2.

From Chapter 3 to Chapter 5, the performances of our CNT-GO based flexible temperature sensor, humidity sensor, and patch antenna prototypes are discussed. The ink printability on flexible thin PET films is studied, and a straightforward ‘peel-and-stick’ approach to use the CNT-trace (or patch)-bearing PET films on surfaces of widely varying wettabilities and curvatures as different prototypes is introduced. Excellent temperature and humidity sensitivity of our CNT-GO based sensors are presented in Chapter 3 and Chapter 4, and the potential of this CNT-GO material for fabrication of ultra-wideband (UWB) patch antennas is discussed in Chapter 5. Furthermore, the stability and reliability of these printed CNT-GO-based prototypes are also explored.

In previous Chapters, the printed CNT-GO patterns were cured by evaporation-mediated deposition on flat substrates (i.e., 2D deposition spanning in x and y directions). This motivated the extension of the physics to the 3rd dimension and probing of particle deposition on a 3D substrate and particle deposition in all x, y, and z directions. Therefore, in Chapter 6, we perform an experiment to demonstrate this kind of possibility using three kinds of micro-nanoparticle-laden water-based droplets (i.e. coffee particles, silver nanoparticles, and CNTs). These droplets were first deposited at the bottom of an un-cured PDMS film; these droplets were lighter than the PDMS and hence, they rose to the top of the PDMS where they could have either attained a Neuman like state or simply remained as an undeformed spherical drop with the top of the drop breaching the air-liquid-PDMS interface. The calculations based on air-water, water-PDMS, and air-PDMS surface tension values confirmed that the Neuman like state was not possible, and the droplets were likely to retain their undeformed shapes as they breached the air-PDMS interface. The timescale differences between the fast PDMS curing and the slower droplet evaporation, led to the formation of spherical shape cavities inside the PDMS after completion of the curing, and allowed evaporation-driven deposition to occur in all x, y, and z directions inside the cavity, with the exact nature of the deposition being dictated by the sizes of the particles (as confirmed by the experiments conducted with coffee particles, silver nanoparticles, and CNTs).

Finally, in Chapter 7, the major contributions of this dissertation and proposed future studies related to this dissertation work are listed.

Author: James Tancabel
Date: Friday, November 4th, 2022 at 1:00pm
Location: Martin Hall, Room EGR-4164B

Committee Members:

Professor Reinhard Radermacher, Chair/Advisor
Professor James Baeder, Dean’s Representative
Associate Professor Johan Larsson
Professor Jungho Kim, 
Professor Jelena Srebric
Research Professor Vikrant Aute

Title of Paper: “MULTI-PHYSICS MODELING OF THERMAL-HYDRAULIC AND MECHANICAL PERFORMANCE, AIRFLOW MALDISTRIBUTION, AND DEHUMIDIFCATION FOR HIGH PERFORMANCE, REDUCED CHARGE AIR-TO-REFRIGERANT HEAT EXCHANGERS WITH SHAPE-OPTIMIZED TUBES”

Abstract:

Air-to-fluid Heat eXchangers (HX) are fundamental components of many systems we encounter in our daily lives, from Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems to electronics cooling, automotive, power plants, and aviation applications. The importance of HXs is evident in the level of investment devoted to HX innovation in recent years. While current state-of-the-art HXs have adequately addressed past challenges, ever-increasing energy demands and increasingly stringent global energy standards require novel tools and methodologies which can quickly and efficiently develop the next generation of high-performance HXs.

In recent years, advancements in computational tools and advanced manufacturing technologies have enabled engineers to consider small characteristic diameter HX tubes with novel shapes and topologies which were not feasible even a decade ago. These small diameter, shape-optimized tubes have been shown to perform the same job as existing HXs while offering significant and desirable improvements in performance metrics such as envelope volume, face area, weight, and refrigerant charge. However, the structural integrity of shape-optimized tubes was often guaranteed by utilizing conservative tube thicknesses to ensure equipment safety, prevent   refrigerant leakages, and satisfy product qualification requirements, resulting in increased material consumption and manufacturer costs while reducing the likelihood of industry acceptance for the new technology.

Additionally, the actual HX operating conditions are often different from design conditions, resulting in significant performance degradations. For example, novel HX design is typically assumes uniform normal airflow on the HX face area even though HXs in HVAC&R applications rarely experience such flows, and compact HXs have been shown to experience water bridging under dehumidification conditions, which greatly impacts HX performance.

This research sheds light on the next generation of air-to-refrigerant HXs and aims to address several practical challenges to HX commercialization such as novelty, manufacturing, and operational challenges through the use of comprehensive multi-physics and multi-scale modeling. The novelty of this research is summarized as follows:

i. A new, comprehensive and experimentally validated air-to-refrigerant HX optimization framework with simultaneous thermal-hydraulic performance and mechanical strength considerations for novel, non-round, shape- and topology-optimized tubes capable of optimizing single and two-phase HX designs for any refrigerant choice and performance requirement with significant engineering time savings compared to conventional design practices. The framework was exercised for a wide range of applications, resulting in HXs which achieved greater than 20% improved performance, 20% reductions in size, and 25% reductions in refrigerant charge.

ii. Development of a fundamental understanding of performance degradation for HXs with shape- and topology-optimized tubes under typical HX installation configurations in practical applications such as inclined and A-type configurations. New modeling capabilities were integrated into existing HX modeling tools to accurately predict the airflow maldistribution profiles for HXs with shape- and topology-optimized tubes without the need for computationally-expensive CFD simulations.

iii. Development of a framework to model and understand the impact of moist air dehumidification on the performance of highly compact HX tube bundles which utilize generalized, non-round tubes. Correlations for Lewis number were developed to understand whether traditional HX dehumidification modeling assumptions remained valid for new HXs with generalized, non-round tube bundles. Such an understanding is critical to accurately and efficiently modeling HX performance under dehumidifying (i.e., wet-coil) conditions.

Author: Debapriya Bhattacharjee
Date: Friday, November 4th, 2022 at 9:30am
Location: Martin Hall, Room EGR-2164

Committee Members:

Dr. Hosam K Fathy, Chair
Dr. Perinkulam Krishnaprasad, Dean’s Representative 
Dr. Balakumar Balachandran
Dr. Christopher Vermillion
Dr. Eleonora Tubaldi

Title of Paper: “Direct Nonlinear Trajectory Optimization and State Estimation for a Tethered Underwater Energy Harvesting Kite”

Abstract:

This dissertation addresses the coupled challenges of state estimation and trajectory optimization for a marine hydro-kinetic energy harvesting kite. The optimization objective is to maximize the kite’s average mechanical power output. This work is motivated by the potential of “pumping-mode” tethered kites to provide attractive levelized costs of electricity, especially when cross-current motion is exploited to maximize energy harvesting. In ”pumping-mode” kites, the kite is tethered to a platform carrying a motor/generator, and electricity generation is achieved by reeling the kite out and in at high and low tether tension levels, respectively.

Marine hydro-kinetic (MHK) systems are heavily influenced by wind energy systems. In both contexts, for instance, tethered kites can be used for electricity generation instead of stationary turbines. Similar to airborne wind energy (AWE) systems, the power production capacities of MHK kites are heavily influenced by their flight trajectories. While trajectory optimization is a well-established research area for AWE systems, it is a nascent but growing field for MHK kites. Moreover, although both AWE and MHK kites have the potential to benefit from trajectory optimization, the lessons learned from AWE systems might not be directly applicable to MHK kites, since MHK systems are often close to neutral buoyancy whereas AWE systems are not. Finally, there is little work in the literature that co-optimizes the spooling and cross-current trajectories of a pumping-mode MHK kite.

The first contribution of this dissertation is to explore the simultaneous optimization of the cross-current trajectory and the spooling motion of a pumping-mode kite using direct transcription. While the results highlight the degree to which simultaneous optimization can be beneficial for these systems, they also motivate the need for a solution approach that satisfies the constraints imposed by the kite dynamics exactly, as opposed to approximately. This leads to the second contribution of this dissertation, namely, finding an analytic solution to the inverse dynamics of the MHK kite, i.e., mapping a desired combination of kite position, velocity, and acceleration onto the corresponding actuation inputs. The dissertation then proceeds to its third contribution, namely, solving the kite trajectory optimization problem based on the above exact solution of the kite’s inverse dynamics. The resulting simulation provides more realistic optimization results. However, all of the above work focuses on the special case where the free-stream fluid velocity is known and spatio-temporally constant. This motivates the fourth and final contribution of this dissertation, namely, the development of an unscented Kalman filter for simultaneously estimating both the kite’s state and the free-stream fluid velocity. One interesting outcome of the estimation study is the finding that simple unscented Kalman filtering is not able to estimate the fluid velocity accurately without the direct measurement of the attitude of the kite. 

Author: Ghazal Arabidarrehdor
Date: Friday, November 4th, 2022 at 12:00pm
Location: Martin Hall, Room EGR-2164

Committee Members:

Dr. Jin-Oh Hahn, Associate Professor, Chair
Dr. Daniel Butts, Associate Professor, Dean’s Representative
Dr. Nikhil Chopra, Professor
Dr. Hosam Fathy, Professor
Dr. Yancy Mercado-Diaz, Assistant Professor
Dr. Eleonora Tubaldi, Assistant Professor 

Title of Paper: “Mathematical Models and Novel Biomarkers toward Optimization of Burn Resuscitation”

Abstract:

Extensive burn injury is not only devastating but also a significant challenge for healthcare providers. Following a chain of inflammatory responses post-burn, significant amounts of plasma shift from the vascular compartment into the tissues, simultaneously posing the risks of hypovolemic shock and edema. Standard burn resuscitation protocols aim to replace the lost blood volume while not exacerbating the edema through hourly-titrated intravenous fluid infusion. Due to the significant variability in treatment efficacy, there is a substantial ongoing effort to optimize and individualize the burn resuscitation protocols. In this work, we aim to contribute to this effort by (i) developing a platform for the virtual evaluation of burn resuscitation protocols and (ii) identifying biomarkers to guide fluid resuscitation effectively.

The first part of this work presents a mathematical model of burn injury and resuscitation, which can be used for the development and non-clinical testing of burn resuscitation protocols and algorithms, as well as to garner knowledge and intuition into this complex pathophysiology. Our mathematical model consists of a multi-compartmental model of blood volume kinetics, a hybrid mechanistic-phenomenological model of kidney function, and novel lumped-parameter models of burn-induced perturbations in volume kinetics and renal function. We examined our mathematical model’s prediction accuracy and reliability using a rich dataset from 16 sheep with extensive burn injuries and clinical data from 233 real-world burn patients.

The second part of this work presents the extension of the mathematical model to incorporate the cardiovascular and renin-angiotensin-aldosterone system, as well as detailed descriptions of the kidney’s blood volume and blood pressure control in the context of renal function. This extension was motivated by the importance of cardiovascular monitoring in the critical care of burn injury patients. We trained and validated the extended mathematical model for three species: eight sheep subjects and 15 swine subjects with rich cardiovascular and volume kinetics data and 120 human subjects with demographic and urinary output (UO) data. To the best of our knowledge, our mathematical model may be the first of its kind, which is extensively validated for use as a digital twin to replicate realistic burn patients and replace standard large mammal pre-clinical testing of burn resuscitation protocols.

The third part of this work presents the identification of biomarkers capable of guiding, optimizing, and individualizing burn resuscitation. The UO, the most common endpoint used to titrate burn resuscitation fluid doses, has many limitations as a single variable. Hence, this work aimed to find convenient and reliable biomarkers from arterial blood pressure (ABP) waveform to complement UO in guiding burn resuscitation. Pulse pressure variation (PPV), systolic pressure variation (SPV), and stroke volume variation (SVV) are dynamic indices derived from ABP that have shown promise in hemorrhage resuscitation but are not investigated for different resuscitation paradigms for burn injury. We observed the longitudinal behavior of PPV, SPV, and SVV for 21 porcine subjects with 40% burn injury, which were each either under-resuscitated, adequately resuscitated; or deliberately over-resuscitated. We investigated the features’ potential in tracking reference cardiac output (CO) and stroke volume (SV) via linear regression and correlation analysis. PPV, SPV, and SVV showed plausible and statistically different trends for different paradigms. While they performed just as well as UO in tracking CO and SV, their inherent advantage of being available in real-time and their disagreement with UO in determining the subject status suggest that they may potentially complement UO in the hemodynamic assessment of burn patients.

Author: Randall Kania

Date: Friday, October 28th, 2022 at 11:00 am

Location: Martin Hall, Room EGR-2164

Committee Members:

Professor Shapour Azarm, Chair
Professor Balakumar Balachandran
Professor Jin-Oh Hahn
Professor Jefferey Herrmann
Professor P.K. Kannan, Dean’s Representative

Title of Paper: “SINGLE- AND MULTI-OBJECTIVE FEASIBILITY ROBUST OPTIMIZATION UNDER INTERVAL UNCERTAINTY WITH SURROGATE MODELING”

Abstract: 

This dissertation presents new methodsfor solving single- and multi-objective
optimization problems when there is uncertainty in the values of decision variables and/or
parameters.The uncertainty in these problems is considered to come from sources with no
known or assumed probability distribution, bounded only by an interval. The goal is to obtain a single solution (for single-objective optimization problems) or multiple solutions (for multi-
objective optimization problems) that are optimal and “feasibly robust”. A feasibly robust solution is one that remainsfeasible for all values of uncertain parameters within the uncertainty
interval. Obtaining such a solution can become computationally costly and require many
function calls.To reduce the computational cost, the presented methods use surrogate modeling
to approximate the functions in the optimization problem. This dissertation aims at addressing several key research questions.

The first Research Question (RQ1) is: How can the computational cost for solving single-objective robust optimization problems be enhanced with surrogate modelling when compared to previous work? RQ2 is: How can the computational cost of solving bi-objective robust optimization problems be improved by using surrogates in concert with a Bayesian optimization technique when compared to previous work? And RQ3 is: How can surrogate modeling be leveraged to make multi-objective robust optimization computationally less expensive when compared to previous work?

In addressing RQ1, a new single-objective robust optimization method has been developed with improvements over an existing method from the literature. This method uses a deterministic, local solver, paired with a surrogate modelling technique for finding worst-case scenario of parameter configurations. Using this single-objective robust optimization method, improved scalability and robust feasibility were demonstrated. The second method presented solves bi-objective robust optimization problems under interval uncertainty by introducing a relaxation technique to facilitate combining iterative robust optimization and Bayesian optimization techniques. This method showed improved feasibility robustness and scalability over existing methods. The third method presented in this dissertation extends the current
literature by considering multiple (beyond two) competing objectivesfor surrogate robust
optimization. Increasing the number of objectives adds more dimensions and complexity to the
search for solutions and can greatly increase the computational costs. In the third method, the
robust optimization strategy from the bi-objective second method was combined with a new
Monte Carlo approximated method. The key contributions in this dissertation are 1) a new single-objective robust optimization method combining a local optimization solver and surrogate modelling for
robustness, 2) a bi-objective robust optimization method that employs iterative Bayesian optimization technique in tandem with iterative robust optimization techniques, and 3) a new acquisition function for robust optimization in problems of more than two objectives.