Announcements

Name: Sebastian Antonio Romo Duenas

Defense Date: June 21st, 2022 at 1pm.

Location: Glenn Martin Hall, EGR-2162.

Committee Members:

Jelena Srebric, Ph.D. (Advisor/Chair)

Reinhard Radermacher, Ph.D.

Bao Yang, Ph.D.

Dongxia Liu, Ph.D.

Peter Sunderland, Ph.D. (Dean’s Representative)

Title: A Validated Modeling Framework for Performance Analyses of Experimental and Proven Desalination Technologies

Abstract:

There is a wide array of desalination methods available for treating water at different salinities and production rates, but there are no systemic approaches on how to directly compare performance of different desalination systems. Existing comparison efforts focus solely on isolated performance metrics for a single desalination system, resulting in segregated case studies and/or incomparable systems. Numerical models for desalination systems can bridge this gap as they can take account of specific deployment needs. However, models in the literature are not mutually compatible, and they seldom disclose all the parameters or equations necessary for development and validation. This dissertation conceives a cross-comparison enabling simulation framework for the most relevant desalination processes. To achieve this, modeling approaches and thermophysical property correlations are curated from volumes of literature and used to create metamodels for six relevant desalination methods. The models are integrated into a simulation framework based on parameter hierarchies imposed in the model structures. The simulation suite is validated with data from the literature and actual operational data from desalination facilities in the field. 

The results show that the cross-comparison across equal parameter hierarchies is possible for all desalination technologies. A comparative analysis between the dominant technologies in the thermal and molecular transport families, Multi-Effect Distillation (MED) and Reverse Osmosis (RO), respectively, shows that energy intensity in MED is an order of magnitude greater for equivalent operational conditions, but actual operational costs are comparable. The models are further refined to reflect conditions from actual systems in the field and an iterative sampling algorithm is developed to find plausible operation scenarios given the scarce data from the field. This method achieves excellent agreement with data from four desalination plants with percent differences ranging between 2.5% and 9.3%. Furthermore, the results identify two plants performing 20% below their theoretically achievable recovery. Apart from evaluating existing deployments, the simulation suite helps identify a niche in the operational map of existing desalination methods characterized by high recovery rates and high feed salinities that is generally unfulfilled by conventional desalination.

Name: Ali Tivay

Committee Members: 

Professor Jin-Oh Hahn, Chair/Advisor
Professor Perinkulam Krishnaprasad, Dean’s Representative
Professor Hosam Fathy
Professor Nikhil Chopra
Professor Yancy Diaz-Mercado

Date: Thursday, June 16th, 2022

Time: 1:00 PM

NOTE: This defense will take place via Zoom: https://umd.zoom.us/j/3546982448?pwd=ZGFLRUR0MVFlL3B4Wm5PZFgraGxIdz09

Title: Inference-Based Modeling, Monitoring, and Control Algorithms for Autonomous Medical Care

Abstract: Autonomous medical care systems are relatively recent developments in biomedical research that aim to leverage the vigilance, precision, and processing power of computers to assist (or replace) humans in providing medical care to patients.  Indeed, past research has demonstrated initial promise for autonomous medical care in applications related to anesthesia, hemodynamic management, and diabetes management, to name a few.  However, many of these technologies yet do not exhibit the maturity necessary for widespread real-world adoption and regulatory approval.  This can be attributed, in part, to several outstanding challenges associated with the design and development of algorithms that interact with physiological processes: Ideally, an autonomous medical care system should be equipped to exhibit (i) transparent behavior, where the system’s perceptions, reasoning, and decisions are human-interpretable; (ii) context-aware behavior, where the system is capable of remaining mindful of contextual and peripheral information in addition to its primary goal; (iii) coordinated behavior, where the system can coordinate multiple actions in synergistic ways to best achieve multiple objectives; (iv) adaptable behavior, where the system is equipped to identify and adapt to variabilities that exist within and across different patients; and (v) uncertainty-aware behavior, where the system can handle imperfect measurements, quantify the uncertainties that arise as a result, and incorporate them into its decisions.  As these desires and challenges are specific to autonomous medical care applications and not fully explored in past research in this area, this dissertation presents a sequence of methodologies to model, monitor, and control a physiological process with special emphasis on addressing these challenges.  For this purpose, first, a collective variational inference (C-VI) method is presented that facilitates the creation of personalized and generative physiological models from low-information and heterogeneous datasets.  The generative physiological model is of special importance for the purposes of this work, as it encodes physiological knowledge by reproducing the patterned randomness that is observed in physiological datasets.  Second, a population-informed particle filtering (PIPF) method is presented that fuses the information encoded in the generative model with real-time clinical data to form perceptions of a patient’s states, characteristics, and events.  Third, a population-informed variational control (PIVC) method is presented that leverages the generative model, the perceptions of the PIPF algorithm, and user-defined definitions of actions and rewards in order to search for optimal courses of treatment for a patient.  These methods together form a physiological decision-support and closed-loop control (PCLC) framework that is intended to facilitate the desirable behaviors sought in the motivations of this work.  The performance, merits, and limitations of this framework are analyzed and discussed based on a clinically-important case study on fluid resuscitation for hemodynamic management.

Name: Ali Berk Kahraman

Date: June 10th at 10:00 AM; NOTE: This defense will be taking place virtually.

Zoom Link: https://umd.zoom.us/j/4487374796  Meeting ID: 448 737 4796

Committee Members:

Associate Professor Johan Larsson (Chair/Advisor)

Professor James Baeder

Asst. Professor Cristoph Brehm

Professor James Duncan

Professor Arnaud Trouve (Dean’s Representative)

Title of dissertation: Adaptivity in Wall-Modeled Large Eddy Simulation

Abstract: In turbulence-resolving simulations, smaller eddies account for most of the computational cost. This is especially true for a wall-bounded turbulent flow, where a wall-resolved large eddy simulation might use more than 99% of the computing power to resolve the inner 10% of the boundary layer in realistic flows. The solution is to use an approximate model in the inner 10% of the boundary layer where the turbulence is expected to exhibit universal behavior, a technique generally called wall-modeled large eddy simulation. Wall-modeled large-eddy simulation introduces a modeling interface (or exchange location) separating the wall-modeled layer from the rest of the domain. The current state-of-the-art is to rely on user expertise when choosing where to place this modeling interface, whether this choice is tied to the grid or not. This dissertation presents three post-processing algorithms that determine the exchange location systematically.

Two algorithms are physics-based, derived based on known attributes of the turbulence in attached boundary layers. These algorithms are assessed on a range of flows, including flat plate boundary layers, the NASA wall-mounted hump, and different shock/boundary-layer interactions. These algorithms in general agree with what an experienced user would suggest, with thinner wall-modeled layers in nonequilibrium flow regions and thicker wall-modeled layers where the boundary layer is closer to equilibrium, but are completely ignorant to the cost of the simulation they are suggesting.

The third algorithm is based on the sensitivity of the wall-model with the predicted wall shear stress and a model of the subsequent computational cost, finding the exchange location that minimizes a combination of the two. This algorithm is tested both a priori and a posteriori using an equilibrium wall model for the flow over a wall-mounted hump, a boundary layer in an adverse pressure gradient, and a shock/boundary-layer interaction. This third algorithm also produces exchange locations that mostly agree with what an experienced user would suggest, with thinner layers where the wall-model sensitivity is high and thicker layers where this sensitivity is low. This suggests that the algorithm should be useful in simulations of realistic and highly complex geometries.

Name: Parham Dehghani

Title: BURNING EMULATIONS OF CONDENSED PHASE FUELS ABOARD THE INTERNATIONAL SPACE STATION

Committee Members:

Dr. Peter Sunderland, Chair
Dr. James G. Quintiere
Dr. John L. deRis
Dr. Arnaud Trouve
Dr. Reinhard Radermacher
Dr. Christopher Cadou, Dean’s Representative

Date: 6/7/2022
Time: 11:00AM
Location: 3106B, J.M. Patterson Building

Zoom Link: https://umd.zoom.us/j/9827241119

Abstract:

Little is known about the fire hazards of solids and liquids in microgravity. Ground-based tests are too short to overcome ignition transients and testing dozens of condensed fuels in orbit is prohibitively expensive. Burning rate emulation is one way to address this gap. It involves emulating condensed fuels with gases using a porous burner with embedded heat flux gages. This is a study of microgravity burning rate emulation aboard the International Space Station. The burner had porous round surfaces with diameter of 25 mm. The fuel mixture was gaseous ethylene, and it was diluted with various amounts of nitrogen. The resulting heats of combustion were 15 – 47.2 kJ/g. The flow rate, oxygen concentration in the ambient, and pressure were varied. Heat flux to the burner was measured with two embedded heat flux gages and a slug calorimeter. The effective heat of gasification was determined from the heat flux divided by the fuel flow rate. Radiometers provided the radiative loss fractions. A dimensional analysis based on radiation theory yielded a relationship for radiative loss fraction. RADCAL, a narrow-band radiation model, yielded flame emissivities from the product concentrations, temperature, flame length, and pressure. Previously published analytical solutions to these flames allowed prediction of flame heights and radius, and when combined with the radiation empirical relationship led to corrections of total heat release rate from the flames due to radiative loss. Average convective and radiative heat flux were obtained from the analytical solution and a model based on the geometrical view factor of the burner surface with respect to the flame sheet, that were used to calculate heats of gasification. All flames burning in 21% by volume oxygen self-extinguished within 40 s. However, steady flames were observed at 26.5, 34, and 40% oxygen. The analytical solution was used to quantify flame steadiness just before extinction. The steadiest flames reached more than 94% of their steady-state heat fluxes and heights. A flammability map as a plot of heat of gasification versus heat of combustion was developed based on the measurement and theory for nominal ambient oxygen mole fractions of 0.265, 0.34, and 0.4.

Name: Dongyu Chen

Committee Members:

Professor Reinhard Radermacher, Chair/Advisor
Professor Amir Riaz, Co-Chair
Professor Vikrant C. Aute
Professor Jelena Srebric

Professor Siddhartha Das
Professor Bao Yang
Professor Peter B. Sunderland, Dean’s Representative

Date: Tuesday, June 7th, 2022

Time: 1:00 PM

Location: EGR 088-2162 (DeWALT Meeting)

Abstract:

Phase change materials (PCMs) are widely used in thermal energy storage systems, as they can absorb and release a large amount of heat during the phase change process. Numerical simulations can be used for parametric studies and analysis of the thermal performance of the PCM heat exchanger (HX) to produce an optimal design. Among various numerical methods, the lattice Boltzmann method (LBM), a mesoscopic approach that considers the molecular interactions at relatively low computation costs, offers certain key advantages in simulating the phase change process compared with the conventional Navier-Stokes-based (NS-based) methods. Therefore, a comprehensive solid-liquid phase change model is developed based on LBM which is capable of accurately and efficiently simulating the process of convective PCM phase change with and without porous media in both Cartesian and axisymmetric domains. 

Double distribution functions (DDF) coupled with a multi-relaxation-time (MRT) scheme are utilized in the LBM formulation for the simulation of the fluid flow and the temperature field. A differential scanning calorimetry (DSC) correlated equation is applied in LBM to model enthalpy, by which the solid-liquid interface can be automatically tracked. The source term in the MRT scheme is modified to eliminate numerical errors at high Rayleigh numbers. The conjugate thermal model is adopted for the consideration of heat transfer fluid (HTF) flow and conducting fins. Moreover, a parallel computational scheme is used, which allows the model to perform parametric studies efficiently. The new model is verified and validated by various case studies. The results indicate that the new model can successfully predict the process of PCM phase change with errors confined to less than 10%.

Parametric studies are then performed using the validated model to quantitatively evaluate the effect of convection on PCM melting, from which the acceleration rates of PCM melting and the threshold Rayleigh numbers at various aspect ratios are defined and quantified. Furthermore, PCM melting in porous cylindrical HX is also investigated. The results indicate that the acceleration of melting could reach 95% compared to that in pure PCM at 60% energy storage. Moreover, the negative effect of uneven temperature distributions on thermal performance of the HX caused by convection is quantified and analyzed. A modified cylindrical HX that offsets this negative effect by varying the geometry is also evaluated. The results indicate that the modified geometry can successfully enhance heat transfer and balance the uneven temperature distributions.