Design Optimization of Submerged Jet Nozzles for Enhanced Mixing

The purpose of this thesis was to identify the optimal design parameters for a jet nozzle which obtains a local maximum shear stress while maximizing the average shear stress on the floor of a fluid filled system. This research examined how geometric parameters of a jet nozzle, such as the nozzle\u27s angle, height, and orifice, influence the shear stress created on the bottom surface of a tank. Simulations were run using a Computational Fluid Dynamics (CFD) software package to determine shear stress values for a parameterized geometric domain including the jet nozzle. A response surface was created based on the shear stress values obtained from 112 simulated designs. A multi-objective optimization software utilized the response surface to generate designs with the best combination of parameters to achieve maximum shear stress and maximum average shear stress. The optimal configuration of parameters achieved larger shear stress values over a commercially available design

Doctor of Philosophy

dissertationThe increasing cost of full-scale testing makes model-based computational methods very important for the reliability assessment of large complex systems. However, physical, statistical, and model uncertainties make it difficult to have high confidence in model-based reliability prediction. Hence, there is an important need to validate model predictions using test data. However, for large-scale systems, availability of test results is rare. This kind of problem can be validated from obtaining test data to validate smaller modules (subsystem and component-level models) of the overall reliability computational model. A framework for Validation and Uncertainty Quantification (VUQ) of a model for an overarching problem with no prior experimental data is implemented on one such problem, measurement of combustion efficiency for industrial flares. The hierarchical VUQ process begins with defining lower subsystems. For the current overarching problem, a nonreacting buoyancy-driven turbulent mixing experiment was selected as a component-scale case and wind-tunnel flare experiments as a pilot-scale case. A 6-step systematic validation framework is adopted from the literature and applied to provide upper and lower bounds of the prediction. Each brick/level in the hierarchy is validated individually as well as together as one big system to propagate the uncertainties and to build confidence in the model. Monte-Carlo method and consistency constraints are used to analyze surrogate models, constructed for complex and expensive multiphysics simulators. The analysis refines the parameter space where the model makes valid predictions and with certain confidence

Flow control optimization in a jet engine serpentine inlet duct

Computational investigations were carried out on an advanced serpentine jet engine inlet duct to understand the development and propagation of secondary flow structures. Computational analysis which went in tandem with experimental investigation was required to aid secondary flow control required for enhanced pressure recovery and decreased distortion at the engine face. In the wake of earlier attempts with modular fluidic actuators used for this study, efforts were directed towards optimizing the actuator configurations. Backed by both computational and experimental resources, many variations in the interaction of fluidic actuators with the mainstream flow were attempted in the hope of best controlling secondary flow formation. Over the length of the studies, better understanding of the flow physics governing flow control for 3D curved ducts was developed. Blowing tangentially, to the wall at the bends of the S-duct, proved extremely effective in enforcing active flow control. At practical jet momentum coefficients, significant improvements characterized by an improved pressure recove ry of 37% and a decrease in distortion close to 90% were seen

Solution-verified reliability analysis and design of bistable MEMS using error estimation and adaptivity.

This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient

Comparing 10 methods for solution verification and linking to model validation

reportGrid convergence is often assumed as a given during computational analyses involving discretization of an assumed continuum process. In practical use of finite difference and finite element analyses, perfect grid convergence is rarely achieved or assured, and this fact must be addressed to make statements about model validation or the use of models in risk analysis. We have previously provided a 4-step quantitative implementation for a quantitative V&V process. One of the steps in the 4-step process is that of Solution Verification. Solution Verification is the process of assuring that a model approximating a physical reality with a discretized continuum (e.g. finite element) code converges in each discretized domain to a converged answer on the quantity of subsequent validation interest. The modeling reality is that often we are modeling a problem with a discretized code because it is neither continuous spatially (e.g. contact and impact) nor smooth in relevant physics (e.g. shocks, melting, etc). The typical result is a non-monotonic convergence plot that can lead to spurious conclusions about the order of convergence, and a lack of means to estimate residual solution verification error or uncertainty at confidence. We compare ten techniques for grid convergence assessment, each formulated to enable a quantification of solution verification uncertainty at confidence and order of convergence for monotonic and non- monotonic mesh convergence studies. The more rigorous of these methods require a minimum of four grids in a grid convergence study to quantify the grid convergence uncertainty. The methods supply the quantitative terms for solution verification error and uncertainty estimates needed for inclusion into subsequent model validation, confidence, and reliability analyses. Naturally, most such methodologies are still evolving, and this work represents the views of the authors and not necessarily the views of Lawrence Livermore National Laboratory

Clean Coal Program Research Activities

Although remarkable progress has been made in developing technologies for the clean and efficient utilization of coal, the biggest challenge in the utilization of coal is still the protection of the environment. Specifically, electric utilities face increasingly stringent restriction on the emissions of NO{sub x} and SO{sub x}, new mercury emission standards, and mounting pressure for the mitigation of CO{sub 2} emissions, an environmental challenge that is greater than any they have previously faced. The Utah Clean Coal Program addressed issues related to innovations for existing power plants including retrofit technologies for carbon capture and sequestration (CCS) or green field plants with CCS. The Program focused on the following areas: simulation, mercury control, oxycoal combustion, gasification, sequestration, chemical looping combustion, materials investigations and student research experiences. The goal of this program was to begin to integrate the experimental and simulation activities and to partner with NETL researchers to integrate the Program's results with those at NETL, using simulation as the vehicle for integration and innovation. The investigators also committed to training students in coal utilization technology tuned to the environmental constraints that we face in the future; to this end the Program supported approximately 12 graduate students toward the completion of their graduate degree in addition to numerous undergraduate students. With the increased importance of coal for energy independence, training of graduate and undergraduate students in the development of new technologies is critical

MODELLING CONTAINER LOGISTICS PROCESSES IN CONTAINER TERMINALS: A CASE STUDY IN ALEXANDRIA

This study aims to optimize the logistics processes of container terminals. Potentially powerful pipe-flow models of container terminal logistics processes have been neglected to date and modelling of terminals is rare. Because research which adopts a pipe flow and dynamic operational perspective is rare, a case application in Alexandria, Egypt collated empirical container and information flows using interviews and company records to describe its logistics processes and model container and information flows. The methodology used includes qualitative and quantitative methods and a descriptive methodology proceeds sequentially. Primary and secondary data were presented as a pipe flow model to show interrelations between the company’s resources and to identify bottlenecks. Simulation modelling used Simul8 software. Operational level modelling of both import and export flows simulated the actual inbound and outbound flows of containers from entry to exit. The import logistics process includes activities such as unloading vessels by quay cranes, moving containers by tractors to yard cranes to go for storage where customs procedures take place before exiting the terminal by customer’s truck. The export logistics process includes the activities associated with customers’ trucks, lifters, storage yards, tractors and quay cranes. The model takes into account the uncertainties in each activity. This study focuses on operational aspects rather than cost issues, and considers container flows rather than vessel flows. Although the simulated model was not generalized, implementation elsewhere is possible. Following successful validation of a base simulation model which reproduces the case company’s historical scenario, scenario testing empowered the case company to pro-actively design and test the impact of operational changes on the entire logistics process. The study evaluates a typical container terminal logistics system including both import and export containers in the presence of multiple uncertainties in terminal operations (e.g. quay crane operations, tractor operations, yard crane operations). Sensitivity testing and scenario analysis can empower terminal managers to make decisions to improve performance, and to guide terminal planners, managers, and operators in testing future investment scenarios before implementation.Arab Academy for Science, Technology and Maritime Transpor