Next generation microprocessor (Vcore) requirements for high current slew rates and fast transient response together with low output voltage have posed great challenges on voltage regulator (VR) design . Since the debut of Intel 80X86 series, CPUs have greatly improved in performance with a dramatic increase on power consumption. According to the latest Intel VR11 design guidelines , the operational current may ramp up to 140A with typical voltages in the 1.1V to 1.4V range, while the slew rate of the transient current can be as high as 1.9A/ns [1, 2]. Meanwhile, the transient-response requirements are becoming stringer and stringer. This dissertation presents several topics on how to improve transient response for multi-phase voltage regulators. The Adaptive Modulation Control (AMC) is a type of non-linear control method which has proven to be effective in achieving high bandwidth designs as well as stabilizing the control loop during large load transients. It adaptively adjusts control bandwidth by changing the modulation gain, depending on different load conditions. With the AMC, a multiphase voltage regulator can be designed with an aggressively high bandwidth. When in heavy load transients where the loop could be potentially unstable, the bandwidth is lowered. Therefore, the AMC provides an optimal means for robust high-bandwidth design with excellent transient performance. The Error Amplifier Voltage Positioning (EAVP) is proposed to improve transient response by removing undesired spikes and dips after initial transient response. The EAVP works only in a short period of time during transient events without modifying the power stage and changing the control loop gain. It facilitates the error amplifier voltage recovering during transient events, achieving a fast settling time without impact on the whole control loop. Coupled inductors are an emerging topology for computing power supplies as VRs with coupled inductors show dynamic and steady-state advantages over traditional VRs. This dissertation first covers the coupling mechanism in terms of both electrical and reluctance modeling. Since the magnetizing inductance plays an important role in the coupled-inductor operation, a unified State-Space Averaging model is then built for a two-phase coupled-inductor voltage regulator. The DC solutions of the phase currents are derived in order to show the impact of the magnetizing inductance on phase current balancing. A small signal model is obtained based on the state-space-averaging model. The effects of magnetizing inductance on dynamic performance are presented. The limitations of conventional DCR current-sensing for coupled inductors are addressed. Traditional inductor DCR current sensing topology and prior arts fail to extract phase currents for coupled inductors. Two new DCR current sensing topologies for coupled inductors are presented in this dissertation. By implementation of simple RC networks, the proposed topologies can preserve the coupling effect between phases. As a result, accurate phase inductor currents and total current can be sensed, resulting in excellent current and voltage regulation. While coupled-inductor topologies are showing advantages in transient response and are becoming industry practices, they are suffering from low steady-state operating efficiency. Motivated by the challenging transient and efficiency requirements, this dissertation proposes a Full Bridge Coupled Inductor (FBCI) scheme which is able to improve transient response as well as savor high efficiency at (a) steady state. The FBCI can change the circuit configuration under different operational conditions. Its "flexible" topology is able to optimize both transient response and steady-state efficiency. The flexible core configuration makes implementation easy and clear of IP issues. A novel design methodology for planar magnetics based on numerical analysis of electromagnetic fields is offered and successfully applied to the design of low-voltage high power density dc-dc converters. The design methodology features intense use of FEM simulation. The design issues of planar magnetics, including loss mechanism in copper and core, winding design on PCB, core selections, winding arrangements and so on are first reviewed. After that, FEM simulators are introduced to numerically compute the core loss and winding loss. Consequently, a software platform for magnetics design is established, and optimized magnetics can then be achieved. Dynamic voltage scaling (DVS) technology is a common industry practice in optimizing power consumption of microprocessors by dynamically altering the supply voltage under different operational modes, while maintaining the performance requirements. During DVS operation, it is desirable to position the output voltage to a new level commanded by the microprocessor (CPU) with minimum delay. However, voltage deviation and slow settling time usually exist due to large output capacitance and compensation delay in voltage regulators. Although optimal DVS can be achieved by modifying the output capacitance and compensation, this method is limited by constraints from stringent static and dynamic requirements. In this dissertation, the effects of output capacitance and compensation network on DVS operation are discussed in detail. An active compensator scheme is then proposed to ensure smooth transition of the output voltage without change of power stage and compensation during DVS. Simulation and experimental results are included to demonstrate the effectiveness of the proposed scheme.

The delivery of high power and smaller footprints through a non-isolated topology demands for the use of multiphase topology in DC-DC converters. Multiphase reduces the ripple observed on both the input and output waveforms; however, it may not be enough to connect to sensitive power sources such as renewable energy sources. A single-phase modified boost converter demonstrates the ability to acquire very minimal input current ripple by addition of passive components. The expansion to multiphase topology is the next logical step for higher power application while furthering the low input current ripple benefit. In this thesis, the multiphase modified boost topology is compared with the multiphase standard boost topology to explore the benefits and trade-offs of the proposed topology. A 12V input to 19V output at 95W output power multiphase standard and modified boost converters were designed and constructed for the thesis. Results from theoretical calculations, computer simulations, and hardware implementations were then compared to evaluate their performances. Results show that compared to the standard boost, the modified boost yields significantly less input current ripple at 2% under full load condition while maintaining output voltage ripple of 5% and higher than 90% efficiency.

Determination of particle slip is a major concern for particle based measurements in un- heated supersonic facilities, as it is a limiting factor for the instruments' frequency response. For the purpose of determining the particle deceleration through a stationary shock wave in a super sonic windtunnel, a novel 1-D Laser Doppler probe with an unique spatial range (~1.5 mm) is presented. The study first gives a short review of the physics of particle motion with respect to different drag models and flow regime encountered in super sonic flows. In the second part, the focus lies on the development of a new Laser Doppler probe using non Gaussian beams to obtain a prolonged measurement volume. This volume covers a major part of the particle lag after a shock wave. An experimental investigation on particle acceleration and drag, using different types and sizes of seeding material, including standardized microspheres is carried out in the Mâ = 2.0 super sonic facility. Three different types of particles with four different sizes are experimentally investigated. The experimental data provides mean velocity as a function of distance from the shock and reveals significant agglomeration and evaporation problems with Titanium Oxide and Polystyrene Latex spheres. Particle acceleration measurements are presented, proving the unique concept of the new Laser Doppler probe. Mean and instantaneous acceleration data is extracted from high SNR signals. The acceleration data obtained is consistent in magnitude and trend with the physical phenomena expected and shows the feasibility of the new instrument. / Master of Science

Flash boiling may be defined as the finite-rate mechanism that governs phase change in a high temperature liquid that is depressurized below its vapor pressure. This is a transient and complicated phenomenon which has applications in many industries. The main focus of the current work is on modeling flash boiling in injectors used in engines operating on the principle of gasoline direct injection (GDI). These engines are prone to flash boiling due to the transfer of thermal energy to the fuel, combined with the sub-atmospheric pressures present in the cylinder during injection. Unlike cavitation, there is little tendency for the fuel vapor to condense as it moves downstream because the fuel vapor pressure exceeds the downstream cylinder pressure, especially in the homogeneous charge mode. In the current work, a pseudo-fluid approach is employed to model the flow, and the non-equilibrium nature of flash boiling is captured through the use of an empirical time scale. This time scale represents the deviation from thermal equilibrium conditions. The fuel composition plays an important role in flash boiling and hence, any modeling of this phenomenon must account for the type of fuel being used. In the current work, standard, NIST codes are used to model single component fluids like n-octane, n-hexane, and water, and a multi-component surrogate for JP8. Additionally, gasoline-ethanol blends are also considered. These mixtures are azeotropic in nature, generating vapor pressures that are higher than those of either pure component. To obtain the properties of these fuels, two mixing models are proposed that capture this non-ideal behavior. Flash boiling simulations in a number of two and three dimensional nozzles are presented, and the flow behavior and phase change inside the nozzles is analyzed in detail. Comparison with experimental data is performed in cases where data are available. The results of these studies indicate that flash boiling significantly affects the characteristics of the nozzle spray, like the spray cone angle and liquid penetration into the cylinder. A parametric study is also presented that can help understand how the two different time scales, namely the residence time in the nozzle and the vaporization time scale, interact and affect the phenomenon of flash boiling.

ual-three phase permanent magnet synchronous motors (DTP-PMSM) are becom ing more popular in the automotive field. Their potential to increase the reliability and efficiency of the vehicle makes them an attractive replacement for the three phase alternative. However, the increased number of phases makes the control of the machine more complex. As a result, conventional controllers can see reduced perfor mance, especially at high speeds and torques. Currently, with the increased process ing power of modern micro-controllers and field-programmable gate arrays (FPGA), many researchers are investigating whether finite-control set model predictive control (FCS-MPC) can be a suitable alternative. FCS-MPC is simple to implement and can achieve a better dynamic performance when compared to other controllers. Furthermore, the algorithm can be augmented for specific optimization goals and non-linearities to the system, which gives the designer creativity in improving the system response. However, Model-Predictive Control suffers from a variable switching frequency as well as reduced steady-state performance. It generally has increased current ripple in the phase currents. This thesis presents a method of reducing the steady-state ripples in FCS-MPC by introducing the use of virtual-flux in the model equations, the incremental model, and a dynamic vector search-space. All three of these applications make FCS-MPC have a iv significantly improved steady-state performance when compared to the conventional algorithm, while still keeping the benefit of the improved dynamic response. The benefits of the proposed techniques techniques are verified through simulation as well as on an experimental setup. / Thesis / Master of Applied Science (MASc)

The development and implementation of silicon carbine (SiC) devices is steadily increasing facilitating the electrification of aircrafts. In this thesis, a printed circuit board (PCB) based heavy copper busbar design and verification are introduced for a SiC based 250 kW multiphase drive system operated at 40,000 ft. Finite-element analysis (FEA) simulation studies of the PCB busbar are conducted to optimize the electric field intensity. Busbar modeling technic is also discussed to derive the current distribution and extract the loss. The measured partial discharge inception voltage (PDIV), switching transients and converter-level validations are provided for insulation, thermal and commutation loop verifications. As the part of the inverter system, the integrated gate driver is designed with SPI communication to drive the wide bandgap SiC power modules. With feature of drain-to-source current sensing feature, the gate driver could also provide over-current protection to fast-switching SiC power modules. The converter level verification is performed under single, dual, and quadruple three-phase inverter system for aviation motor drive to evaluate the overall performance of the powertrain converter. The outcomes of this research contribute to the advancement of electric aircraft technology by leveraging the benefits of SiC devices and optimizing busbar design, providing valuable insights and guidelines for engineers and researchers involved in the development and optimization of power electronic systems for all-electric aircraft applications. / Master of Science / This research focuses on the design and verification of PCB (Printed Circuit Board) busbars for a multiphase SiC-based all-electric aircraft powertrain converter. Silicon Carbide (SiC) devices, known for their high efficiency and fast-switching capabilities, are used in the converter to enhance its performance. The goal is to develop an optimized busbar design that ensures efficient power distribution and minimizes energy losses in this advanced aviation powertrain system. The study explores different aspects of busbar insulation design and analyzes busbar current distribution and loss extraction using simulation and modeling techniques. Additionally, gate driver design and communication network are investigated to drive and protect the wide bandgap SiC devices and to ensure the overall performance of the powertrain converter. The converter level verification is also performed under single, dual, and quadruple three-phase inverter system for aviation motor drive. The findings of this research contribute to the advancement of electric aircraft technology, utilizing SiC devices and optimized busbar design, and provide valuable insights for engineers and researchers working on power electronic systems for electric aircraft applications.

This dissertation focuses on the development and application of numerical algorithms for solving compressible multiphase fluid-material interaction problems. The first part of this dissertation is motivated by the extraordinary shock-resisting ability of elastomer coating materials (e.g., polyurea) under explosive loading conditions. Their performance, however, highly depends on their dynamic interaction with the substrate (e.g., metal) and ambient fluid (e.g., air or liquid); and the detailed interaction process is still unclear. Therefore, to certify the application of these materials, a fluid-structure coupled computational framework is needed. The first part of this dissertation developes such a framework. In particualr, the hyper-viscoelastic constitutive relation of polyurea is incorporated into a high-fidelity computational framework which couples a finite volume compressible multiphase fluid dynamics solver and a nonlinear finite element structural dynamics solver. Within this framework, the fluid-structure and liquid-gas interfaces are tracked using embedded boundary and level set methods. Then, the developed computational framework is applied to study the behavior a bilayer coating–substrate (i.e., polyurea-aluminum) system under various loading conditions. The observed two-way coupling between the structure and the bubble generated in a near-field underwater explosion motivates the next part of this dissertation. The second part of this dissertation investigates the yielding and collapse of an underwater thin-walled aluminum cylinder in near-field explosions. As the explosion intensity varies by two orders of magnitude, three different modes of collapse are discovered, including one that appears counterintuitive (i.e., one lobe extending towards the explosive charge), yet has been observed in previous laboratory experiments. Because of the transition of modes, the time it takes for the structure to reach self-contact does not decrease monotonically as the explosion intensity increases. Detailed analysis of the bubble-structure interaction suggests that, in addition to the incident shock wave, the second pressure pulse resulting from the contraction of the explosion bubble also has a significant effect on the structure's collapse. The phase difference between the structural vibration and the bubble's expansion and contraction strongly influences the structure's mode of collapse. The third part focuses on the development of efficient solution algorithms for compressible multi-material flow simulations. In these simulations, an unresolved challenge is the computation of advective fluxes across material interfaces that separate drastically different thermodynamic states and relations. A popular class of methods in this regard is to locally construct bimaterial Riemann problems, and to apply their exact solutions in flux computation, such as the one used in the preceding parts of the dissertation. For general equations of state, however, finding the exact solution of a Riemann problem is expensive as it requires nested loops. Multiplied by the large number of Riemann problems constructed during a simulation, the computational cost often becomes prohibitive. This dissertation accelerates the solution of bimaterial Riemann problems without introducing approximations or offline precomputation tasks. The basic idea is to exploit some special properties of the Riemann problem equations, and to recycle previous solutions as much as possible. Following this idea, four acceleration methods are developed. The performance of these acceleration methods is assessed using four example problems that exhibit strong shock waves, large interface deformation, contact of multiple (>2) interfaces, and interaction between gases and condensed matters. For all the problems, the solution of bimaterial Riemann problems is accelerated by 37 to 87 times. As a result, the total cost of advective flux computation, which includes the exact Riemann problem solution at material interfaces and the numerical flux calculation over the entire computational domain, is accelerated by 18 to 81 times. / Doctor of Philosophy / This dissertation focuses on the development and application of numerical methods for solving multiphase fluid-material interaction problems. The first part of this dissertation is motivated by the extraordinary shock-resisting ability of elastomer coating materials (e.g., polyurea) under explosive loading conditions. Their performance, however, highly depends on their dynamic interaction with the underlying structure and the ambient water or air; and the detailed interaction process is still unclear. Therefore, the first part of this dissertation developes a fluid-structure coupled computational framework to certify the application of these materials. In particular, the special material property of the coating material is incorparated into a state-of-the-art fluid-structure coupled computational framework that is able to model large deformation under extreme physical conditions. Then, the developed computational framework is applied to study how a thin-walled aluminum cylinder with polyurea coating responds to various loading conditions. The observed two-way coupling between the structure and the bubble generated in a near-field underwater explosion motivates the next part of this dissertation. The second part of this dissertation investigates the failure (i.e., yielding and collapse) of an underwater thin-walled aluminum cylinder in near-field explosions. As the explosion intensity varies by two orders of magnitude, three different modes of collapse are discovered, including one that appears counterintuitive (i.e., one lobe extending towards the explosive charge), yet has been observed in previous laboratory experiments. Via a detailed analysis of the interaction between the explosion gas bubble, the aluminum cylinder, and the ambient liquid water, this dissertation elucidated the role of bubble dynamics in the structure's different failure behaviors and revealed the transition mechanism between these behaviors. The third part of this dissertation presents efficient solution algorithms for the simulations of compressible multi-material flows. Many problems involving bubbles, droplets, phase transitions, and chemical reactions fall into this category. In these problems, discontinuities in fluid state variables (e.g., density) and material properties arise across the material interfaces, challenging numerical schemes' accuracy and robustness. In this regard, a promising class of methods that emerges in the recent decade is to resolve the exact wave structure at material interfaces, such as the one used in the preceding parts of the dissertation. However, the computational cost of these methods is prohibitive due to the nested loops invoked at every mesh edge along the material interface. To address this issue, the dissertation develops four efficient solution methods, following the idea of exploiting special properties of governing equations and recycling previous solutions. Then, the acceleration effect of these methods is assessed using various challenging multi-material flow problems. In different test cases, significant reduction in computational cost (acceleration of 18 to 81 times) is achieved, without sacrificing solver robustness and solution accuracy.

Steel is an important engineering material used in a variety of applications due to its mechanical properties and durability. With increasing demand for higher performance, complex structures, and the need for cost reduction within manufacturing processes, there are numerous challenges with traditional steel design options and production methods with manufacturing cost being the most significant. In this research, this challenge is addressed by developing a micro-genetic algorithm to minimize the manufacturing cost while designing steel with the desired mechanical properties. The algorithm was integrated with machine learning models to predict the mechanical properties and microstructure for the generated alloys based on their chemical compositions and heat treatment conditions. Through this, it was demonstrated that new steel alloys with specific mechanical property targets could be generated at an optimal cost. The research’s contribution lies in the development of a different approach to optimize steel production that combines the advantages of machine learning and evolutionary algorithms while increasing the number of input parameters. Additionally, it uses a small dataset illustrating that it can be used in applications where data is lacking. This approach has significant implications for the steel industry as it provides a more efficient way to design and produce new steel alloys. It also contributes to the overall material science field by demonstrating its ability in a material’s design and optimization. Overall, the proposed framework highlights the potential of utilizing machine learning and evolutionary algorithms in material design and optimization. / Thesis / Master of Applied Science (MASc) / This research aims to develop an AI-based functional integrated with a heuristic algorithm that optimizes parameters to meet desired mechanical properties and cost for steels. The developed algorithm generates new alloys which meet desired mechanical property targets by considering alloy composition and heat treatment condition inputs. Used in combination with machine learning models for the mechanical property and microstructure prediction of new alloys, the algorithm successfully demonstrates its ability to meet specified targets while achieving cost savings. The approach presented has significant implications for the steel industry as it offers a quick method of optimizing steel production, which can reduce overall costs and improve efficiency. The integration of machine learning within the algorithm offers a different way of designing new steel alloys which has the potential to improve manufactured products by ultimately improving their performance and quality.

The main focus of this work is to investigate thermal performance of self-contained drum motor drive systems (SCDMDS). All components of a SCDMDS are contained inside a rotating drum including the electric motor, gearbox, and an air/oil multiphase flow. A considerable amount of heat is generated within the SCDMDS from various sources, namely, the electric motor losses, the oil viscous dissipation and the gearbox losses. In meantime, a limited amount of heat is dissipated through the surface of the rotating drum and the side flanges. Therefore, a SCDMDS sometimes encounters a serious overheating problem, which often results in electric motor failure. The different heat generation and dissipation mechanisms as well as the two-phase flow within the SCDMDS have been studied experimentally and numerically under different operating parameters, namely, the oil level (OV), the drum rotational speed (N), the torque (ζ), the number of motor poles (n) and the electric motor dimensions. The effects of rubber lagging material and thickness as well as the use of rubber belts have been investigated as well. The numerical part of the present study has been carried out using Ansys-CFX and was validated using experimental data. Results showed that the optimum oil level (OV) for the best thermal performance is about 65%. The increase in the rotational speed (N) enhanced the heat transfer within the SCDMDS due to the improved oil splashing. Viscous dissipation (VD) between the motor stator and the rotating drive drum was found to be almost negligible. However, oil viscous dissipation within the gap between the motor rotor and stator was found to have an important effect on the thermal performance. An analytical model has been developed and implemented using MATLAB to estimate VD within the motor. The losses from the gearbox were studied experimentally and numerically considering planetary and co-axial gear trains. The numerical work was carried out using the KISSsoft and KISSsys software. Results showed that the increase in the drum rotational speed (N) or the drum torque (ζ) increased the gearbox losses. In the planetary gearbox, any increase in the OV increases the churning losses, however, the increase in OV increased the losses in the co-axial gearbox up to OV = 31% beyond which the losses remained constant. After understanding the complex interplay between all the heat generating and dissipating mechanisms within the SCDMDS, a number of possible modifications have been proposed in order to resolve the overheating problem. The effect of cooling the electric motor by using an axial air flow has been investigated. The effect of adding fins along the inner surface of the outer rotating drum has also been studied. Correlations of the various contributing mechanisms have been developed. Based on a thermal resistance network, a SCDMDS sizing and performance assessment computer software tool in the form of a digital twin (DT) has been developed. A user-friendly interface has been developed using Visual Basics and Excel. The DT estimates temperature distribution and the amount of heat generated and dissipated from each component within the SCDMDS and hence it identifies whether the case is considered safe to operate or overheating is expected. In overheated cases, the DT also suggests several possible modifications the user could consider to resolve the overheating problem. The DT has been validated against several experimental case studies and found to be very reasonably accurate. / Thesis / Doctor of Philosophy (PhD) / This study is focused on investigating heat transfer and fluid flow inside a self-contained drum motor drive system (SCDMDS). The problem of interest involves multiple heat sources enclosed inside a tight space of the rotating drum. There is an electrical motor, gearbox and a multiphase (oil/air) flow inside the rotating drum of the SCDMDS. In this thesis, experimental test rigs were constructed to investigate the effect of a number of operating and geometrical parameters. In addition, numerical analysis of the multiphase oil/air flow was carried out using Ansys - CFX. The KISSsoft and KISSsys software packages were used to determine various types of heat losses within the geartrain. Due to the presence of multiple heat sources inside a confined space, overheating of a number of SCDMDS has been reported. The overheating problem worsened even more when rubber lagging is used to increase traction between the drive drum and the belt. Several correlations have been developed for various heat transfer mechanisms governing the overall thermal performance of the entire SCDMDS. An analytical model (a digital twin) has been developed using Visual Basics and Excel. The digital twin estimates the temperature distribution and the amount of heat generated and dissipated inside the SCDMDS. It has been validated against many case studies provided by the industrial partner. The model identifies the possibility of overheating and provides the user with several potential modifications to resolve it. Hence, the model can be used as a performance and design tool of various models of SCDMDS.

Multiphase flows are simultaneous flows of two or more immiscible fluids in a pipe or vessel. Multiphase flows occur in a wide variety of industrial applications, such as chemical reactors, power generation, oil and gas production or transportation, etc. In most of these applications, efficiency and process reliability depend not insignificantly on the composition and flow morphology of these multiphase flows. Therefore, accurate determination of parameters such as phase fractions and their spatial distribution, as well as measurement of volumetric or mass flow rates, is essential to optimize and ensure correct operation of the equipment. For a better prediction of flow characteristics of multiphase systems, the development and validation of analytical models and CFD codes for simulations of multiphase flows has been promoted for some time in thermofluid dynamics research. For this purpose, the in-depth analysis of multiphase flows with high spatial and temporal resolution is essential. However, to date, there is no universal sensor that can directly measure all the required flow parameters over the full range of all flow conditions. Therefore, several strategies have been developed to solve this problem. For pure measurement of fluid composition and mixture volume flow, for example, the fluid mixture is often conditioned before measurement by separation into individual phases or by homogenization. However, this does not allow any more information about the flow morphology. In situations where the fluid cannot be preconditioned, for example when investigating bubble size distributions or predicting plug flows, imaging techniques such as wire-mesh sensors therefore play an important role because they provide cross-sectional images of the flow in rapid succession. This information can be used to determine phase distributions and identify flow regimes, which in turn can serve as input to other sensors to find optimal operating points. In addition, such information is important for validating models and numerical simulations. Although wire-mesh sensors are very attractive and now widely used due to their high spatial and temporal resolution, the measurement signals obtained from the sensor can be corrupted by energy losses and channel crosstalk under certain conditions. Therefore, a better understanding of the real physical conditions when using wire-mesh sensors is essential to improve the measurement accuracy and to extend the range of applications, e.g., for the measurement of media with very high conductivities or for an accurate quantification of individual phases in three-phase flows. In the present work, the current limitations of existing wire-mesh sensor systems are investigated in detail, thus providing a basis for technical improvements and the development of new methods for better interpretation of the measured values of wire-mesh sensors. For this purpose, the electronic measurement principle and the real sensor geometries are first investigated with respect to inherent energy losses and channel crosstalk. Based on mixing models, a method for visualization and quantification of three-phase gas-oil-water flows even in the presence of dispersions is presented. In addition, nonlinearities of wire-mesh sensors are predicted for the first time by a hybrid model based on the finite element method, which also incorporates the real parameters of the electronic components of signal generation and measurement. This model is subsequently used to generate synthetic data and to test new correction methods. Finally, two methods are proposed to compensate for unavoidable energy losses. The first method allows inherent determination of energy losses that cannot be suppressed by further circuit optimization. The second method allows determination of the voltage drop caused by the impedance of the electrodes when measured in highly conductive liquids. Numerical and experimental analyses show an improvement in the measurement accuracy of wire-mesh sensors with respect to the average and local phase fractions. The deviations of the average phase fraction were reduced from more than 15% to less than 2% and the deviations in local measurements from more than 30% to less than 5%.:Abstract 3 Zusammenfassung 5 Statement of authorship 9 Acronyms 13 Symbols 15 1. Introduction 17 2. State of the science and technology 21 3. Wire-mesh sensor and experimental test facilities 43 4. Three-phase flow measurement based on dual-modality wire-mesh sensor 53 5. Wire-mesh sensor model based on finite-element method and circuit simulation 67 6. Analysis of non-linear effects in measurements of wire-mesh sensor 79 7. Methods for improving the measurement accuracy of wire-mesh sensors 87 8. Conclusions and outlook 97 Bibliography 101 Appendices 111 A. List of scientific publications 113