Parameter optimization for noise and vibration reduction associated with position and speed sensorless control of permanent magnet machines at low speed

With the advantages and popularity of Permanent Magnet (PM) motors due to their high power density, there is an increasing incentive to use them in variety of applications including electric actuation. These applications have strict noise emission standards. The generation of audible noise and associated vibration modes are characteristics of all electric motors, it is especially problematic in low speed sensorless control rotary actuation applications using high frequency voltage injection technique. This dissertation is aimed at solving the problem of optimizing the sensorless control algorithm for low noise and vibration while achieving at least 12 bit absolute accuracy for speed and position control. The low speed sensorless algorithm is simulated using an improved Phase Variable Model, developed and implemented in a hardware-in-the-loop prototyping environment. Two experimental testbeds were developed and built to test and verify the algorithm in real time.^ A neural network based modeling approach was used to predict the audible noise due to the high frequency injected carrier signal. This model was created based on noise measurements in an especially built chamber. The developed noise model is then integrated into the high frequency based sensorless control scheme so that appropriate tradeoffs and mitigation techniques can be devised. This will improve the position estimation and control performance while keeping the noise below a certain level. Genetic algorithms were used for including the noise optimization parameters into the developed control algorithm.^ A novel wavelet based filtering approach was proposed in this dissertation for the sensorless control algorithm at low speed. This novel filter was capable of extracting the position information at low values of injection voltage where conventional filters fail. This filtering approach can be used in practice to reduce the injected voltage in sensorless control algorithm resulting in significant reduction of noise and vibration.^ Online optimization of sensorless position estimation algorithm was performed to reduce vibration and to improve the position estimation performance. The results obtained are important and represent original contributions that can be helpful in choosing optimal parameters for sensorless control algorithm in many practical applications.^

Design optimization of modern machine drive systems for maximum fault tolerant and optimal operation

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. ^ A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. ^ The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. ^ The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. ^ To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.^

A completely kinematostatically decoupled XY compliant parallel manipulator through new topology structure

This paper deals with a completely kinematostaticaly decoupled XY compliant parallel manipulator (CPM) composed of exactly-constrained compliant modules. A new 4-PP XY translational parallel mechanism (TPM) with a new topology structure is firstly proposed where each two P (P: prismatic) joints on the base in two non-adjacent legs are rigidly connected. A novel 4-PP XY CPM is then obtained by replacing each traditional P join on the base in the 4-PP XY TPM with a compound basic parallelogram module (CBPM) and replacing each traditional P joint on the motion stage with a basic parallelogram module (BPM). Approximate analytical model is derived with comparison to the FEA (finite element analysis) model and experiment for a case study. The proposed novel XY CPM has a compact configuration with good dynamics, and is able to well constrain the parasitic rotation and the cross-axis coupling of the motion stage. The cross-axis motion of the input stage can be completely eliminated, and the lost motion between the input stage and the motion stage is significantly reduced.

Investigating the Tradespace between Increased Automation and Optimal Manning on Aircraft Carrier Decks

With increasing prevalence and capabilities of autonomous systems as part of complex heterogeneous manned-unmanned environments (HMUEs), an important consideration is the impact of the introduction of automation on the optimal assignment of human personnel. The US Navy has implemented optimal staffing techniques before in the 1990's and 2000's with a "minimal staffing" approach. The results were poor, leading to the degradation of Naval preparedness. Clearly, another approach to determining optimal staffing is necessary. To this end, the goal of this research is to develop human performance models for use in determining optimal manning of HMUEs. The human performance models are developed using an agent-based simulation of the aircraft carrier flight deck, a representative safety-critical HMUE. The Personnel Multi-Agent Safety and Control Simulation (PMASCS) simulates and analyzes the effects of introducing generalized maintenance crew skill sets and accelerated failure repair times on the overall performance and safety of the carrier flight deck. A behavioral model of four operator types (ordnance officers, chocks and chains, fueling officers, plane captains, and maintenance operators) is presented here along with an aircraft failure model. The main focus of this work is on the maintenance operators and aircraft failure modeling, since they have a direct impact on total launch time, a primary metric for carrier deck performance. With PMASCS I explore the effects of two variables on total launch time of 22 aircraft: 1) skill level of maintenance operators and 2) aircraft failure repair times while on the catapult (referred to as Phase 4 repair times). It is found that neither introducing a generic skill set to maintenance crews nor introducing a technology to accelerate Phase 4 aircraft repair times improves the average total launch time of 22 aircraft. An optimal manning level of 3 maintenance crews is found under all conditions, the point at which any additional maintenance crews does not reduce the total launch time. An additional discussion is included about how these results change if the operations are relieved of the bottleneck of installing the holdback bar at launch time.

Bridging the Scale Gap from Leaf to Canopy in Biosphere-Atmosphere Gas and Particle Exchanges

Terrestrial ecosystems, occupying more than 25% of the Earth's surface, can serve as

`biological valves' in regulating the anthropogenic emissions of atmospheric aerosol

particles and greenhouse gases (GHGs) as responses to their surrounding environments.

While the signicance of quantifying the exchange rates of GHGs and atmospheric

aerosol particles between the terrestrial biosphere and the atmosphere is

hardly questioned in many scientic elds, the progress in improving model predictability,

data interpretation or the combination of the two remains impeded by

the lack of precise framework elucidating their dynamic transport processes over a

wide range of spatiotemporal scales. The diculty in developing prognostic modeling

tools to quantify the source or sink strength of these atmospheric substances

can be further magnied by the fact that the climate system is also sensitive to the

feedback from terrestrial ecosystems forming the so-called `feedback cycle'. Hence,

the emergent need is to reduce uncertainties when assessing this complex and dynamic

feedback cycle that is necessary to support the decisions of mitigation and

adaptation policies associated with human activities (e.g., anthropogenic emission

controls and land use managements) under current and future climate regimes.

With the goal to improve the predictions for the biosphere-atmosphere exchange

of biologically active gases and atmospheric aerosol particles, the main focus of this

dissertation is on revising and up-scaling the biotic and abiotic transport processes

from leaf to canopy scales. The validity of previous modeling studies in determining

iv

the exchange rate of gases and particles is evaluated with detailed descriptions of their

limitations. Mechanistic-based modeling approaches along with empirical studies

across dierent scales are employed to rene the mathematical descriptions of surface

conductance responsible for gas and particle exchanges as commonly adopted by all

operational models. Specically, how variation in horizontal leaf area density within

the vegetated medium, leaf size and leaf microroughness impact the aerodynamic attributes

and thereby the ultrane particle collection eciency at the leaf/branch scale

is explored using wind tunnel experiments with interpretations by a porous media

model and a scaling analysis. A multi-layered and size-resolved second-order closure

model combined with particle

uxes and concentration measurements within and

above a forest is used to explore the particle transport processes within the canopy

sub-layer and the partitioning of particle deposition onto canopy medium and forest

oor. For gases, a modeling framework accounting for the leaf-level boundary layer

eects on the stomatal pathway for gas exchange is proposed and combined with sap

ux measurements in a wind tunnel to assess how leaf-level transpiration varies with

increasing wind speed. How exogenous environmental conditions and endogenous

soil-root-stem-leaf hydraulic and eco-physiological properties impact the above- and

below-ground water dynamics in the soil-plant system and shape plant responses

to droughts is assessed by a porous media model that accommodates the transient

water

ow within the plant vascular system and is coupled with the aforementioned

leaf-level gas exchange model and soil-root interaction model. It should be noted

that tackling all aspects of potential issues causing uncertainties in forecasting the

feedback cycle between terrestrial ecosystem and the climate is unrealistic in a single

dissertation but further research questions and opportunities based on the foundation

derived from this dissertation are also brie

y discussed.

Coupled diusion and mechanics in battery electrodes

Thesis (Ph.D.)--University of Washington, 2016-08

Efficient algorithms to solve scheduling problems with a variety of optimization criteria

La programmation par contraintes est une technique puissante pour résoudre, entre autres, des problèmes d’ordonnancement de grande envergure. L’ordonnancement vise à allouer dans le temps des tâches à des ressources. Lors de son exécution, une tâche consomme une ressource à un taux constant. Généralement, on cherche à optimiser une fonction objectif telle la durée totale d’un ordonnancement. Résoudre un problème d’ordonnancement signifie trouver quand chaque tâche doit débuter et quelle ressource doit l’exécuter. La plupart des problèmes d’ordonnancement sont NP-Difficiles. Conséquemment, il n’existe aucun algorithme connu capable de les résoudre en temps polynomial. Cependant, il existe des spécialisations aux problèmes d’ordonnancement qui ne sont pas NP-Complet. Ces problèmes peuvent être résolus en temps polynomial en utilisant des algorithmes qui leur sont propres. Notre objectif est d’explorer ces algorithmes d’ordonnancement dans plusieurs contextes variés. Les techniques de filtrage ont beaucoup évolué dans les dernières années en ordonnancement basé sur les contraintes. La proéminence des algorithmes de filtrage repose sur leur habilité à réduire l’arbre de recherche en excluant les valeurs des domaines qui ne participent pas à des solutions au problème. Nous proposons des améliorations et présentons des algorithmes de filtrage plus efficaces pour résoudre des problèmes classiques d’ordonnancement. De plus, nous présentons des adaptations de techniques de filtrage pour le cas où les tâches peuvent être retardées. Nous considérons aussi différentes propriétés de problèmes industriels et résolvons plus efficacement des problèmes où le critère d’optimisation n’est pas nécessairement le moment où la dernière tâche se termine. Par exemple, nous présentons des algorithmes à temps polynomial pour le cas où la quantité de ressources fluctue dans le temps, ou quand le coût d’exécuter une tâche au temps t dépend de t.

Mechanizmy wybiórczości środowiskowej u świstunki leśnej Phylloscopus sibilatrix

Wydział Biologii

Using Intelligent Agents to Understand Management Practices and Retail Productivity

Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities in the future. The project is still at an early stage. So far we have conducted a case study in a UK department store to collect data and capture impressions about operations and actors within departments. Furthermore, based on our case study we have built and tested our first version of a retail branch simulator which we will present in this paper.

Understanding Retail Productivity by Simulating Management Practices

Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities in the future. Our research so far has led us to conduct case study work with a top ten UK retailer, collecting data in four departments in two stores. Based on our case study data we have built and tested a first version of a department store simulator. In this paper we will report on the current development of our simulator which includes new features concerning more realistic data on the pattern of footfall during the day and the week, a more differentiated view of customers, and the evolution of customers over time. This allows us to investigate more complex scenarios and to analyze the impact of various management practices.

A Multi-Agent Simulation of Retail Management Practices

We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents do offer potential for developing organizational capabilities in the future. Our multi-disciplinary research team has worked with a UK department store to collect data and capture perceptions about operations from actors within departments. Based on this case study work, we have built a simulator that we present in this paper. We then use the simulator to gather empirical evidence regarding two specific management practices: empowerment and employee development.

I. Quantal effects in biochemical cooperativity and a proposed mechanism for the differentiation of calcium signaling in synaptic plasticity. II. Evolutionary algorithms for the optimization of methods in computational chemistry

In Part 1 of this thesis, we propose that biochemical cooperativity is a fundamentally non-ideal process. We show quantal effects underlying biochemical cooperativity and highlight apparent ergodic breaking at small volumes. The apparent ergodic breaking manifests itself in a divergence of deterministic and stochastic models. We further predict that this divergence of deterministic and stochastic results is a failure of the deterministic methods rather than an issue of stochastic simulations.

Ergodic breaking at small volumes may allow these molecular complexes to function as switches to a greater degree than has previously been shown. We propose that this ergodic breaking is a phenomenon that the synapse might exploit to differentiate Ca$^{2+}$ signaling that would lead to either the strengthening or weakening of a synapse. Techniques such as lattice-based statistics and rule-based modeling are tools that allow us to directly confront this non-ideality. A natural next step to understanding the chemical physics that underlies these processes is to consider \textit{in silico} specifically atomistic simulation methods that might augment our modeling efforts.

In the second part of this thesis, we use evolutionary algorithms to optimize \textit{in silico} methods that might be used to describe biochemical processes at the subcellular and molecular levels. While we have applied evolutionary algorithms to several methods, this thesis will focus on the optimization of charge equilibration methods. Accurate charges are essential to understanding the electrostatic interactions that are involved in ligand binding, as frequently discussed in the first part of this thesis.

Modelling and simulating retail management practices: a first approach

Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UK’s top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering “what-if” questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers’ satisfaction with service, based on their individual shopping experiences.

AN ASSESSMENT OF FLOW REGIME MAPS AND A NUMERICAL HEAT TRANSFER CORRELATION FOR THE STRATIFIED/ANNULAR REGIME OF SHEAR-DRIVEN INTERNAL CONDENSING FLOWS

Several modern-day cooling applications require the incorporation of mini/micro-channel shear-driven flow condensers. There are several design challenges that need to be overcome in order to meet those requirements. The difficulty in developing effective design tools for shear-driven flow condensers is exacerbated due to the lack of a bridge between the physics-based modelling of condensing flows and the current, popular approach based on semi-empirical heat transfer correlations. One of the primary contributors of this disconnect is a lack of understanding caused by the fact that typical heat transfer correlations eliminate the dependence of the heat transfer coefficient on the method of cooling employed on the condenser surface when it may very well not be the case. This is in direct contrast to direct physics-based modeling approaches where the thermal boundary conditions have a direct and huge impact on the heat transfer coefficient values. Typical heat transfer correlations instead introduce vapor quality as one of the variables on which the value of the heat transfer coefficient depends. This study shows how, under certain conditions, a heat transfer correlation from direct physics-based modeling can be equivalent to typical engineering heat transfer correlations without making the same apriori assumptions. Another huge factor that raises doubts on the validity of the heat-transfer correlations is the opacity associated with the application of flow regime maps for internal condensing flows. It is well known that flow regimes influence heat transfer rates strongly. However, several heat transfer correlations ignore flow regimes entirely and present a single heat transfer correlation for all flow regimes. This is believed to be inaccurate since one would expect significant differences in the heat transfer correlations for different flow regimes. Several other studies present a heat transfer correlation for a particular flow regime - however, they ignore the method by which extents of the flow regime is established. This thesis provides a definitive answer (in the context of stratified/annular flows) to: (i) whether a heat transfer correlation can always be independent of the thermal boundary condition and represented as a function of vapor quality, and (ii) whether a heat transfer correlation can be independently obtained for a flow regime without knowing the flow regime boundary (even if the flow regime boundary is represented through a separate and independent correlation). To obtain the results required to arrive at an answer to these questions, this study uses two numerical simulation tools - the approximate but highly efficient Quasi-1D simulation tool and the exact but more expensive 2D Steady Simulation tool. Using these tools and the approximate values of flow regime transitions, a deeper understanding of the current state of knowledge in flow regime maps and heat transfer correlations in shear-driven internal condensing flows is obtained. The ideas presented here can be extended for other flow regimes of shear-driven flows as well. Analogous correlations can also be obtained for internal condensers in the gravity-driven and mixed-driven configuration.

Design Optimization of Modern Machine-drive Systems for Maximum Fault Tolerant and Optimal Operation

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.