Review of selection criteria for sensor and actuator configurations suitable for internal combustion engines

This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited. Simple control metrics such as conditioning number are popular, mostly because they need fewer assumptions than closed-loop metrics, which require a full plant, disturbance and goal model. Overall, no clear consensus can be found on the choice of metrics to define optimal control configurations, with physical measures, linear algebra metrics and modern control metrics all being used. Genetic algorithms and multi-criterial optimisation were identified as the most widely used methods for optimal sensor selection, although addressing the dimensionality and complexity of formulating the problem remains a challenge. This review does present a number of different successful approaches for specific applications domains, some of which may be applicable to diesel engines and other automotive applications. For a thorough treatment, non-linear dynamics and uncertainties need to be considered together, which requires sophisticated (non-Gaussian) stochastic models to establish the value of a control architecture

Performing heavy transfers for offshore wind maintenance

As offshore wind farms become larger and further from the shore, there are strong economic and climate incentives to perform transfers required for operations and maintenance from floating vessels, rather than employing expensive and slow jack up rigs. However, successful transfers of heavy and sensitive equipment from a floating vessel (in all but benign sea/wind conditions) are heavily dependent on multiple degrees of freedom, high performance control. This project aims to bring a novel modelling and simulation methodology in Simulink that could be used to assess offshore wind installation and maintenance procedures. More specifically, the goal is to demonstrate that a crane prototype assumed to be located on a floating ship can transfer loads of hundreds of tons onto a fixed platform. Furthermore, this process should be completed with good precision and minimal impact force during equipment loading onto the stand. This problem has not yet been answered in research, with the only relevant patent in the field being the Ampelmann platform, a motionless bridge allowing technicians to access the offshore turbine. The first main contribution to knowledge of this thesis was the design of a 90 m crane that could handle a 660 tons load. This thesis presents a procedure, based on both mechanical/hydraulics design as well as empirical findings, which could be re-used for scaling the crane model to a more realistic dimension. It is worth noting that the goal here was to assess whether a realistically weighing piece of equipment could be stably handled, while the actual size of the crane was deemed unimportant. Another missing gap in literature this project wanted to fill was achieving active motion compensation for a larger scale system such as the current one. This refers to balancing out the base motions on multiple axes, so the payload can be moved on a given trajectory unaffected by them. Currently, research in the field mainly consists of crane mechanisms that feature active heave compensation, which only refers to the vertical axis. Hence, two control design methods were employed to assess the viability of heavy payload positioning from floating vessels through the development of a simulation approach using Simulink. The crane prototype was designed and modelled to operate under simulated vessel motions given by sea states with a significant wave height of 5 m and maximum wave frequency of 1 rad/s. Then, traditional control (feedback and feedforward) was designed to achieve active motion compensation with steady-state position errors under 20 cm. A second controller architecture was then designed/implemented as a comparison basis for the first one, with the aim being to find the most robust solution of the two. The nonlinear generalised minimum variance (NGMV) control algorithm was chosen for control design in this application. Due to its ability to compensate for significant system nonlinearities and the ease of implementation, NGMV was a good candidate for the task at hand. Tuning controller parameters to stabilize the system could also be based on the previously determined traditional control solutions. An investigation of controllers’ robustness against model mismatch was carried out by introducing various levels of uncertainty which influence actuators’ natural frequency to assess system sensitivity. The outcome of the investigation determined that traditional and NGMV controllers provided comparable regulating performance in terms of reference tracking and disturbance rejection, for the nominal case. This confirmed the assertion that the PID-based NGMV weightings selection is a useful starting point for controller tuning. Increasing the mismatch between the nominal system based on which the controllers’ were designed and the actual plant showed that the traditional control was marginally more robust in this application. The final contribution to knowledge this thesis aimed to bring was minimising the impact force during load placement on a fixed and rigid platform. To that end, the contact forces between the payload and a platform were first successfully modelled and measured. A switching algorithm between position and force control was then developed based on a methodology found in literature but on a microscopic scale project. To execute smooth load placement, an automated hybrid force/position control scheme was implemented. The proposed algorithm enabled position control on x and y axes, while minimising impact forces on the z-axis. Unfortunately, preliminary findings showed that there is still work to be done to claim any success in this regard. However, the author hopes this offers a good starting point for future work.As offshore wind farms become larger and further from the shore, there are strong economic and climate incentives to perform transfers required for operations and maintenance from floating vessels, rather than employing expensive and slow jack up rigs. However, successful transfers of heavy and sensitive equipment from a floating vessel (in all but benign sea/wind conditions) are heavily dependent on multiple degrees of freedom, high performance control. This project aims to bring a novel modelling and simulation methodology in Simulink that could be used to assess offshore wind installation and maintenance procedures. More specifically, the goal is to demonstrate that a crane prototype assumed to be located on a floating ship can transfer loads of hundreds of tons onto a fixed platform. Furthermore, this process should be completed with good precision and minimal impact force during equipment loading onto the stand. This problem has not yet been answered in research, with the only relevant patent in the field being the Ampelmann platform, a motionless bridge allowing technicians to access the offshore turbine. The first main contribution to knowledge of this thesis was the design of a 90 m crane that could handle a 660 tons load. This thesis presents a procedure, based on both mechanical/hydraulics design as well as empirical findings, which could be re-used for scaling the crane model to a more realistic dimension. It is worth noting that the goal here was to assess whether a realistically weighing piece of equipment could be stably handled, while the actual size of the crane was deemed unimportant. Another missing gap in literature this project wanted to fill was achieving active motion compensation for a larger scale system such as the current one. This refers to balancing out the base motions on multiple axes, so the payload can be moved on a given trajectory unaffected by them. Currently, research in the field mainly consists of crane mechanisms that feature active heave compensation, which only refers to the vertical axis. Hence, two control design methods were employed to assess the viability of heavy payload positioning from floating vessels through the development of a simulation approach using Simulink. The crane prototype was designed and modelled to operate under simulated vessel motions given by sea states with a significant wave height of 5 m and maximum wave frequency of 1 rad/s. Then, traditional control (feedback and feedforward) was designed to achieve active motion compensation with steady-state position errors under 20 cm. A second controller architecture was then designed/implemented as a comparison basis for the first one, with the aim being to find the most robust solution of the two. The nonlinear generalised minimum variance (NGMV) control algorithm was chosen for control design in this application. Due to its ability to compensate for significant system nonlinearities and the ease of implementation, NGMV was a good candidate for the task at hand. Tuning controller parameters to stabilize the system could also be based on the previously determined traditional control solutions. An investigation of controllers’ robustness against model mismatch was carried out by introducing various levels of uncertainty which influence actuators’ natural frequency to assess system sensitivity. The outcome of the investigation determined that traditional and NGMV controllers provided comparable regulating performance in terms of reference tracking and disturbance rejection, for the nominal case. This confirmed the assertion that the PID-based NGMV weightings selection is a useful starting point for controller tuning. Increasing the mismatch between the nominal system based on which the controllers’ were designed and the actual plant showed that the traditional control was marginally more robust in this application. The final contribution to knowledge this thesis aimed to bring was minimising the impact force during load placement on a fixed and rigid platform. To that end, the contact forces between the payload and a platform were first successfully modelled and measured. A switching algorithm between position and force control was then developed based on a methodology found in literature but on a microscopic scale project. To execute smooth load placement, an automated hybrid force/position control scheme was implemented. The proposed algorithm enabled position control on x and y axes, while minimising impact forces on the z-axis. Unfortunately, preliminary findings showed that there is still work to be done to claim any success in this regard. However, the author hopes this offers a good starting point for future work

An effective optimisation method for multifactor and reliability-related structural design problems

This thesis first presents a systematic design procedure which satisfies the required strength and stiffness, and structural mass for conceptual engineering structural designs. The procedure employs a multi-objective and multi-disciplinary (MO–MD) optimisation method (multifactor optimisation of structure techniques, MOST) which is coupled with finite element analysis (FEA) as an analysis tool for seeking the optimum design. The effectiveness of the MOST technique is demonstrated in two case studies.Next, a reliability-related multi-factor optimisation method is proposed and developed, representing a combination of MOST (as a method of multi-factor optimisation) and the reliability-loading case index (RLI) (as a method of calculating the reliability index). The RLI is developed based on a well-known reliability method: the first-order reliability method (FORM). The effectiveness and robustness of the proposed methodology are demonstrated in two case studies in which the method is used to simultaneously consider multi-objective, multi-disciplinary, and multi-loading-case problems. The optimised designs meet the targeted performance criteria under various loading conditions.The results show that the attributes of the proposed optimisation methods can be used to address engineering design problems which require simultaneous consideration of multi-disciplinary problems. An important contribution of this study is the development of a conceptual MO–MD design optimisation method, in which multi-factor structural and reliability design problems can be simultaneously considered

Speeding up neighborhood search in local Gaussian process prediction

Recent implementations of local approximate Gaussian process models have pushed computational boundaries for non-linear, non-parametric prediction problems, particularly when deployed as emulators for computer experiments. Their flavor of spatially independent computation accommodates massive parallelization, meaning that they can handle designs two or more orders of magnitude larger than previously. However, accomplishing that feat can still require massive supercomputing resources. Here we aim to ease that burden. We study how predictive variance is reduced as local designs are built up for prediction. We then observe how the exhaustive and discrete nature of an important search subroutine involved in building such local designs may be overly conservative. Rather, we suggest that searching the space radially, i.e., continuously along rays emanating from the predictive location of interest, is a far thriftier alternative. Our empirical work demonstrates that ray-based search yields predictors with accuracy comparable to exhaustive search, but in a fraction of the time - bringing a supercomputer implementation back onto the desktop.Comment: 24 pages, 5 figures, 4 table

ADVANCES IN SYSTEM RELIABILITY-BASED DESIGN AND PROGNOSTICS AND HEALTH MANAGEMENT (PHM) FOR SYSTEM RESILIENCE ANALYSIS AND DESIGN

Failures of engineered systems can lead to significant economic and societal losses. Despite tremendous efforts (e.g., $200 billion annually) denoted to reliability and maintenance, unexpected catastrophic failures still occurs. To minimize the losses, reliability of engineered systems must be ensured throughout their life-cycle amidst uncertain operational condition and manufacturing variability. In most engineered systems, the required system reliability level under adverse events is achieved by adding system redundancies and/or conducting system reliability-based design optimization (RBDO). However, a high level of system redundancy increases a system's life-cycle cost (LCC) and system RBDO cannot ensure the system reliability when unexpected loading/environmental conditions are applied and unexpected system failures are developed. In contrast, a new design paradigm, referred to as resilience-driven system design, can ensure highly reliable system designs under any loading/environmental conditions and system failures while considerably reducing systems' LCC. In order to facilitate the development of formal methodologies for this design paradigm, this research aims at advancing two essential and co-related research areas: Research Thrust 1 - system RBDO and Research Thrust 2 - system prognostics and health management (PHM). In Research Thrust 1, reliability analyses under uncertainty will be carried out in both component and system levels against critical failure mechanisms. In Research Thrust 2, highly accurate and robust PHM systems will be designed for engineered systems with a single or multiple time-scale(s). To demonstrate the effectiveness of the proposed system RBDO and PHM techniques, multiple engineering case studies will be presented and discussed. Following the development of Research Thrusts 1 and 2, Research Thrust 3 - resilience-driven system design will establish a theoretical basis and design framework of engineering resilience in a mathematical and statistical context, where engineering resilience will be formulated in terms of system reliability and restoration and the proposed design framework will be demonstrated with a simplified aircraft control actuator design problem

Iterative learning control in the commissioning of industrial presses

182 p.This thesis presents solutions to the control problems that exist nowadays in industrial presses, followed by a discussion of the most appropriate control schemes that may be used for their solution. Iterative Learning Control is subsequently analyzed, as the most promising control scheme for machine presses, due to its capability to improve the performance of a system that operates repeatedly.A novel Iterative Learning Control design is presented, which makes use of the dynamic characteristics of the system to improve the current controller performance and stability. This, results in an adaptation of the presented Iterative Learning Control design to two use cases: the single-input-single-output force control of mechanical presses and the multiple-input-multiple-output position control of hydraulic presses. While existing Iterative Learning Control approaches are also described and applied to the previously mentioned use cases, the presented novel approach has been shown to outperform the existing algorithms in terms of control performance.The proposed Iterative Learning control algorithms are validated in an experimental hydraulic test rig, in which the performance, robustness and stability of the algorithm have been demonstrated

Mastering Uncertainty in Mechanical Engineering

This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master uncertainty in engineering design, production and product usage alike. It gathers authoritative contributions by more than 30 scientists reporting on years of research in the areas of engineering, applied mathematics and law, thus offering a timely, comprehensive and multidisciplinary account of theories and methods for quantifying data, model and structural uncertainty, and of fundamental strategies for mastering uncertainty. It covers key concepts such as robustness, flexibility and resilience in detail. All the described methods, technologies and strategies have been validated with the help of three technical systems, i.e. the Modular Active Spring-Damper System, the Active Air Spring and the 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research. Overall, this book offers a timely, practice-oriented reference guide to graduate students, researchers and professionals dealing with uncertainty in the broad field of mechanical engineering

Design and Development of a High-Temperature High-Pressure Rolling Ball Viscometer/Densimeter and Evaluation of Star Polymer-Solvent Mixtures

Modern automotive applications such as transmission clutch plates, combustion chambers, diesel fuel injector tips, and axle gears and friction plates operate at temperatures that can exceed 250°C and pressures of 40,000 psia. Industrial practice is to add homopolymers and copolymers to base oils to modify bulk fluid viscosity and frictional properties for these demanding applications. However, designing polymeric additives for lubricants and predicting their performance is limited by the lack of available high-temperature high-pressure (HTHP) viscosity and density data needed to test contemporary lubricity models. Thus, a major objective of this thesis is the design, development, and commissioning of a rolling ball viscometer/densitometer (RBVD) capable of simultaneously determining fluid densities and viscosities at temperatures in excess of 250°C and pressures of 40,000 psia. Resulting data may then be generated to directly address the fundamental need for lubricant property data at these HTHP conditions. The design and development of the RBVD is described in detail to highlight the design iterations and modifications utilized to ensure robust operation at extreme conditions. Three significant and novel features of this RBVD apparatus that distinguish and differentiate it from other apparatus of this type are: (1) specially designed metal-to-metal and sapphire-to-metal seated surfaces capable of eliminating temperature- and chemically-sensitive elastomeric seals; (2) use of a bellows piston to eliminate significant temperature and operational constraints; and (3) incorporation of a linear variable differential transducer (LVDT) to simultaneously permit determination of solution density and viscosity. A detailed analysis of initial accumulated uncertainty for the experimental viscosity and density techniques revealed the need for key RBVD modifications. Final data are presented showing that the RBVD is capable of measuring viscosities with an accuracy of ± 2 to 3 percent and densities to ± 0.7 percent, including at the extreme operating conditions targeted. A second objective of this thesis is the measurement of HTHP viscosities of star polymer-solvent mixtures to determine the impact of star polymer architecture on solution viscosity at extreme conditions similar to those that might be experienced in automotive applications. This objective is motivated by current challenges facing industry to identify polymeric additives that can be added to base oils to improve fuel economy and allow for the implementation of novel hardware technology that relies on enhanced lubricant properties. Relative to linear polymers, the unique architecture of star polymers enhances polymer solubility in base oils while having a more favorable impact on viscosity and density properties over a wide range of temperatures and pressures. Data are presented for an industrially-relevant star polymer in octane to assess the impact of the star configuration on solvent viscosity at extreme conditions. The star polymer used in this instance consists of an ethylene glycol dimethacrylate (EGDMA) core with poly(lauryl methacrylate-co-methyl methacrylate) (LMA-MMA) arms. The star polymer has a total weight averaged molecular weight (Mw) and Mw of each arm of 575,000, and 45,000, respectively. The copolymer arms of the star polymer have an LMA-to-MMA mole ratio of 0.6. The results of further viscosity studies are presented for a model system of well-characterized commercially available narrow polydispersity index (PDI) star polystyrenes (PS) in toluene. Each PS is evaluated at a two percent by weight concentration in toluene to evaluate the effect of arm molecular weigh on viscosity. Each three-arm star polymer has arm and total molecular weights ([arm Mw] total star Mw) of ([15,400] 41,200), ([36,000] 97,600), and ([108,000] 305,000). In this instance, the viscosity of toluene increased by more than a factor of three for the star with the highest Mw arms