Real-time measurement of oxides of nitrogen from heavy-duty diesel engines

New emissions regulations and performance requirements imposed on modern diesel engines encourage the development of improved tools for emissions measurements. This study investigated one of the improved tools for NOx emissions measurement. Objectives of this thesis included measurement, comparison and prediction of NOx emissions from heavy-duty diesel engines using two different NOx analyzers. Steady state and transient tests were conducted on six heavy-duty diesel engines in the WVU Engine and Emissions Research Center (EERC). NOx emissions were measured using a conventional Rosemount NOx analyzer and a Cambustion fast response NOx analyzer. The Rosemount analyzer sampled dilute emissions whereas the fast NOx analyzer was capable of sampling both raw and dilute NO x emissions. Test data obtained from both the analyzers were compared and contrasted. It was observed that there occurred a time shift and dispersion in the measured NOx emissions due to the delay and diffusion effects of the sampling train and the difference in response time of each of the analyzers. A difference of about 8--10% was observed between the measured values of NOx emissions from the two analyzers. Instantaneous emissions data obtained from the fast NOx analyzer were used to create emissions inventory tables for further analysis. These data were used to deduce power-based fast NOx emissions prediction models, which could predict instantaneous NOx emissions for different engines and cycles within an error range of 5--13%. An attempt was also made to derive algebraic backward transformation equations for predicting the instantaneous engine out emissions (raw) from the dispersed (dilute) emissions

Development of a Pipe Crawler Inspection Tool for Fossil Energy Power Plants

Fossil fuel power plants are complex systems containing multiple components that create extreme environments for the purpose of extracting usable energy. Failures in the system can lead to increased down time for the plant, reduction of power and significant cost for repairs. In the past, inspections and maintenance of the plant\u27s superheater tubes has been predominantly manual, laborious, and extremely time consuming. This is due to the pipe\u27s small diameter size (between 1.3 and 7.6 cm) and the coiled structure of the tubing. In addition, the tubes are often stacked close to each other, limiting access for external inspection. Detection of pipe degradation, such as increased levels of corrosion, creep, and the formation of micro-cracks is possible using standard non-destructive evaluation (NDE) methods, including ultrasonic, radiography and electromagnetic methods. However, when the access to the sub-systems is limited or the configuration of the structure is prohibitive, alternative methods are needed for deploying the NDE tools. This research effort considers a novel robotic inspection system for the evaluation of small pipes found in typical boiler superheaters that have limited access. The pipe crawler system is an internal inspection device that can potentially navigate through the entire pipe length using linear actuators to grip the walls and inch along the pipe. The modular nature of the system allows it to traverse through straight sections and multiple 90-degree and 180-degree bends. The crawler is also capable of providing visual inspections, ultrasonic thickness measurements, and generating inner diameter surface maps using LiDAR (light detection and ranging). Ultimately, the development of this robotic inspection tool can provide information regarding the structural integrity of key pipeline components in fossil fuel power plants that are not easily accessible

Hierarchical tactile sensation integration from prosthetic fingertips enables multi-texture surface recognition\u3csup\u3e†\u3c/sup\u3e

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands

Multi-objective Design Optimization of Engineering Systems: Uncertainty Approach and Practical Applications.

The increase in complexity of optimization problems results in an emerging need for simpler, faster and non-classical solutions. One of the options is conversion of a traditional non-hierarchical optimization system to a hierarchical system using an approach called multi-level (ML) decomposition (for optimization). Most of the work in the literature deals with the application of multi-level approach to deterministic optimization problems. But, in nature, many applications are uncertain, and hence, it is realistic to introduce uncertainty in the analysis and optimization. The first part of the present research deals with the development of a multi-level optimization procedure for uncertain engineering systems. The uncertainty in the problem is assumed to be stochastic and interval in nature. The methodology developed is illustrated by considering the optimization of structural and mechanical engineering problems. The second part of the present study deals in modifying a relatively new swarm intelligence technique based on the foraging behavior of ants called Ant Colony Optimization (ACO). A new multi-objective ant colony optimization algorithm is developed and applied to structural and mechanical engineering problems. The illustrative examples in the present research include the design optimization of an electric transmission tower (space truss), plane truss, gear box and the combustion chamber of an internal combustion engine. The third part of the research attempts to apply optimization techniques to practical engineering systems in the field of Heating Ventilation and Air Conditioning (HVAC) and Micro-Electronics. Novel design optimization models are created and hybrid optimization algorithms are developed for chiller plants and micro-channel heat exchangers used in electronic cooling. Illustrative case studies are performed

Multi-objective Design Optimization of Engineering Systems: Uncertainty Approach and Practical Applications.

The increase in complexity of optimization problems results in an emerging need for simpler, faster and non-classical solutions. One of the options is conversion of a traditional non-hierarchical optimization system to a hierarchical system using an approach called multi-level (ML) decomposition (for optimization). Most of the work in the literature deals with the application of multi-level approach to deterministic optimization problems. But, in nature, many applications are uncertain, and hence, it is realistic to introduce uncertainty in the analysis and optimization. The first part of the present research deals with the development of a multi-level optimization procedure for uncertain engineering systems. The uncertainty in the problem is assumed to be stochastic and interval in nature. The methodology developed is illustrated by considering the optimization of structural and mechanical engineering problems. The second part of the present study deals in modifying a relatively new swarm intelligence technique based on the foraging behavior of ants called Ant Colony Optimization (ACO). A new multi-objective ant colony optimization algorithm is developed and applied to structural and mechanical engineering problems. The illustrative examples in the present research include the design optimization of an electric transmission tower (space truss), plane truss, gear box and the combustion chamber of an internal combustion engine. The third part of the research attempts to apply optimization techniques to practical engineering systems in the field of Heating Ventilation and Air Conditioning (HVAC) and Micro-Electronics. Novel design optimization models are created and hybrid optimization algorithms are developed for chiller plants and micro-channel heat exchangers used in electronic cooling. Illustrative case studies are performed

Energy optimization in chiller plants: A novel formulation and solution using a hybrid optimization technique

The central chilled water plant is one of the major power-consuming units of a building. Even small reductions in power consumption could achieve significant energy conservation. Hence, optimization of a chiller plant is necessary for energy savings without compromising the comfort level of the end user. The present work deals with identifying the system parameters and developing a novel formulation for a chiller plant and its optimization using a hybrid optimization technique. The optimization model formulation is based on finding an optimal mix of equipment and operating parameters in the chiller plant for minimum electrical power consumption. It takes into account the performance characteristics of the chillers, cooling towers and pumps, and optimizes the energy consumed based on the required loads and the ambient atmospheric conditions. Sequential quadratic programming combined with the modified branch and bound method was used to develop the hybrid optimization algorithm. A case study is presented for a typical chiller plant. The results indicate that the present optimization method could be a potential method of making energy savings

Design Optimization of Micro-channel Heat Exchanger embedded in LTCC

Increase in the density of electronic packaging leads to the investigation of highly efficient thermal management systems. The challenge in these micro-systems is to maximize heat transfer per unit volume. In the author's previous work, experimental and computational analysis has been performed on LTCC substrates using embedded silver vias. This novel technique of embedding silver vias along with forced convection resulted in higher heat transfer rates. The present work further investigates into the optimization of this model. A Multi-objective optimization problem has been formulated for the heat transfer in the LTCC model. The Log Mean Temperature Difference (LMTD) method of heat exchangers has been used in the formulation. Optimization is done based on maximization of the total heat transferred and minimization of the coolant pumping power. Structural and thermal design variables are considered to meet the manufacturability and energy requirements. Demanded pressure loss and volume of the silver metal are used as constraints. The classical optimization technique Sequential Quadratic Programming (SQP) is used to solve the micro-heat exchanger problem. The optimal design is presented and sensitivity analysis results are discussed

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands

Novel Reproducible Manufacturing and Reversible Sealing Method for Microfluidic Devices

Conventional manufacturing methods for polydimethylsiloxane (PDMS)-based microdevices require multiple steps and elements that increase cost and production time. Also, these PDMS microdevices are mostly limited to single use, and it is difficult to recover the contents inside the microchannels or perform advanced microscopy visualization due to their irreversible sealing method. Herein, we developed a novel manufacturing method based on polymethylmethacrylate (PMMA) plates adjusted using a mechanical pressure-based system. One conformation of the PMMA plate assembly system allows the reproducible manufacture of PDMS replicas, reducing the cost since a precise amount of PDMS is used, and the PDMS replicas show uniform dimensions. A second form of assembling the PMMA plates permits pressure-based sealing of the PDMS layer with a glass base. By reversibly sealing the microdevice without using plasma for bonding, we achieve chip on/off configurations, which allow the user to open and close the device and reuse it in an easy-to-use way. No deformation was observed on the structures of the PDMS microchannels when a range of 10 to 18 kPa pressure was applied using the technique. Furthermore, the functionality of the proposed system was successfully validated by the generation of microdroplets with reused microdevices via three repetitions