Multizone modeling of a fumigated diesel engine

A phenomenological multizone transient spray model has been developed to simulate the performance and nitric oxide emission characteristics of a turbocharged diesel engine fumigated with alcohol. The effects of speed, load, alcohol proof, and the fraction of the engine\u27s power supplied by the alcohol have been investigated. The multizone model is designed to account for the heterogeneous composition of the cylinder contents by dividing the cylinder into a number of smaller zones. Each of these smaller divisions is treated as being locally homogeneous. The model includes the interactions between the fuel spray and swirling air in the cylinder. The effect of wall impingement on the fuel-air mixing and combustion has been incorporated in the model. Correlations for spray geometry, fuel-air distribution in the spray plume, air entrainment, and heat transfer have been used to develop the model. A complete thermodynamic analysis has been applied to the individual zones to obtain cylinder pressure data. The indicated mean effective pressure and indicated thermal efficiency are derived from the pressure data. The exhaust temperature is estimated using a simple two-step blowdown and expansion process. The exhaust nitric oxide level is predicted by a chemical kinetic model applied to each individual zone;To validate the model, a four-cylinder turbocharged diesel engine was operated over a large matrix of different conditions. A mixture of alcohol and water was fumigated into the intake manifold after the turbocharger compressor. The engine was operated at two different speeds and four different load conditions. Varying amounts of alcohol were fumigated to replace 10, 20, 30, and 40% of the full load torque. Alcohol-water mixtures of varying proofs. between 40 and 169.5 proof, were tested. Comparisons were made between ethanol-water, and methanol-water mixtures on an equal energy basis that includes the enthalpy of vaporization of the alcohol and water. Tests with water injection into the intake manifold were also carried out to study the effect of water contained in the alcohol-water mixture on engine performance;The values of indicated mean effective pressure, indicated thermal efficiency, and exhaust temperature predicted by the model matched closely with experimental data. The general trends in the three parameters were also correctly predicted by the model. The exhaust nitric oxide emission predicted by the model matched reasonably well for diesel-only operation. With alcohol fumigation, the model overpredicts the reduction in nitric oxide. This is attributed to the inability of a single-zone wall impingement model to accurately simulate the temperature and composition of the regions where nitric oxide is formed

Multizone modeling of a fumigated diesel engine

A phenomenological multizone transient spray model has been developed to simulate the performance and nitric oxide emission characteristics of a turbocharged diesel engine fumigated with alcohol. The effects of speed, load, alcohol proof, and the fraction of the engine's power supplied by the alcohol have been investigated. The multizone model is designed to account for the heterogeneous composition of the cylinder contents by dividing the cylinder into a number of smaller zones. Each of these smaller divisions is treated as being locally homogeneous. The model includes the interactions between the fuel spray and swirling air in the cylinder. The effect of wall impingement on the fuel-air mixing and combustion has been incorporated in the model. Correlations for spray geometry, fuel-air distribution in the spray plume, air entrainment, and heat transfer have been used to develop the model. A complete thermodynamic analysis has been applied to the individual zones to obtain cylinder pressure data. The indicated mean effective pressure and indicated thermal efficiency are derived from the pressure data. The exhaust temperature is estimated using a simple two-step blowdown and expansion process. The exhaust nitric oxide level is predicted by a chemical kinetic model applied to each individual zone;To validate the model, a four-cylinder turbocharged diesel engine was operated over a large matrix of different conditions. A mixture of alcohol and water was fumigated into the intake manifold after the turbocharger compressor. The engine was operated at two different speeds and four different load conditions. Varying amounts of alcohol were fumigated to replace 10, 20, 30, and 40% of the full load torque. Alcohol-water mixtures of varying proofs. between 40 and 169.5 proof, were tested. Comparisons were made between ethanol-water, and methanol-water mixtures on an equal energy basis that includes the enthalpy of vaporization of the alcohol and water. Tests with water injection into the intake manifold were also carried out to study the effect of water contained in the alcohol-water mixture on engine performance;The values of indicated mean effective pressure, indicated thermal efficiency, and exhaust temperature predicted by the model matched closely with experimental data. The general trends in the three parameters were also correctly predicted by the model. The exhaust nitric oxide emission predicted by the model matched reasonably well for diesel-only operation. With alcohol fumigation, the model overpredicts the reduction in nitric oxide. This is attributed to the inability of a single-zone wall impingement model to accurately simulate the temperature and composition of the regions where nitric oxide is formed.</p

Effective Optimization of Deployment for Wearable Sensors in Transfemoral Prosthesis

Transfemoralor above-the-knee amputees face discomfort in their prothesis primarily due to irregular distribution of pressure and shear forces in the Socket-stump interface (SSI). To quantify this discomfort it is necessary to first determine the pressure distribution in the SSI using sensors. However, knowledge of how sensors should be deployed is necessary to support the testing of said pressure on a test-rig or amputee. Previous methods used to determine sensor placement include discretization of the SSI into several regions or the use of a reiterative method based on pressure readings from sensors to determine the optimal placement of sensors. The former fails to identify high regions of pressure as the regions covered by the sensors may not have high pressure whereas the latter is time consuming and may cause further trauma to amputees as it requires repeated experimentation. With the advances in pressure sensor technologies, biomechanical simulations, and Finite elementanalysis(FEA)simulations it is now increasingly possible to determine an accurate estimate of dynamic pressure distribution occurring in the SSI during the gait cycle. The thesis investigates the dynamic pressure distribution in the SSI and determines an effective method of locating the optimal positions for the sensors using two different algorithms. The first is a Genetic Algorithm whereas the second is Pattern Search.Transfemorala eller amputerade över knäet möter obehag i sin protes främst på grund av oregelbunden fördelning av tryck och skjuvkrafter i SSI. För att kvantifiera detta obehag är det nödvändigt att först bestämma tryckfördelningen i SSI med hjälp av sensorer. Men kunskap om hur sensorer ska distribueras är nödvändig för att stödja testningen av nämnda tryck på en testrigg eller amputerad. Tidigare metoder som använts för att bestämma sensorplacering inkluderar diskretisering av SSI i flera regioner eller användning av en upprepad metod baserad på tryckavläsningar från sensorer för att bestämma den optimala placeringen av sensorer. Den förstnämnda misslyckas med att identifiera höga tryckregioner eftersom den områden som täcks av sensorerna kanske inte har högt tryck medan de senare är tidskrävande och kan orsaka ytterligare trauma för amputerade eftersom det kräver upprepade experiment. Med framstegen inom trycksensorteknologier, biomekaniska simuleringar och FEA-simuleringar är det nu alltmer möjligt att bestämma en exakt uppskattning av dynamisk tryckfördelning i SSI under gångcykeln. Avhandlingen undersöker den dynamiska tryckfördelningen i SSI och bestämmer en effektiv metod för att lokalisera de optimala positionerna för sensorerna med hjälp av två olika algoritmer. Den första är en genetisk algoritm medan den andra är mönstresöknin

Effective Optimization of Deployment for Wearable Sensors in Transfemoral Prosthesis

Transfemoralor above-the-knee amputees face discomfort in their prothesis primarily due to irregular distribution of pressure and shear forces in the Socket-stump interface (SSI). To quantify this discomfort it is necessary to first determine the pressure distribution in the SSI using sensors. However, knowledge of how sensors should be deployed is necessary to support the testing of said pressure on a test-rig or amputee. Previous methods used to determine sensor placement include discretization of the SSI into several regions or the use of a reiterative method based on pressure readings from sensors to determine the optimal placement of sensors. The former fails to identify high regions of pressure as the regions covered by the sensors may not have high pressure whereas the latter is time consuming and may cause further trauma to amputees as it requires repeated experimentation. With the advances in pressure sensor technologies, biomechanical simulations, and Finite elementanalysis(FEA)simulations it is now increasingly possible to determine an accurate estimate of dynamic pressure distribution occurring in the SSI during the gait cycle. The thesis investigates the dynamic pressure distribution in the SSI and determines an effective method of locating the optimal positions for the sensors using two different algorithms. The first is a Genetic Algorithm whereas the second is Pattern Search.Transfemorala eller amputerade över knäet möter obehag i sin protes främst på grund av oregelbunden fördelning av tryck och skjuvkrafter i SSI. För att kvantifiera detta obehag är det nödvändigt att först bestämma tryckfördelningen i SSI med hjälp av sensorer. Men kunskap om hur sensorer ska distribueras är nödvändig för att stödja testningen av nämnda tryck på en testrigg eller amputerad. Tidigare metoder som använts för att bestämma sensorplacering inkluderar diskretisering av SSI i flera regioner eller användning av en upprepad metod baserad på tryckavläsningar från sensorer för att bestämma den optimala placeringen av sensorer. Den förstnämnda misslyckas med att identifiera höga tryckregioner eftersom den områden som täcks av sensorerna kanske inte har högt tryck medan de senare är tidskrävande och kan orsaka ytterligare trauma för amputerade eftersom det kräver upprepade experiment. Med framstegen inom trycksensorteknologier, biomekaniska simuleringar och FEA-simuleringar är det nu alltmer möjligt att bestämma en exakt uppskattning av dynamisk tryckfördelning i SSI under gångcykeln. Avhandlingen undersöker den dynamiska tryckfördelningen i SSI och bestämmer en effektiv metod för att lokalisera de optimala positionerna för sensorerna med hjälp av två olika algoritmer. Den första är en genetisk algoritm medan den andra är mönstresöknin

A Mechatronics-twin Framework based on Stewart Platform for Effective Exploration of Operational Behaviors of Prosthetic Sockets with Amputees

A Stewart platform is a six-degree-of-freedom parallel manipulator widely used as the motion base for flight simulators, antenna positioning systems, machine tool technology, etc. This work presents a novel mechatronics-twin framework that integrates such a manipulator with advanced biomechanical models and simulations for effective exploration of operational behaviors of prosthetic sockets with amputees. By means of the biomechanical models and simulations, the framework allows the users to first analyze the fundamental operational characteristics of individual amputees according to their specific body geometries, pelvis-femur structures, sizes of transfemoral sockets, etc. Such operational characteristics are then fed to one Stewart platform as the reference control signals for the generation of dynamic loads and behaviors of prosthetic sockets that are otherwise difficult to observe or realize with the real amputees. Experiments in form of integration testing show that the proposed control strategy is capable of generating expected dynamic operational conditions. Currently, the mechatronics-twin framework supports a wide range of biomechanical configurations and the quantification of the respective intra-socket load conditions for socket design optimization and anomaly detection.QC 20220517SocketSens

Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework

Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous

Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework

Lower limb prostheses offer a solution to restore the ambulation and self-esteem of am-putees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore neces-sary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous. © 2022 by the authorsLicensee MDPI, Basel, Switzerland

Using a VAE-SOM architecture for anomaly detection of flexible sensors in limb prosthesis

Flexible wearable sensor electronics, combined with advanced software functions, pave the way toward increasingly intelligent healthcare devices. One important application area is limb prosthesis, where printed flexible sensor solutions enable efficient monitoring and assessing of the actual intra-socket dynamic operation conditions in clinical and other more natural environments. However, the data collected by such sensors suffer from variations and errors, leading to difficulty in perceiving the actual operational conditions. This paper proposes a novel method for detecting anomalies in the data that are collected for measuring the intra-socket dynamic operation conditions by printed flexible wearable sensors. A discrete generative model based on Variational AutoEncoder (VAE) is used first to encode the collected multi-variant time-series data in terms of latent states. After that, a clustering method based on the Self-Organizing Map (SOM) is used to acquire discrete and interpretable representations of the VAE encoded latent states. An adaptive Markov chain is utilized to detect anomalies by quantifying state transitions and revealing temporal dependencies. The contributions of the proposed architecture conclude as follows: (1) Using the VAE-SOM hybrid model to regularize the continues data as discrete states, supporting interpreting the operational data to analytic models. (2) Employing adaptive Markov chains to generalize the transitions of these states, allowing to model the complex operational conditions. Compared with benchmark methods, our architecture is validated via two public datasets and achieves the best F1 scores. Moreover, we measure the run-time performance of this lightweight architecture. The results indicate that the proposed method performs low computational complexity, facilitating the applications on real-life productions.QC 20230816</p