Dynamic modeling of disc brake contact phenomena

Interakcija između diska kočnice i frikcionog materijala disk kočnice motornih vozila se odlikuje velikim brojem kontaktnih fenomena. Nastanak ovih fenomena je vezan za radne uslove kočnice (pritisak aktiviranja, brzina, temperatura u kontaktu) kao i za karakteristike materijala frikcionog para. Dinamičke i izraženo nelinearne promene, koje se dešavaju u kontaktu frikcionog para, izazivaju teško predvidivu promenu momenta kočenja, kao najvažnije izlazne performanse kočnice. Složena situacija u kontaktu frikcionog para se ne može lako modelirati i predvideti korišćenjem klasičnih matematičkih metoda. Zbog toga su istraživane mogućnosti razvoja metode za predviđanje uticaja radnih režima disk kočnice na pojavu tzv. 'stickslip' fenomena tokom ciklusa kočenja. Korišćenjem dinamičkih neuronskih mreža, razvijen je dinamički model uticaja radnih uslova disk kočnice na pojavu kontaktnih fenomena i način promene momenta kočenja.An interaction between a brake disc and friction material of automotive brake is characterized by a number of braking phenomena. These phenomena are influenced by brake operation conditions (applied pressure, speed, and brake interface temperature) and material characteristics of a friction couple. The dynamic and highly non-linear changes occurred in the contact of the friction pair, provokes hard-to-predict change of braking torque as the most important brake's output performance. Complex disc brake contact situation is causing sudden change of braking torque and could not be easily modeled and predicted using classical mathematical methods. That is why, the possibilities for development of the method for prediction of influence of braking regimes on generation of the stick-slip phenomena during a braking cycle has been investigated in this paper. Dynamic neural networks have been employed for development of the model of influences of the disc brake operation conditions on contact phenomena generation and 'nature' of braking torque change

Dynamic modeling of disc brake contact phenomena

Interakcija između diska kočnice i frikcionog materijala disk kočnice motornih vozila se odlikuje velikim brojem kontaktnih fenomena. Nastanak ovih fenomena je vezan za radne uslove kočnice (pritisak aktiviranja, brzina, temperatura u kontaktu) kao i za karakteristike materijala frikcionog para. Dinamičke i izraženo nelinearne promene, koje se dešavaju u kontaktu frikcionog para, izazivaju teško predvidivu promenu momenta kočenja, kao najvažnije izlazne performanse kočnice. Složena situacija u kontaktu frikcionog para se ne može lako modelirati i predvideti korišćenjem klasičnih matematičkih metoda. Zbog toga su istraživane mogućnosti razvoja metode za predviđanje uticaja radnih režima disk kočnice na pojavu tzv. 'stickslip' fenomena tokom ciklusa kočenja. Korišćenjem dinamičkih neuronskih mreža, razvijen je dinamički model uticaja radnih uslova disk kočnice na pojavu kontaktnih fenomena i način promene momenta kočenja.An interaction between a brake disc and friction material of automotive brake is characterized by a number of braking phenomena. These phenomena are influenced by brake operation conditions (applied pressure, speed, and brake interface temperature) and material characteristics of a friction couple. The dynamic and highly non-linear changes occurred in the contact of the friction pair, provokes hard-to-predict change of braking torque as the most important brake's output performance. Complex disc brake contact situation is causing sudden change of braking torque and could not be easily modeled and predicted using classical mathematical methods. That is why, the possibilities for development of the method for prediction of influence of braking regimes on generation of the stick-slip phenomena during a braking cycle has been investigated in this paper. Dynamic neural networks have been employed for development of the model of influences of the disc brake operation conditions on contact phenomena generation and 'nature' of braking torque change

Prediction of Electricity Consumption of a HVAC System in a Multi-Complex Building Using Back Propagation and Radial Basis Function Neural Networks

This study examined approaches to predict electricity consumption of a Heating, Ventilation and Air- Conditioning (HVAC) system in a multi-complex building using two neural network models: Back Propagation (BP) and Radial Basis Function (RBF) with input nodes, e.g., temperature, humidity ratio, and wind speed. Predicting HVAC energy consumption of buildings is a crucial part of energy management systems. We used two main neural network models, BP and RBF, to evaluate the prediction performance of electricity consumption of HVAC systems. The BP neural network method exhibited good performance, but it exhibited relatively large fluctuations and slow convergence in the training process. In contrast, RBF exhibited relatively fast learning and reduced computing costs. The HVAC energy consumption rate of working days was higher than that of non-working days. The results indicate that the prediction of HVAC energy consumption using neural networks can effectively control the relationship between the HVAC system and environment conditions.publishedVersio

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Machine assisted quantitative seismic interpretation

During the past decades, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. Reducing the labor associated with seismic interpretation while increasing the reliability of the interpreted result has been an on going challenge that becomes increasingly more difficult with the amount of data available to interpreters. To address this issue, geoscientists often adopt concepts and algorithms from fields such as image processing, signal processing, and statistics, with much of the focus on auto-picking and automatic seismic facies analysis. I focus my research on adapting and improving machine learning and pattern recognition methods for automatic seismic facies analysis. Being an emerging and rapid developing topic, there is an endless list of machine learning and pattern recognition techniques available to scientific researchers. More often, the obstacle that prevents geoscientists from using such techniques is the “black box” nature of such techniques. Interpreters may not know the assumptions and limitations of a given technique, resulting in subsequent choices that may be suboptimum. In this dissertation, I provide a review of the more commonly used seismic facies analysis algorithms. My goal is to assist seismic interpreters in choosing the best method for a specific problem. Moreover, because all these methods are just generic mathematic tools that solve highly abstract, analytical problems, we have to tailor them to fit seismic interpretation problems. Self-organizing map (SOM) is a popular unsupervised learning technique that interpreters use to explore seismic facies using multiple seismic attributes as input. It projects the high dimensional seismic attribute data onto a lower dimensional (usually 2D) space in which interpreters are able to identify clusters of seismic facies. In this dissertation, using SOM as an example, I provide three improvements on the traditional algorithm, in order to present the information residing in the seismic attributes more adequately, and therefore reducing the uncertainly in the generated seismic facies map

The potentional application of artificial intelligence in motor vehicles braking system performance

Osnovni zahtevi koji se postavljaju pred današnje kočne sisteme motornih i priključnih vozila, u pogledu bezbednosti vozila i saobraćaja, se odnose na njihovo dalje unapređenje kroz razvoj novih, inteligentnih, rešenja. Suština ovih zahteva jeste da se omogući pomoć vozaču kroz inteligentno upravljanje sistemima na vozilu, odnosno njihovim performansama u različitim, dinamički promenljivim, radnim uslovima. Pošto kočne performanse vozila zavise od performansi kočnica, koje funkcionišu na principima trenja i samim tim imaju vrlo nepredvidiv karakter, i od usklađenosti tih performansi sa trenutnim uslovima prijanjanja u kontaktu pneumatika sa tlom, koji se mogu intenzivno menjati tokom samo jednog ciklusa kočenja, realizacija ovih zahteva je izuzetno kompleksna. To je osnovni razlog za sprovođenje istraživanja u pogledu razvoja i implementacije inteligentnijih načina upravljanja performansama kočnog sistema na osnovu uslova prijanjanja u kontaktu pneumatik–tlo. U ovoj doktorskoj disertaciji su istraživane mogućnosti primene tehnika iz oblasti veštačke inteligencije u cilju modeliranja složenih dinamičkih uticaja radnih režima kočnica motornih vozila i uslova u kontaktu pneumatik–tlo, kao i predviđanja ovih uticaja u cilju upravljanja performansama kočnica, a time i performansama kočnog sistema, u toku ciklusa kočenja. Zbog nemogućnosti modeliranja složenih dinamičkih uticaja radnih režima kočnica motornih vozila na njihove izlazne performanse, odnosno na vrednosti klizanja u kontaktu pneumatika i puta pomoću klasičnih matematičkih metoda, uvedena je nova inteligentna metoda bazirana na dinamičkim veštačkim neuronskim mrežama i fazi logici. U skladu sa time, u ovoj disertaciji su istraživane mogućnosti primene dinamičkih veštačkih neuronskih mreža i fazi logike u cilju modeliranja, predviđanja i inteligentnog upravljanja performansama kočnica, odnosno performansama kočnog sistema. Predmetno istraživanje je usmereno ka razvoju sposobnosti kočnog sistema ka inteligentnom prilagođavanju sile kočenja dinamičkim promenama podužnog klizanja točka (pneumatika) u kontaktu sa putem u toku ciklusa kočenja. Ovakav koncept upravljanja performansama kočnog sistema, na osnovu prethodnih i trenutnih vrednosti posmatranih uticajnih veličina i identifikovanih uslova prijanjanja tokom kočenja, podrazumeva predviđanje potrebne vrednosti pritisaka aktiviranja kočnica, na prednjoj i zadnjoj osovini, za date uslove kočenja (vrednosti pritiska aktiviranja kočnice, vrednosti brzine točka na prednjoj/zadnjoj osovini, temperature u kontaktu frikcionog para kočnice na prednjoj/zadnjoj osovini i vrednosti klizanja u kontaktu pneumatik–tlo) kako bi se u kontaktu pneumatika i tla postiglo željeno (optimalno) klizanje u podužnom pravcu.In terms of vehicle and traffic safety, the main demands imposed to the braking systems of motor vehicles and trailers are related to their further improvement through development of new, intelligent, solutions. It could enable the driver assistance function through an intelligent control of the vehicle systems performance in different and dynamically changing operating conditions. Since the braking performance of vehicles depend on the performance of the brakes, which based their function on the friction, it is a difficult to control stochastically changed the brakes performance. Furthermore, harmonization of that performance with the actual conditions in the tire-road contact, which is also intensively changed during a braking cycle, the realization of demands towards an intelligent control the braking system performance is very complex. This is the main reason for conducting research regarding development and implementation of more intelligent ways for control of the braking system performance. In this doctoral thesis, possibilities for employing of an artificial intelligence have been investigated in order to model and predict the impact of the brakes operating regimes and the complex conditions in the tire-road contact in order to provide intelligent controlling of the braking system performance during a braking cycle. Due to the impossibility for modeling of complex dynamic influences of brakes’ operating conditions on their performance and consequently on the value of the longitudinal wheel slip using conventional mathematical methods, a new method has been introduced based on an integration of dynamic neural networks and fuzzy logic. Accordingly, this thesis investigated possibilities for the proper integration of dynamic artificial neural networks and fuzzy logic in modeling, prediction, and intelligent control of the brakes’ performance, i.e. performance of the braking system. It should provide inherent capabilities of the braking system towards an intelligent adaptation of the braking forces to the dynamic changes of the longitudinal slip ratio in the tire–road contact during a braking cycle. This concept for control of the braking system performance, based on previous and current values of observed influential factors, means predicting of the brake applied pressure values, on the front and rear axle, for the given braking conditions (brake applied pressure, wheel speed on the front/rear axle, brake interface temperature on the front/rear axle, and wheel slip) in order to achieve the desired and/or optimal slip level in the longitudinal direction. Furthermore, the braking system should continuously learn about the complex and stochastic influences between these factors during a braking cycle. Since this is especially important for commercial vehicles, the focus of research has been directed on possibilities for improving the performance of electronically controlled braking system. It is done not only to achieve the optimal value of the longitudinal wheel slip in the tire-road contact, but also enables later optimization of the lateral wheel slip