The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Peter Lindroth - One of the best experts on this subject based on the ideXlab platform.
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Modelling of optimal tyres selection for a certain truck and transport application
International Journal of Vehicle Systems Modelling and Testing, 2017Co-Authors: Zuzana Nedelkova, Peter Lindroth, Bengt J H JacobsonAbstract:The main aim of the research leading to this paper is to select---for a truck and its transport application---a configuration of the tyres such that the energy losses caused by these are minimized. We show that neither the Rolling Resistance Coefficient (RRC) classes provided by tyre suppliers nor any other nominal values of RRC evaluated for specific operating conditions are sufficient to do the tyres selection. Therefore, a surrogate model of the RRC is developed. A tyre model based on the RRC model is introduced in this paper. The modularity of the tyre model is demonstrated by coupling it with two different vehicle models and an~operating environment model. The usage of the joint vehicle, tyres, and operating environment model is demonstrated by solving a few illustrative instances of the tyres selection problem. The potential savings wrt. energy losses when the selected tyre configurations are used are presented.
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Integration of expert knowledge into radial basis function surrogate models
Optimization and Engineering, 2016Co-Authors: Zuzana Nedělková, Peter Lindroth, Ann-brith Strömberg, Michael PatrikssonAbstract:A current application in a collaboration between Chalmers University of Technology and Volvo Group Trucks Technology concerns the global optimization of a complex simulation-based function describing the Rolling Resistance Coefficient (RRC) of a truck tyre. This function is crucial for the optimization of truck tyres selection considered. The need to explicitly describe and optimize this function provided the main motivation for the research presented in this article. Many optimization algorithms for simulation-based optimization problems use sample points to create a computationally simple surrogate model of the objective function. Typically, not all important characteristics of the complex function (as, e.g., non-negativity)—here referred to as expert knowledge—are automatically inherited by the surrogate model. We demonstrate the integration of several types of expert knowledge into a radial basis function interpolation. The methodology is first illustrated on a simple example function and then applied to a function describing the RRC of truck tyres. Our numerical results indicate that expert knowledge can be advantageously incorporated and utilized when creating global approximations of unknown functions from sample points.
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Integration of expert knowledge into radial basis function surrogate models
Optimization and Engineering, 2015Co-Authors: Zuzana Nedelkova, Peter Lindroth, Ann-brith Strömberg, Michael PatrikssonAbstract:A current application in a collaboration between Chalmers University of Technology and Volvo Group Trucks Technology concerns the global optimization of a complex simulation-based function describing the Rolling Resistance Coefficient of a truck tyre. This function is crucial for the optimization of truck tyres selection considered. The need to explicitly describe and optimize this function provided the main motivation for the research presented in this article. Many optimization algorithms for simulation-based optimization problems use sample points to create a computationally simple surrogate model of the objective function. Typically, not all important characteristics of the complex function (as, e.g., non-negativity)—here referred to as expert knowledge—are automatically inherited by the surrogate model. We demonstrate the integration of several types of expert knowledge into a radial basis function interpolation. The methodology is first illustrated on a simple example function and then applied to a function describing the Rolling Resistance Coefficient of truck tyres. Our numerical results indicate that expert knowledge can be advantageously incorporated and utilized when creating global approximations of unknown functions from sample points.
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A joint model of vehicle, tyres, and operation for the optimization of truck tyres
2015Co-Authors: Zuzana Sabartova, Bengt J H Jacobson, Peter LindrothAbstract:This paper introduces the truck tyres selection optimization problem, which is to be solved in a research project, the overall goal of which is to identify—for each vehicle and each operating environment specification—an optimal tyre configuration. The paper focuses on the joint vehicle, tyres, and operating environment model developed within the project. The tyres are modelled using regression models of lateral and vertical stiffness of the tyre and a computationally efficient model of the Rolling Resistance Coefficient (RRC) based on a radial basis function interpolation of sample points, which are evaluated by a finite element model of the tyre. The interpolation is combined with existing expert knowledge about the true RRC function. The joint model developed is validated through investigations of how certain functional properties, such as the fuel consumption, vary with the tyre design variables as well as with some operating parameters. The validation results show that the model developed approximates the tyre-related functions well enough and can be used in the truck tyres selection optimization problem.
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An optimization model for truck tyres selection
Engineering Optimization 2014, 2014Co-Authors: Zuzana Sabartova, Peter Lindroth, Ann-brith Strömberg, Michael PatrikssonAbstract:To improve the truck tyre selection process at Volvo Group Trucks Technology which is currently based on convention rather than on scientific methodology, an optimization model has been developed with the aim of determining an optimal set of tyres for each vehicle and operating environment specification. The overall purpose is to reduce the cost of operation, which is in this case measured by fuel consumption and tyre wear, while preserving the levels of other tyre dependent features such as startability, handling, and ride comfort. We have developed a joint model of the vehicle, the tyres, and the operating environment. The model is based on vehicle dynamics equations describing the vehicle and is implemented in Matlab and Simulink. To be able to distinguish between different tyres, the part of the model describing tyres must be able to describe more complex properties than the commonly used Pacejka's model. Hence, a surrogate model of the function describing the Rolling Resistance Coefficient of a truck tyre and regression models of vertical and lateral stiffness have been developed and inserted into the part of the model describing tyres; the surrogate model is based on sample points evaluated through a finite element analysis. The road is then generated based on the operating environment classification of the actual truck. Since the resulting optimization model has a simulation-based objective function and simulation-based constraints, a global derivative-free optimization algorithm has to be used to solve the problem. Characteristics of available solvers for the resulting optimization problem, i.e., rbfSolve, EGO, ConstrLMSRBF, and NOMAD, are discussed.
Mateusz Kukla - One of the best experts on this subject based on the ideXlab platform.
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determination of the Rolling Resistance Coefficient of pneumatic wheel systems
AUTOBUSY – Technika Eksploatacja Systemy Transportowe, 2019Co-Authors: łukasz Wargula, Mateusz Kukla, Bartosz WieczorekAbstract:The basic Resistance during moving objects that are equipped with a circular system is Rolling Resistance. In objects powered by muscle power, such as: bicycles, wheelchairs, mobile machines, shelves and storage trolleys, the problem of Rolling Resistance limitation is more important than in the case of structures powered by engines characterized by a significant excess of driving force relative to the sum of Resistance forces. Research is being carried out on limiting the Rolling Resistance force, however, there is a lack of methods for measuring this parameter in the actual operating conditions of devices with a drive system without a drive unit. In the article for research, an innovative method was used of measuring the Rolling Resistance Coefficient of objects equipped only with the Rolling chassis of accordance with the patent application P.424484 and a test device compatible with the patent application P.424483. The study involved a pneumatic wheel commonly used in wheelchairs, the use of which gains popularity with increased interest in the construction of electric or diesel vehicles with low energy demand. Examples of such vehicles are available during the Shell Eco-marathon competition. The study was financed from the means of the National Centre for Research and Development under LIDER VII programme, research project no. LIDER/7/0025/L-7/15/NCBR/2016.
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The determination of the Rolling Resistance Coefficient of objects equipped with the wheels and suspension system – results of preliminary tests
MATEC Web of Conferences, 2019Co-Authors: Łukasz Warguła, Bartosz Wieczorek, Mateusz KuklaAbstract:Rolling Resistance Coefficient is one of the basic Resistance when moving objects. In case of objects not equipped with a motor-driven wheels and suspension system , such as: wheelchairs, mobile machinery chopper, shelving and warehouse trucks all Resistances are overcome by the muscle strength of the operator. Research is carried out to limit this phenomenon, however, there is a lack of methods for measuring this parameter in the actual operating conditions of devices with a wheels and suspension system without a drive unit. The article presents an innovative method of measuring the Rolling Resistance Coefficient of objects equipped only with the wheels and suspension system accordant with the patent application P.424484 and the developed device for these tests in accordance with patent application P.424483. Additionally, the paper presents the results of pilot tests on the measurement of pivoting Coefficient of a transport truck loaded with a given mass.
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the determination of the Rolling Resistance Coefficient of objects equipped with the wheels and suspension system results of preliminary tests
XXIII Polish-Slovak Scientific Conference on Machine Modelling and Simulations (MMS 2018) 4-7.09.2018 Rydzyna Polska, 2019Co-Authors: łukasz Wargula, Bartosz Wieczorek, Mateusz KuklaAbstract:Rolling Resistance Coefficient is one of the basic Resistance when moving objects. In case of objects not equipped with a motor-driven wheels and suspension system , such as: wheelchairs, mobile machinery chopper, shelving and warehouse trucks all Resistances are overcome by the muscle strength of the operator. Research is carried out to limit this phenomenon, however, there is a lack of methods for measuring this parameter in the actual operating conditions of devices with a wheels and suspension system without a drive unit. The article presents an innovative method of measuring the Rolling Resistance Coefficient of objects equipped only with the wheels and suspension system accordant with the patent application P.424484 and the developed device for these tests in accordance with patent application P.424483. Additionally, the paper presents the results of pilot tests on the measurement of pivoting Coefficient of a transport truck loaded with a given mass.
Michael Patriksson - One of the best experts on this subject based on the ideXlab platform.
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Integration of expert knowledge into radial basis function surrogate models
Optimization and Engineering, 2016Co-Authors: Zuzana Nedělková, Peter Lindroth, Ann-brith Strömberg, Michael PatrikssonAbstract:A current application in a collaboration between Chalmers University of Technology and Volvo Group Trucks Technology concerns the global optimization of a complex simulation-based function describing the Rolling Resistance Coefficient (RRC) of a truck tyre. This function is crucial for the optimization of truck tyres selection considered. The need to explicitly describe and optimize this function provided the main motivation for the research presented in this article. Many optimization algorithms for simulation-based optimization problems use sample points to create a computationally simple surrogate model of the objective function. Typically, not all important characteristics of the complex function (as, e.g., non-negativity)—here referred to as expert knowledge—are automatically inherited by the surrogate model. We demonstrate the integration of several types of expert knowledge into a radial basis function interpolation. The methodology is first illustrated on a simple example function and then applied to a function describing the RRC of truck tyres. Our numerical results indicate that expert knowledge can be advantageously incorporated and utilized when creating global approximations of unknown functions from sample points.
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Integration of expert knowledge into radial basis function surrogate models
Optimization and Engineering, 2015Co-Authors: Zuzana Nedelkova, Peter Lindroth, Ann-brith Strömberg, Michael PatrikssonAbstract:A current application in a collaboration between Chalmers University of Technology and Volvo Group Trucks Technology concerns the global optimization of a complex simulation-based function describing the Rolling Resistance Coefficient of a truck tyre. This function is crucial for the optimization of truck tyres selection considered. The need to explicitly describe and optimize this function provided the main motivation for the research presented in this article. Many optimization algorithms for simulation-based optimization problems use sample points to create a computationally simple surrogate model of the objective function. Typically, not all important characteristics of the complex function (as, e.g., non-negativity)—here referred to as expert knowledge—are automatically inherited by the surrogate model. We demonstrate the integration of several types of expert knowledge into a radial basis function interpolation. The methodology is first illustrated on a simple example function and then applied to a function describing the Rolling Resistance Coefficient of truck tyres. Our numerical results indicate that expert knowledge can be advantageously incorporated and utilized when creating global approximations of unknown functions from sample points.
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An optimization model for truck tyres selection
Engineering Optimization 2014, 2014Co-Authors: Zuzana Sabartova, Peter Lindroth, Ann-brith Strömberg, Michael PatrikssonAbstract:To improve the truck tyre selection process at Volvo Group Trucks Technology which is currently based on convention rather than on scientific methodology, an optimization model has been developed with the aim of determining an optimal set of tyres for each vehicle and operating environment specification. The overall purpose is to reduce the cost of operation, which is in this case measured by fuel consumption and tyre wear, while preserving the levels of other tyre dependent features such as startability, handling, and ride comfort. We have developed a joint model of the vehicle, the tyres, and the operating environment. The model is based on vehicle dynamics equations describing the vehicle and is implemented in Matlab and Simulink. To be able to distinguish between different tyres, the part of the model describing tyres must be able to describe more complex properties than the commonly used Pacejka's model. Hence, a surrogate model of the function describing the Rolling Resistance Coefficient of a truck tyre and regression models of vertical and lateral stiffness have been developed and inserted into the part of the model describing tyres; the surrogate model is based on sample points evaluated through a finite element analysis. The road is then generated based on the operating environment classification of the actual truck. Since the resulting optimization model has a simulation-based objective function and simulation-based constraints, a global derivative-free optimization algorithm has to be used to solve the problem. Characteristics of available solvers for the resulting optimization problem, i.e., rbfSolve, EGO, ConstrLMSRBF, and NOMAD, are discussed.
V. Corcoba Magana - One of the best experts on this subject based on the ideXlab platform.
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WATI: Warning of Traffic Incidents for Fuel Saving
Mobile Information Systems, 2016Co-Authors: V. Corcoba Magana, Mario Munoz-organeroAbstract:Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detects the traffic incidents based on measured telemetry data from vehicles and the different driver profiles. The proposal takes into account the Rolling Resistance Coefficient, the road slope angle, and the vehicles speeds, from vehicles which are on the scene of the traffic incident, in order to estimate the optimal deceleration profile. Adapted advice and feedback are provided to the drivers in order to appropriately and timely release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a dataset of 150 tests using 15 different drivers. The main contribution of this paper is the proposal of a system to detect traffic incidents and provide an optimal deceleration pattern for the driver to follow without requiring sensors on the road. The results show an improvement on the fuel consumption of up to 13.47%.
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Validating the Impact on Reducing Fuel Consumption by Using an EcoDriving Assistant Based on Traffic Sign Detection and Optimal Deceleration Patterns
IEEE Transactions on Intelligent Transportation Systems, 2013Co-Authors: Mario Munoz-organero, V. Corcoba MaganaAbstract:This paper implements and validates an expert system that, based on the detection or previous knowledge of certain types of traffic signals, proposes a method to reduce fuel consumption by calculating optimal deceleration patterns, minimizing the use of braking. The expert system uses a mobile device's embedded camera to monitor the environment and to recognize certain types of static traffic signals that force or can force a vehicle to stop. The system uses an adaptation of the algorithm proposed by Viola and Jones for the recognition of faces in real time, adapted to the detection of traffic signals. Detected signals are also incorporated into a central database for future use. When the vehicle approaches an upcoming traffic signal, the algorithm estimates the distance required to stop the vehicle without using the brakes, taking into account the Rolling Resistance Coefficient and the road slope angle. Appropriate advice and feedback are provided to the driver to release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a data set of 180 tests with five different models of vehicles and nine different drivers. The main contribution of this paper is the proposal of an assistant that uses information from the environment and from the vehicle to calculate optimal deceleration patterns when approaching traffic signals that force or may force the vehicle to stop. In addition, the proposed solution does not require the installation of infrastructure on the road, and it can be installed into any vehicle.
Amy E. Landis - One of the best experts on this subject based on the ideXlab platform.
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Pathway to domestic natural rubber production: a cradle-to-grave life cycle assessment of the first guayule automobile tire manufactured in the United States
The International Journal of Life Cycle Assessment, 2019Co-Authors: Pragnya L. Eranki, Amy E. LandisAbstract:Purpose Guayule ( Parthenium argentatum ) is a perennial shrub that can be cultivated in the Southwestern US. It produces natural rubber that could be a viable substitute for Hevea natural rubber and synthetic rubbers currently used in tires. Drivers for producing domestic guayule rubber include fluctuations in price and availability of imported Hevea rubber. A tire was manufactured in 2017 where guayule rubber was substituted for all of the Hevea and synthetic rubber in the conventional tire. Methods Life cycle assessment (LCA) from cradle to grave was used to evaluate the environmental and energy sustainability of the guayule tire, and these metrics were benchmarked against those of the conventional tire (CT). Functional units of 1 kg natural rubber for agricultural processes, as well as 1 tire for the cradle-to-grave study were considered. Life cycle inventory (LCI) data were collected directly from primary sources, including guayule field experiments, a rubber-processing company, and a major tire manufacturer. Scenario analysis was used to evaluate alternative processes, such as irrigation options in guayule cultivation, processing scale in rubber extraction, and selection of allocation methodology in LCAs. Model uncertainty was characterized using Monte Carlo analysis. Results and discussion The LC energy consumption of the guayule tire (GT) was 13.7 GJ/tire (including co-product credits, excluding C sequestration during agriculture), compared to 16.4 GJ/tire for the CT. The GT had 6–30% lower emissions than CT in ten different environmental impact categories. Bagasse co-product in energy applications showed benefits of reducing energy consumption by 10% and decreasing environmental impacts by up to 11%. GT’s use-phase resulted in the highest energy consumption (95%) and environmental impacts ranging between 81 and 99%. Variables in use phase, i.e., Rolling Resistance Coefficient, vehicle efficiency, and tire lifetime, and those in guayule cultivation i.e., rubber and biomass yields, were key model parameters. The effect of excluded but potentially important model parameters, i.e., guayule carbon sequestration and resin co-product were tested via sensitivity analyses. Conclusions Based on these results that factored in the lower Rolling Resistance Coefficient of a guayule tire—a significant element that improves the fuel economy of an automobile—the guayule rubber tire shows promise in its ability to replace current conventional tires. The first commercially manufactured guayule rubber passenger tire will most likely substitute components, which are either partially or fully guayule, instead of guayule replacing 100% of the existing rubbers as shown in this study.
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Pathway to domestic natural rubber production: a cradle-to-grave life cycle assessment of the first guayule automobile tire manufactured in the United States
International Journal of Life Cycle Assessment, 2018Co-Authors: Pragnya L. Eranki, Amy E. LandisAbstract:PURPOSE: Guayule (Parthenium argentatum) is a perennial shrub that can be cultivated in the Southwestern US. It produces natural rubber that could be a viable substitute for Hevea natural rubber and synthetic rubbers currently used in tires. Drivers for producing domestic guayule rubber include fluctuations in price and availability of imported Hevea rubber. A tire was manufactured in 2017 where guayule rubber was substituted for all of the Hevea and synthetic rubber in the conventional tire. METHODS: Life cycle assessment (LCA) from cradle to grave was used to evaluate the environmental and energy sustainability of the guayule tire, and these metrics were benchmarked against those of the conventional tire (CT). Functional units of 1 kg natural rubber for agricultural processes, as well as 1 tire for the cradle-to-grave study were considered. Life cycle inventory (LCI) data were collected directly from primary sources, including guayule field experiments, a rubber-processing company, and a major tire manufacturer. Scenario analysis was used to evaluate alternative processes, such as irrigation options in guayule cultivation, processing scale in rubber extraction, and selection of allocation methodology in LCAs. Model uncertainty was characterized using Monte Carlo analysis. RESULTS AND DISCUSSION: The LC energy consumption of the guayule tire (GT) was 13.7 GJ/tire (including co-product credits, excluding C sequestration during agriculture), compared to 16.4 GJ/tire for the CT. The GT had 6–30% lower emissions than CT in ten different environmental impact categories. Bagasse co-product in energy applications showed benefits of reducing energy consumption by 10% and decreasing environmental impacts by up to 11%. GT’s use-phase resulted in the highest energy consumption (95%) and environmental impacts ranging between 81 and 99%. Variables in use phase, i.e., Rolling Resistance Coefficient, vehicle efficiency, and tire lifetime, and those in guayule cultivation i.e., rubber and biomass yields, were key model parameters. The effect of excluded but potentially important model parameters, i.e., guayule carbon sequestration and resin co-product were tested via sensitivity analyses. CONCLUSIONS: Based on these results that factored in the lower Rolling Resistance Coefficient of a guayule tire—a significant element that improves the fuel economy of an automobile—the guayule rubber tire shows promise in its ability to replace current conventional tires. The first commercially manufactured guayule rubber passenger tire will most likely substitute components, which are either partially or fully guayule, instead of guayule replacing 100% of the existing rubbers as shown in this study.
