The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
M Labordus - One of the best experts on this subject based on the ideXlab platform.
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vacuum injection moulding for large structural applications
Composites Part A-applied Science and Manufacturing, 2003Co-Authors: W D Brouwer, E C F C Van Herpt, M LabordusAbstract:Resin Transfer Moulding (RTM) is a successful closed mould technology for small composite products in large series. For large products in smaller series (i.e. Boat Hulls or rotor blades) the Vacuum Assisted RTM is applied in order to save cost of moulds. Compared with RTM the VARTM technology is still not practised in many products today. The risk of failures in very large products is often considered too high. This paper describes ways to improve reliability and predictability of the VARTM technology in order to decrease development cost and to make the risk of failures during production as small as possible. First, appropriate methods to achieve void free processing of the composite material are described. Secondly, it is explained how the injection strategy can be optimised by using resin flow simulation software. Two examples of successful developments of large structural applications are described; a 20 m rotor blade and a 16 m long Boat Hull for a sailing yacht.
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Vacuum injection moulding for large structural applications
Composites Part A: Applied Science and Manufacturing, 2003Co-Authors: W D Brouwer, E.c.f.c Van Herpt, M LabordusAbstract:Abstract Resin Transfer Moulding (RTM) is a successful closed mould technology for small composite products in large series. For large products in smaller series (i.e. Boat Hulls or rotor blades) the Vacuum Assisted RTM is applied in order to save cost of moulds. Compared with RTM the VARTM technology is still not practised in many products today. The risk of failures in very large products is often considered too high. This paper describes ways to improve reliability and predictability of the VARTM technology in order to decrease development cost and to make the risk of failures during production as small as possible. First, appropriate methods to achieve void free processing of the composite material are described. Secondly, it is explained how the injection strategy can be optimised by using resin flow simulation software. Two examples of successful developments of large structural applications are described; a 20 m rotor blade and a 16 m long Boat Hull for a sailing yacht. Download : Download full-size image
W D Brouwer - One of the best experts on this subject based on the ideXlab platform.
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vacuum injection moulding for large structural applications
Composites Part A-applied Science and Manufacturing, 2003Co-Authors: W D Brouwer, E C F C Van Herpt, M LabordusAbstract:Resin Transfer Moulding (RTM) is a successful closed mould technology for small composite products in large series. For large products in smaller series (i.e. Boat Hulls or rotor blades) the Vacuum Assisted RTM is applied in order to save cost of moulds. Compared with RTM the VARTM technology is still not practised in many products today. The risk of failures in very large products is often considered too high. This paper describes ways to improve reliability and predictability of the VARTM technology in order to decrease development cost and to make the risk of failures during production as small as possible. First, appropriate methods to achieve void free processing of the composite material are described. Secondly, it is explained how the injection strategy can be optimised by using resin flow simulation software. Two examples of successful developments of large structural applications are described; a 20 m rotor blade and a 16 m long Boat Hull for a sailing yacht.
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Vacuum injection moulding for large structural applications
Composites Part A: Applied Science and Manufacturing, 2003Co-Authors: W D Brouwer, E.c.f.c Van Herpt, M LabordusAbstract:Abstract Resin Transfer Moulding (RTM) is a successful closed mould technology for small composite products in large series. For large products in smaller series (i.e. Boat Hulls or rotor blades) the Vacuum Assisted RTM is applied in order to save cost of moulds. Compared with RTM the VARTM technology is still not practised in many products today. The risk of failures in very large products is often considered too high. This paper describes ways to improve reliability and predictability of the VARTM technology in order to decrease development cost and to make the risk of failures during production as small as possible. First, appropriate methods to achieve void free processing of the composite material are described. Secondly, it is explained how the injection strategy can be optimised by using resin flow simulation software. Two examples of successful developments of large structural applications are described; a 20 m rotor blade and a 16 m long Boat Hull for a sailing yacht. Download : Download full-size image
Henk Corporaal - One of the best experts on this subject based on the ideXlab platform.
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the Boat Hull model enabling performance prediction for parallel computing prior to code development
Computing Frontiers, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years and are expected to continue for the next decade. With these trends of parallel computing, it becomes increasingly difficult to decide on which processor to run a given application, mainly because the programming of these processors has become increasingly challenging. In this work, we present a model to predict the performance of a given application on a multi-core or many-core processor. Since programming these processors can be challenging and time consuming, our model does not require source code to be available for the target processor. This is in contrast to existing performance prediction techniques such as mathematical models and simulators, which require code to be available and optimized for the target architecture. To enable performance prediction prior to algorithm implementation, we classify algorithms using an existing algorithm classification. For each class, we create a specific instance of the roofline model, resulting in a new class-specific model. This new model, named the Boat Hull model, enables performance prediction and processor selection prior to the development of architecture specific code. We demonstrate the Boat Hull model using GPUs and CPUs as target architectures. We show that performance is accurately predicted for an example real-life application.
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the Boat Hull model adapting the roofline model to enable performance prediction for parallel computing
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given application. In this work, we use an algorithm classification to predict performance prior to algorithm implementation. For this purpose, we modify the roofline model to include class information. In this way, we enable architectural choice through performance prediction prior to the development of architecture specific code. The new model, the Boat Hull model, is demonstrated using a GPU as a target architecture. We show for 6 example algorithms that performance is predicted accurately without requiring code to be available.
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Conf. Computing Frontiers - The Boat Hull model: enabling performance prediction for parallel computing prior to code development
Proceedings of the 9th conference on Computing Frontiers - CF '12, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years and are expected to continue for the next decade. With these trends of parallel computing, it becomes increasingly difficult to decide on which processor to run a given application, mainly because the programming of these processors has become increasingly challenging. In this work, we present a model to predict the performance of a given application on a multi-core or many-core processor. Since programming these processors can be challenging and time consuming, our model does not require source code to be available for the target processor. This is in contrast to existing performance prediction techniques such as mathematical models and simulators, which require code to be available and optimized for the target architecture. To enable performance prediction prior to algorithm implementation, we classify algorithms using an existing algorithm classification. For each class, we create a specific instance of the roofline model, resulting in a new class-specific model. This new model, named the Boat Hull model, enables performance prediction and processor selection prior to the development of architecture specific code. We demonstrate the Boat Hull model using GPUs and CPUs as target architectures. We show that performance is accurately predicted for an example real-life application.
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PPOPP - The Boat Hull model: adapting the roofline model to enable performance prediction for parallel computing
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming - PPoPP '12, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given application. In this work, we use an algorithm classification to predict performance prior to algorithm implementation. For this purpose, we modify the roofline model to include class information. In this way, we enable architectural choice through performance prediction prior to the development of architecture specific code. The new model, the Boat Hull model, is demonstrated using a GPU as a target architecture. We show for 6 example algorithms that performance is predicted accurately without requiring code to be available.
E C F C Van Herpt - One of the best experts on this subject based on the ideXlab platform.
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vacuum injection moulding for large structural applications
Composites Part A-applied Science and Manufacturing, 2003Co-Authors: W D Brouwer, E C F C Van Herpt, M LabordusAbstract:Resin Transfer Moulding (RTM) is a successful closed mould technology for small composite products in large series. For large products in smaller series (i.e. Boat Hulls or rotor blades) the Vacuum Assisted RTM is applied in order to save cost of moulds. Compared with RTM the VARTM technology is still not practised in many products today. The risk of failures in very large products is often considered too high. This paper describes ways to improve reliability and predictability of the VARTM technology in order to decrease development cost and to make the risk of failures during production as small as possible. First, appropriate methods to achieve void free processing of the composite material are described. Secondly, it is explained how the injection strategy can be optimised by using resin flow simulation software. Two examples of successful developments of large structural applications are described; a 20 m rotor blade and a 16 m long Boat Hull for a sailing yacht.
Cedric Nugteren - One of the best experts on this subject based on the ideXlab platform.
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the Boat Hull model enabling performance prediction for parallel computing prior to code development
Computing Frontiers, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years and are expected to continue for the next decade. With these trends of parallel computing, it becomes increasingly difficult to decide on which processor to run a given application, mainly because the programming of these processors has become increasingly challenging. In this work, we present a model to predict the performance of a given application on a multi-core or many-core processor. Since programming these processors can be challenging and time consuming, our model does not require source code to be available for the target processor. This is in contrast to existing performance prediction techniques such as mathematical models and simulators, which require code to be available and optimized for the target architecture. To enable performance prediction prior to algorithm implementation, we classify algorithms using an existing algorithm classification. For each class, we create a specific instance of the roofline model, resulting in a new class-specific model. This new model, named the Boat Hull model, enables performance prediction and processor selection prior to the development of architecture specific code. We demonstrate the Boat Hull model using GPUs and CPUs as target architectures. We show that performance is accurately predicted for an example real-life application.
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the Boat Hull model adapting the roofline model to enable performance prediction for parallel computing
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given application. In this work, we use an algorithm classification to predict performance prior to algorithm implementation. For this purpose, we modify the roofline model to include class information. In this way, we enable architectural choice through performance prediction prior to the development of architecture specific code. The new model, the Boat Hull model, is demonstrated using a GPU as a target architecture. We show for 6 example algorithms that performance is predicted accurately without requiring code to be available.
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Conf. Computing Frontiers - The Boat Hull model: enabling performance prediction for parallel computing prior to code development
Proceedings of the 9th conference on Computing Frontiers - CF '12, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years and are expected to continue for the next decade. With these trends of parallel computing, it becomes increasingly difficult to decide on which processor to run a given application, mainly because the programming of these processors has become increasingly challenging. In this work, we present a model to predict the performance of a given application on a multi-core or many-core processor. Since programming these processors can be challenging and time consuming, our model does not require source code to be available for the target processor. This is in contrast to existing performance prediction techniques such as mathematical models and simulators, which require code to be available and optimized for the target architecture. To enable performance prediction prior to algorithm implementation, we classify algorithms using an existing algorithm classification. For each class, we create a specific instance of the roofline model, resulting in a new class-specific model. This new model, named the Boat Hull model, enables performance prediction and processor selection prior to the development of architecture specific code. We demonstrate the Boat Hull model using GPUs and CPUs as target architectures. We show that performance is accurately predicted for an example real-life application.
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PPOPP - The Boat Hull model: adapting the roofline model to enable performance prediction for parallel computing
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming - PPoPP '12, 2012Co-Authors: Cedric Nugteren, Henk CorporaalAbstract:Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given application. In this work, we use an algorithm classification to predict performance prior to algorithm implementation. For this purpose, we modify the roofline model to include class information. In this way, we enable architectural choice through performance prediction prior to the development of architecture specific code. The new model, the Boat Hull model, is demonstrated using a GPU as a target architecture. We show for 6 example algorithms that performance is predicted accurately without requiring code to be available.