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Search: WFRF:(Larsson SC) > Other academic/artistic

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  • Barlo, Alexander, M.Sc. Eng. 1994- (author)
  • Failure Prediction of Complex Load Cases in Sheet Metal Forming : Emphasis on Non-Linear Strain Paths, Stretch-Bending and Edge Effects
  • 2023
  • Licentiate thesis (other academic/artistic)abstract
    • With the increased focus on reducing carbon emissions in today’s society, several industries have to overcome new challenges, where especially the automotive industry is under a lot of scrutiny to deliver improved and more environmentally friendly products. To meet the demands from customers and optimize vehicles aerodynamically, new cars often contain complex body geometries, together with advanced materials that are introduced to reduce the total vehicle weight. With the introduction of the complex body components and advanced materials,one area in the automotive industry that has to overcome these challenges is manufacturing engineering, and in particular the departments working with the sheet metal forming process. In this process complex body component geometries can lead to non-linear strain paths and stretch bending load cases, and newly introduced advanced materials can be prone to exhibit behaviour of edge cracks not observed in conventional sheet metals. This thesis takes it onset in the challenges seen in industry today with predicting failure of the three complex load cases: Non-Linear Strain Paths, Stretch-Bending,and Edge Cracks. Through Finite Element simulation attempts are made to accurately predict failure caused by aforementioned load cases in industrial components or experimental setups in an effort to develop post-processing methods that are applicable to all cases.
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  • Larsson, Magnus, et al. (author)
  • Aerodynamic Identification using Neural Networks
  • 1997
  • Reports (other academic/artistic)abstract
    • The use of neural networks and efficient identification algorithms in aerodynamic modeling could substantially reduce the time and work effort in going from wind tunnel and flight test data to model. The model is globally differentiable and can be inspected in any way desired. A number of structured and black box sigmoid type neural net models have been identified for mainly the C z aerodynamic coefficient in the region 0 ffi ff 60 ffi , where the aerodynamic coefficients behave highly nonlinear. The estimation data has been directly extracted from an existing aerodatabase for a generic fighter aircraft, that also has been used for validation. All available data has been used for estimation and the data is considered noiseless, so only the approximation properties of the different models are tested. Somewhat surprisingly, it is found that pure black box models with the same number of parameters as structured models utilizing physical insight, often perform better.
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  • Result 1-10 of 23

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