Lightweight design requires an accurate life prediction for structures and components under service loading histories. However, predicted life with the existing methods seems too conservative in some cases, leading to...Lightweight design requires an accurate life prediction for structures and components under service loading histories. However, predicted life with the existing methods seems too conservative in some cases, leading to a heavy structure. Because these methods are established on the basis that load cycles would only cause fatigue damage, ignore the strengthening effect of loads. Based on Palmgren-Miner Rule （PMR）, this paper introduces a new method for fatigue life prediction under service loadings by taking into account the strengthening effect of loads below the fatigue limit. In this method, the service loadings are classified into three categories： damaging load, strengthening load and none-effect load, and the process for fatigue life prediction is divided into two stages： stage I and stage II, according to the best strengthening number of cycles. During stage I, fatigue damage is calculated considering both the strengthening and damaging effect of load cycles. While during stage II, only the damaging effect is considered. To validate this method, fatigue lives of automobile half shaft and torsion beam rear axle are calculated based on the new method and traditional methods, such as PMR and Modified Miner Rule （MMR）, and fatigue tests of the two components are conducted under service loading histories. The tests results show that the percentage errors of the predicted life with the new method to mean life of tests for the two components are –3.78% and –1.76% separately, much lesser than that with PMR and MMR. By considering the strengthening effect of loads below the fatigue limit, the new method can significantly improve the accuracy for fatigue life prediction. Thus lightweight design can be fully realized in the design stage.展开更多
This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In t...This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In this paper, first we study the multilevel matching arithmetic of geographical information services. This arithmetic is used for filtering and matching the services in the service register center according to the similarity between services selected and services requested from the definition of the function similarity and credit standing similarity. The matching arithmetic, geographical information service ontology and semantic description constitute the discovering model. Finally, we test and analyze the model from the recall ratio, precision ratio, responsivity and load balance. The result indicates that the ontology-driven discovering model is excellent in recall ratio and precision ratio, and can maintain the dynamic load balance of service copy.展开更多
基金Supported by National High Technology Research and Development Program of China (Grant No.2011AA11A265)National Natural Science Foundation of China (Grant Nos.50875173,51105241)Shanghai Municipal Natural Science Foundation of China (Grant No.11ZR1414700)
文摘Lightweight design requires an accurate life prediction for structures and components under service loading histories. However, predicted life with the existing methods seems too conservative in some cases, leading to a heavy structure. Because these methods are established on the basis that load cycles would only cause fatigue damage, ignore the strengthening effect of loads. Based on Palmgren-Miner Rule （PMR）, this paper introduces a new method for fatigue life prediction under service loadings by taking into account the strengthening effect of loads below the fatigue limit. In this method, the service loadings are classified into three categories： damaging load, strengthening load and none-effect load, and the process for fatigue life prediction is divided into two stages： stage I and stage II, according to the best strengthening number of cycles. During stage I, fatigue damage is calculated considering both the strengthening and damaging effect of load cycles. While during stage II, only the damaging effect is considered. To validate this method, fatigue lives of automobile half shaft and torsion beam rear axle are calculated based on the new method and traditional methods, such as PMR and Modified Miner Rule （MMR）, and fatigue tests of the two components are conducted under service loading histories. The tests results show that the percentage errors of the predicted life with the new method to mean life of tests for the two components are –3.78% and –1.76% separately, much lesser than that with PMR and MMR. By considering the strengthening effect of loads below the fatigue limit, the new method can significantly improve the accuracy for fatigue life prediction. Thus lightweight design can be fully realized in the design stage.
基金Supported by the Degree Dissertation of Doctor Natural Science Innovation Foundation of Information Engineering University(2007)
文摘This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In this paper, first we study the multilevel matching arithmetic of geographical information services. This arithmetic is used for filtering and matching the services in the service register center according to the similarity between services selected and services requested from the definition of the function similarity and credit standing similarity. The matching arithmetic, geographical information service ontology and semantic description constitute the discovering model. Finally, we test and analyze the model from the recall ratio, precision ratio, responsivity and load balance. The result indicates that the ontology-driven discovering model is excellent in recall ratio and precision ratio, and can maintain the dynamic load balance of service copy.
