以废打印机壳PC/ABS再生粒子(R-PC/ABS)为基体材料,对苯二酚双(二苯基磷酸脂)(HDP)和梯形倍半硅氧烷(TSQ)为阻燃剂,采用熔融共混制备了无卤阻燃PC/ABS,对其阻燃性能、力学性能、尺寸稳定性和负荷热变形温度(HDT)进行分析,结果发现,TSQ...以废打印机壳PC/ABS再生粒子(R-PC/ABS)为基体材料,对苯二酚双(二苯基磷酸脂)(HDP)和梯形倍半硅氧烷(TSQ)为阻燃剂,采用熔融共混制备了无卤阻燃PC/ABS,对其阻燃性能、力学性能、尺寸稳定性和负荷热变形温度(HDT)进行分析,结果发现,TSQ可以阻燃R-PC/ABS,并且,对力学性能、尺寸稳定性和HDT影响较小,R-PC/ABS/0.8TSQ的LOI为29.8%,阻燃达到3.0 mm V-0和2.0 mm V-1级;HDP可以有效地阻燃R-PC/ABS,但是,对力学性能、尺寸稳定性和HDT的负面影响较大,R-PC/ABS/12HDP的LOI为36.1%,阻燃可达到UL 941.0 mm V-0级,与R-PC/ABS相比,HDT、拉伸强度、弯曲强度、弯曲模量和缺口冲击强度分别降低了20.2℃、26.6%、14.5%、16.9%和60.9%;R-PC/ABS/0.8TSQ/6HDP的LOI为35.7%,阻燃级别达到UL 941.0 mm V-0级,与R-PC/ABS/12HDP相比,模后收缩率(PMS)降低了19.7%,HDT、拉伸强度、弯曲强度、弯曲模量和缺口冲击强度分别提高了13℃、21.0%、11.3%、14.3%和85.9%。展开更多
以钛酸四丁酯水解反应原位生成的TiO_(2)包覆微胶囊红磷(microcapsulated red phosphorus,TDP)为主体阻燃剂,开展PC/ABS合金的协效阻燃研究。以PC/ABS阻燃复合材料的LOI值、UL-94等级为主要考察指标,筛选并确定较适宜的TDP基三元协效复...以钛酸四丁酯水解反应原位生成的TiO_(2)包覆微胶囊红磷(microcapsulated red phosphorus,TDP)为主体阻燃剂,开展PC/ABS合金的协效阻燃研究。以PC/ABS阻燃复合材料的LOI值、UL-94等级为主要考察指标,筛选并确定较适宜的TDP基三元协效复合阻燃剂及其质量配比为TDP∶ZnO∶DOPO=16∶4∶5。燃烧特性、阻燃性能和力学性能等测试、分析结果显示,随TDP/ZnO/DOPO添加量的增大,PC/ABS阻燃复合材料的着火时间(TTI)、热释放速率峰值(PHRR)、总热释放量(THR)、平均有效燃烧热(AEHC)、CO_(2)释放量峰值等燃烧特性数值均下降,阻燃性能(LOI值、UL-94等级)提升,但弯曲强度、拉伸强度均稍有下降。综合考虑,认为较适宜三元协效复合阻燃剂添加量为5%(质量分数),此时,PC/ABS阻燃复合材料的LOI值为28.6%、UL-94等级为V-0级;相较于PC/ABS合金,PC/ABS阻燃复合材料的TTI、PHRR、THR、AEHC、CO_(2)释放量峰值分别下降了27.27%、21.62%、22.10%、5.95%、25.97%,弯曲强度、拉伸强度分别下降了19.65%、13.26%。对三元协效复合阻燃剂的阻燃作用机制进行了初步探讨,认为TDP/ZnO/DOPO对PC/ABS合金的阻燃是DOPO的气相阻燃、TDP和ZnO的凝聚相阻燃两种作用机制协同作用的结果。展开更多
低周疲劳是发动机活塞的典型失效模式,为研究多源不确定性因素对活塞低周疲劳可靠性的影响,提高可靠性分析效率,基于Polynomial-Chaos-based Kriging(PC-Kriging)模型和蒙特卡洛模拟(Monte Carlo Simulation,MCS),构建了一种新的可靠性...低周疲劳是发动机活塞的典型失效模式,为研究多源不确定性因素对活塞低周疲劳可靠性的影响,提高可靠性分析效率,基于Polynomial-Chaos-based Kriging(PC-Kriging)模型和蒙特卡洛模拟(Monte Carlo Simulation,MCS),构建了一种新的可靠性计算方法,并通过数值算例证明了该方法的准确性和高效性。以某型柴油发动机活塞组结构为研究对象,基于热-机耦合分析建立活塞有限元模型,综合考虑关键尺寸、材料属性及载荷的不确定性,运用该方法对活塞进行了低周疲劳可靠性分析。可靠性分析结果表明,与同类型方法相比,该方法计算效率更高,仅需要有限元计算20+93次,当活塞的期望设计寿命为1.4×10^(4)时,其疲劳失效概率为1.053%;灵敏度分析结果表明,活塞高度、活塞直径、材料弹性模量和疲劳计算模型参数对可靠性的影响较大,分析结果可为活塞的可靠性设计提供指导。展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
文摘以废打印机壳PC/ABS再生粒子(R-PC/ABS)为基体材料,对苯二酚双(二苯基磷酸脂)(HDP)和梯形倍半硅氧烷(TSQ)为阻燃剂,采用熔融共混制备了无卤阻燃PC/ABS,对其阻燃性能、力学性能、尺寸稳定性和负荷热变形温度(HDT)进行分析,结果发现,TSQ可以阻燃R-PC/ABS,并且,对力学性能、尺寸稳定性和HDT影响较小,R-PC/ABS/0.8TSQ的LOI为29.8%,阻燃达到3.0 mm V-0和2.0 mm V-1级;HDP可以有效地阻燃R-PC/ABS,但是,对力学性能、尺寸稳定性和HDT的负面影响较大,R-PC/ABS/12HDP的LOI为36.1%,阻燃可达到UL 941.0 mm V-0级,与R-PC/ABS相比,HDT、拉伸强度、弯曲强度、弯曲模量和缺口冲击强度分别降低了20.2℃、26.6%、14.5%、16.9%和60.9%;R-PC/ABS/0.8TSQ/6HDP的LOI为35.7%,阻燃级别达到UL 941.0 mm V-0级,与R-PC/ABS/12HDP相比,模后收缩率(PMS)降低了19.7%,HDT、拉伸强度、弯曲强度、弯曲模量和缺口冲击强度分别提高了13℃、21.0%、11.3%、14.3%和85.9%。
文摘以钛酸四丁酯水解反应原位生成的TiO_(2)包覆微胶囊红磷(microcapsulated red phosphorus,TDP)为主体阻燃剂,开展PC/ABS合金的协效阻燃研究。以PC/ABS阻燃复合材料的LOI值、UL-94等级为主要考察指标,筛选并确定较适宜的TDP基三元协效复合阻燃剂及其质量配比为TDP∶ZnO∶DOPO=16∶4∶5。燃烧特性、阻燃性能和力学性能等测试、分析结果显示,随TDP/ZnO/DOPO添加量的增大,PC/ABS阻燃复合材料的着火时间(TTI)、热释放速率峰值(PHRR)、总热释放量(THR)、平均有效燃烧热(AEHC)、CO_(2)释放量峰值等燃烧特性数值均下降,阻燃性能(LOI值、UL-94等级)提升,但弯曲强度、拉伸强度均稍有下降。综合考虑,认为较适宜三元协效复合阻燃剂添加量为5%(质量分数),此时,PC/ABS阻燃复合材料的LOI值为28.6%、UL-94等级为V-0级;相较于PC/ABS合金,PC/ABS阻燃复合材料的TTI、PHRR、THR、AEHC、CO_(2)释放量峰值分别下降了27.27%、21.62%、22.10%、5.95%、25.97%,弯曲强度、拉伸强度分别下降了19.65%、13.26%。对三元协效复合阻燃剂的阻燃作用机制进行了初步探讨,认为TDP/ZnO/DOPO对PC/ABS合金的阻燃是DOPO的气相阻燃、TDP和ZnO的凝聚相阻燃两种作用机制协同作用的结果。
文摘低周疲劳是发动机活塞的典型失效模式,为研究多源不确定性因素对活塞低周疲劳可靠性的影响,提高可靠性分析效率,基于Polynomial-Chaos-based Kriging(PC-Kriging)模型和蒙特卡洛模拟(Monte Carlo Simulation,MCS),构建了一种新的可靠性计算方法,并通过数值算例证明了该方法的准确性和高效性。以某型柴油发动机活塞组结构为研究对象,基于热-机耦合分析建立活塞有限元模型,综合考虑关键尺寸、材料属性及载荷的不确定性,运用该方法对活塞进行了低周疲劳可靠性分析。可靠性分析结果表明,与同类型方法相比,该方法计算效率更高,仅需要有限元计算20+93次,当活塞的期望设计寿命为1.4×10^(4)时,其疲劳失效概率为1.053%;灵敏度分析结果表明,活塞高度、活塞直径、材料弹性模量和疲劳计算模型参数对可靠性的影响较大,分析结果可为活塞的可靠性设计提供指导。
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
