广州地铁盾构隧道建设期碳排放强度与减排潜力研究

为合理量化地铁盾构隧道建设的碳排放水平并测度其减排潜力,采用LCA方法开展地铁盾构隧道建设阶段碳排放评价工作,并结合实际工程数据进行碳排放强度和水平量化分析;同时,基于情景分析法从推广绿色建材及清洁能源等角度探究其减碳潜力。研究结果表明： 单环预制管片碳排放强度约为7.1 tCO2e/环,而单位地铁盾构隧道建设碳排放强度约为1.1万tCO2e/km; 其中,建材及预制管片生产、运输及盾构施工和安装等过程占比分别为72.7%、1.9%和25.4%。若通过提高再生建材和优化能源结构,年均碳排放量下降约6%,但碳排放量累积可达约13 MtCO2e（2022—2035年）,与2020年全国城市轨道交通运营排放基本持平。     

飞机环控系统蝶形阀气动噪声特性及影响因素

为研究飞机环控系统管路蝶形阀气动噪声特性及影响因素,采用CFD软件与专业声学分析软件进行联合仿真,对气体流经阀门时的流场进行瞬态分析,将所得流场数据导入专业声学软件LMS Virtual.Lab,生成气动噪声声源,并建立气动噪声模型。分析结果表明:气体流过蝶形阀后产生较强烈的涡流扰动,导致管路和阀门壁面产生脉动压力,从而辐射气动噪声;蝶形阀气动噪声声压级频谱较宽,无明显主频率;阀门开度和气体流速会显著影响气动噪声声压级,在系统设计中应避免阀门长时间在低开度下工作。     

未知参数的舰船辐射噪声线谱Duffing振子检测方法

当被动声呐探测舰船目标时,舰船辐射噪声中的线谱成分是舰船分类识别及航行状态监测的特征参量,通常情况下线谱的频率及数量是未知的。针对现有未知频率的Duffing振子检测方法存在系统结构复杂,判别时间长,需要人为参与等问题,本文构造了变参数单一Duffing振子检测模型,并提出了变频频率切片小波变换的Duffing振子检测方法。该方法通过设置合理的频率灵敏度参数自动地调整系统内置策动力频率值,利用频率切片小波变换提升系统抗噪性能,结合Poincare映射特征函数的系统相态定量判决方法,变频搜索待测信号中的线谱分量。在实测数据的分析处理中,对不同观测时间内的数据进行检测,实现了线谱成分的检测跟踪与频率估计,并根据时间轨迹上的频率识别信息确定存在的稳定线谱成分。数据处理结果表明,该方法能够实现低信噪比下参数未知线谱的检测与频率估计。     

关于地下物流系统运营安全的若干问题的探讨

用于集装箱运输的地下物流系统对解决港口城市交通、环境等方面的港城矛盾具有重要意义。作为大型地下工程,该系统在建设和运营中存在诸多不确定性。该系统有别于地铁、公路隧道等地下基础设施,具有自动化、无人操作的特点,因此在防灾减灾的考虑上存在一定的特殊性。因此,针对该系统运营期面临的火灾、水灾和车辆故障等风险因素进行探讨,并提出了相应对策,对未来该系统的安全运营具有一定的借鉴意义。     

无砟轨道聚合微粒吸声板研究

分析聚合微粒吸声材料特性,采用阻抗管试验研究吸声板厚度、材料颗粒大小、空腔设置对吸声系数的影响规律,确定聚合微粒材料的关键参数,首次采用聚合微粒材料研发一种无砟轨道降噪吸声板并确定其几何尺寸、表面设计及强度设计。提出采用拱形空腔提高吸声板的承载力且拓展其吸声频段;通过配置纤维钢筋增强吸声板的安全冗余;通过材料与结构的综合设计使吸声板兼具微孔吸声、共振吸声和干涉消声功能,增强了吸声效果。混响室试验表明,吸声板降噪系数为1.0;实车试验结果表明,测试速度为60~170 km/h时距轨道中心线25 m处吸声板降噪4.0~4.4 dB(A),降噪效果显著。     

高速动车组客室内噪声声源特性及其随车辆运行里程的演变规律

介绍了实测的高速动车组在不同运行速度、不同线路形式、不同运行方向、不同升弓车厢条件下客室内的噪声和声源特性,分析了不同运行里程阶段的车辆以相同速度运行时客室内噪声的演变规律。研究结果可为高速动车组减振降噪工作的开展提供科学依据。     

基于模态贡献量分析方法的地铁车辆结构降噪优化研究

地铁列车的运行过程中伴随着不同程度的车体板件振动,由此而引起的车体板件辐射噪声是地铁列车车内噪声的重要来源之一。应用模态贡献量分析方法,研究了车体板件的振动对车内场点声压级的影响特性,并通过修改局部板件等效厚度的方式改善车内声场。将地板等效厚度减少2 mm后,场点43 Hz、82 Hz频率处的线性声压级均降低了6 dB以上。通过模态贡献量分析找出对车内噪声贡献较大的模态,并结合其模态振型以及板件节点贡献量分析进行针对性结构优化,这种方法可以起到改善车内场点处声学响应的效果。     

Effects of a hovering unmanned aerial vehicle on urban soundscapes perception

Several industry leaders and governmental agencies are currently investigating the use of Unmanned Aerial Vehicles (UAVs), or ‘drones’ as commonly known, for an ever-growing number of applications from blue light services to parcel delivery. For the specific case of the delivery sector, drones can alleviate road space usage and also lead to reductions in CO₂ and air pollution emissions, compared to traditional diesel-powered vehicles. However, due to their unconventional acoustic characteristics and operational manoeuvres, it is uncertain how communities will respond to drone operations. Noise has been suggested as a major barrier to public acceptance of drone operations in urban areas. In this paper, a series of audio-visual scenarios were created to investigate the effects of drone noise on the reported loudness, annoyance and pleasantness of seven different types of urban soundscapes. In soundscapes highly impacted by road traffic noise, the presence of drone noise lead to small changes in the perceived loudness, annoyance and pleasantness. In soundscapes with reduced road traffic noise, the participants reported a significantly higher perceived loudness and annoyance and a lower pleasantness with the presence of the same drone noise. For instance, the reported annoyance increased from 2.3 ± 0.8 (without drone noise) to 6.8 ± 0.3 (with drone noise), in an 11-point scale (0-not at all, 10-extremely). Based on these results, the concentration of drone operations along flight paths through busy roads might aid in the mitigation of the overall community noise impact caused by drones.     

Modeling traffic noise in a mountainous city using artificial neural networks and gradient correction

Different regions have established traffic noise prediction models to adapt to their particular environmental characteristics. This paper aimed to develop a traffic noise prediction model for mountainous cities. In China, the traffic noise prediction model HJ 2.4-2009, which itself is based on the sound pressure level corrected for roadway gradients (RGs), has been receiving widespread acceptance. On the basis of the model in HJ 2.4-2009, the RG correction coefficient was proposed to modify the original model and a per-vehicle noise prediction model was built using a multilayer feedforward artificial neural network (ANN) model. The data collected from a municipal road of a hilly city, Chongqing, was used to train and validate the ANN model. The predictor variables comprised the per-vehicle noise value, vehicle type, vehicle velocity, and roadway gradient. The results showed that the modified HJ 2.4-2009 model incorporating the gradient correction coefficient achieved a significantly higher R² for mountainous cities than the original model. Besides, the ANN-based noise prediction model achieved considerable accuracy improvement over the empirical predictive equations.     

Variability in sound power levels: Implications for static and dynamic traffic models

Classically, one mean vehicle representative of each category is used by both static and dynamic traffic noise prediction models. The spectrum associated with this mean vehicle is determined from a linear statistical regression analysis based on measurement campaigns on a track or in situ. However, the variability of individual vehicle emissions can influence predictions and hinder comparison between static and dynamic models. In order to estimate the induced bias, statistical analysis of the distributions of sound power levels emitted by the individual passage of vehicles during 82 measurement campaigns was carried out. The results show that 92% of the residual regression distributions are Gaussian and that standard deviations can reach 3.6 dBA. The value of the proposed correction term for this case study could reach 1.4 dBA for light vehicles and 1.2 dBA for heavy vehicles. This analysis also shows that the variability in sound power levels and thus the corresponding corrections are higher at the lowest speeds that correspond to urban driving conditions.