New Optimization Method, the Algorithms of Changes, for Heat Exchanger Design

Heat exchangers are widely used in the process engineering such as the chemical industries, the petroleum industries, and the HVAC applications etc. An optimally designed heat exchanger cannot only help the optimization of the equipment size but also the reduction of the power consumption. In this paper, a new optimization approach called algorithms of changes (AOC) is proposed for design and optimization of the shell-tube heat exchanger. This new optimization technique is developed based on the concept of the book of changes (I Ching) which is one of the oldest Chinese classic texts. In AOC, the hexagram operations in I Ching are generalized to binary string case and an iterative process, which imitates the I Ching inference, is defined. Before applying the AOC to the heat exchanger design problem, the new optimization method is examined by the benchmark optimization problems such as the global optimization test functions and the travelling salesman problem (TSP). Based on the TSP results, the AOC is shown to be superior to the genetic algorithms (GA). The AOC is then used in the optimal design of heat exchanger. The shell inside diameter, tube outside diameter, and baffles spacing are treated as the design (or optimized) variables. The cost of the heat exchanger is arranged as the objective function. For the heat exchanger design problem, the results show that the AOC is comparable to the GA method. Both methods can find the optimal solution in a short period of time.

Assessing the collapse risk of Stipa bungeana grassland in China based on its distribution changes

The criteria used by International Union for Conservation of Nature(IUCN) for its Red List of Ecosystems(RLE) are the global standards for ecosystem-level risk assessment, and they have been increasingly used for biodiversity conservation. The changed distribution area of an ecosystem is one of the key criteria in such assessments. The Stipa bungeana grassland is one of the most widely distributed grasslands in the warm-temperate semi-arid regions of China. However, the total distribution area of this grassland was noted to have shrunk and become fragmented because of its conversion to cropland and grazing-induced degradation. Following the IUCN-RLE standards, here we analyzed changes in the geographical distribution of this degraded grassland, to evaluate its degradation and risk of collapse. Past(1950-1980) distribution areas were extracted from the Vegetation Map of China(1:1,000,000). Present realizable distribution areas were equated to these past areas minus any habitat area losses. We then predicted the grassland’s present and future(under the Representative Concentration Pathway 8.5 scenario) potential distribution areas using maximum entropy algorithm(MaxEnt), based on field survey data and nine environmental layers. Our results showed that the S. bungeana grassland was mainly distributed in the Loess Plateau, Hexi Corridor, and low altitudes of the Qilian Mountains and Longshou Mountain. This ecosystem occurred mainly on loess soils, kastanozems, steppe aeolian soils and sierozems. Thermal and edaphic factors were the most important factors limiting the distribution of S. bungeana grassland across China. Since 56.1% of its past distribution area(4.9×10~4 km^2) disappeared in the last 50 a, the present realizable distribution area only amounts to 2.2×10~4 km^2. But only 15.7% of its present potential distribution area(14.0×10~4 km^2) is actually occupied by the S. bungeana grassland. The future potential distribution of S. bungeana grassland was predicted to shift towards northwest, and the total area of this ecosystem will shrink by 12.4% over the next 50 a under the most pessimistic climate change scenario. Accordingly, following the IUCN-RLE criteria, we deemed the S. bungeana grassland ecosystem in China to be endangered(EN). Revegetation projects and the establishment of protected areas are recommended as effective ways to avert this looming crisis. This empirical modeling study provides an example of how IUCN-RLE categories and criteria may be valuably used for ecosystem assessments in China and abroad.

A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region

A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.

Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

Assessing impact of climate change on forest cover type shifts in Western Himalayan Eco-region

Climate is a critical factor affecting forest ecosystems and their capacity to produce goods and services. Effects of climate change on forests depend on ecosystem-specific factors including dimensions of climate （temperature, precipitation, drought, wind etc.）. Available infor- mation is not sufficient to support a quantitative assessment of the eco- logical, social and economic consequences. The present study assessed shifts in forest cover types of Western Himalayan Eco-region （700-4 500 m）. 100 randomly selected samples （75 for training and 25 for testing the model）, genetic algorithm of rule set parameters and climatic envelopes were used to assess the distribution of five prominent forest cover types （Temperate evergreen, Tropical semi-evergreen, Temperate conifer, Sub- tropical conifer, and Tropical moist deciduous forests）. Modelling was conducted for four different scenarios, current scenario, changed precipi- tation （8% increase）, changed temperature （1.07℃ increase）, and both changed temperature and precipitation. On increasing precipitation a downward shift in the temperate evergreen and tropical semi-evergreen was observed, while sub-tropical conifer and tropical moist-deciduous forests showed a slight upward shift and temperate conifer showed ＇no shift. On increasing temperatm＇e, an upward shift in all forest types was observed except sub-tropical conifer forests without significant changes. When both temperature and precipitation were changed, the actual dis- tribution was maintained and slight upward shift was observed in all the forest types except sub-tropical conifer. It is important to understand the likely impacts of the projected climate change on the forest ecosystems, so that better management and conservation strategies can be adopted for the biodiversity and forest dependent community. Knowledge of impact mechanisms also enables identification and mitigation of some of the conditions that increase vulnerability to climate change in the forest sector.

Characterisation of Landscape with ForestFragmentation Dynamics

Land cover (LC) and land use (LU) dynamics induced by human and natural processes play a major role in global as well as regional patterns of landscapes influencing biodiversity, hydrology, ecology and climate. Changes in LC features resulting in forest fragmentations have posed direct threats to biodiversity, endangering the sustainability of ecological goods and services. Habitat fragmentation is of added concern as the residual spatial patterns mitigate or exacerbate edge effects. LU dynamics are obtained by classifying temporal remotely sensed satellite imagery of different spatial and spectral resolutions. This paper reviews five different image classification algorithms using spatio-temporal data of a temperate watershed in Himachal Pradesh, India. Gaussian Maximum Likelihood classifier was found to be apt for analysing spatial pattern at regional scale based on accuracy assessment through error matrix and ROC (receiver operating characteristic) curves. The LU information thus derived was then used to assess spatial changes from temporal data using principal component analysis and correspondence analysis based image differencing. The forest area dynamics was further studied by analysing the different types of fragmentation through forest fragmentation models. The computed forest fragmentation and landscape metrics show a decline of interior intact forests with a substantial increase in patch forest during 1972-2007.

Main environmental variables influencing the abundance of plant species under risk category

Determining climatic and physiographic variables in Mexico’s major ecoregions that are limiting to biodiversity and species of high conservation concern is essential for their conservation.Yet,at the national level to date,few studies have been performed with large data sets and crossconfirmation using multiple statistical analyses.Here,we used 25 endemic,rare and endangered species from 3610 sampling points throughout Mexico and 25 environmental attributes,including average precipitation for different seasons of the year,annual dryness index,slope of the terrain;and maximum,minimum and average temperatures to test our hypothesis that these species could be assessed with the same weight among all variables,showing similar indices of importance.Our results using principal component analysis,covariation analysis by permutations,and random forest regression showed that summer precipitation,length of the frost-free period,spring precipitation,winter precipitation and growing season precipitation all strongly influence the abundance of tropical species.In contrast,annual precipitation and the balance at different seasons(summer and growing season)were the most relevant variables on the temperate region species.For dry areas,the minimum temperature of the coldest month and the maximum temperature of the warmest month were the most significant variables.Using these different associations in different climatic regions could support a more precise management and conservation plan for the preservation of plant species diversity in forests under different global warming scenarios.

An Efficient Approach for Historical Storage and Retrieval of Segmented Road Data in Geographic Information System for Transportation

One of the most powerful functions of Geographic Information System for Transportation (GIS-T) is Dynamic Segmentation (DS), which is used to increase the efficiency and precision of road management by generating segments based on attributes. The road segments describing transportation data are both spatially and temporally referenced. For a variety of transportation applications, historical road segments must be preserved. This study presents an appropriate approach to preserve and retrieve the historical road segments efficiently. In the proposed method, only the portions of segments of a time stamp that have been changed into new segments rather than storing the entire segments for every old time stamp are recorded .The storage of these portions is based on the type of changes. A recursive algorithm is developed to retrieve all segments for every old time stamp. Experimental results using real data of Tehran City, Iran justify the strength of the proposed approach in many aspects. An important achievement of the results is that database volume for 2006, 2007 and 2008 within the Historical Line Event Table (HLET) is reduced by 70%, 80% and 78%, respectively. The proposed method has the potential to prevent from vast data redundancy and the unnecessary storage of entire segments for each time stamp. Since the present technique is performed on ordinary plain tables that are readable by all GIS software, special software platforms to manage the storage and retrieval of historical segments are not needed. In addition, this method simplifies spatio-temporal queries.

Improved Ladder Wave Modulation of Circulating Current Suppressing Control Strategy of MMC

This paper partitions the arm current of MMC into uncontrollable current and controllable current. The former is determined by the load that can’t be controlled by taking any control strategy. The later caused by the unbalanced total inserted voltage of three arms can be controlled by some improved algorithms. The conclusion based on the researching the essence of circulating current is reached that change the number of the inserted sub-modules in each phase can suppress the circulating current. Combined with the improved ladder wave modulation, a novel circulating current suppression strategy particularly for the inverter station is developed. The improved strategy can adapt to load changes and reduce the circulating current and output voltage THD of MMC ac terminals greatly without increasing any peripheral circuits. Finally, the simulation model of 100 submodules in each phase is constructed in MATLAB and the simulation results verify the correctness and effectiveness of the modified control algorithm.

基于多特征和LOF的用户负荷突变检测

负荷突变对电网冲击力大,会造成电网频率和功率振荡。为了对复杂且大体量的用户负荷异常数据进行排查,提出了多特征与LOF(局部离群因子)算法相结合的检测方法。提取负荷数据统计特征平均值、标准差以及波形特征离散系数、峭度、波形因子和脉冲因子,经过PCA(主成分分析)降维后获得有效特征,并采用LOF算法对每天的用户负荷异常数据进行检测。此检测算法在以阿里云为基础的浙电数据中台中运行,结果表明所提方法能够每天定时在海量量测数据中将负荷突变的用户查找出来,实现了在线检测并具有较高的准确率。

电动汽车集中型充电站选址定容模型

针对充电和换电等单一模式的盲目建设,规划不集中带来的资源利用率低与部分充电需求得不到满足等问题,本文提出一种考虑充电和换电与移动充电的多种充电模式下集中型充电站选址与定容方法。首先,分析不同类型电动汽车的充电行为特性,模拟得到规划区域的充电需求分布;然后,基于最近距离原则采用排队论方法计算电动汽车充电损耗成本,优化移动充电设备自充电时刻,求得电动汽车自充电成本;最后,以集中型充电站建设维护成本、用户损失成本及设备自充电成本总和最小为目标建立选址定容模型,结合某城市部分实际道路网为研究区域,采用遗传算法求解模型,确定规划区域内集中型充电站建设数量、位置及不同设备的配置数量。结果表明:规划区域内建设8座集中型充电站总成本达到最低,充电需求车辆数量与充电功率变化对集中型充电站成本均有较大影响,且优化移动充电设备自充电调度管理,可降低集中型充电站高峰时期32.62%的电网负荷,提高了电网稳定性。

基于航测数据的不同风格换道轨迹规划

不当的换道行为可能危及交通安全,导致交通事故和拥堵,因此有必要探索不同驾驶风格下在车道出口的换道轨迹.本文利用中国高速公路和快速路拥堵场景数据集中的车辆轨迹数据,采用K-means算法将驾驶人分为谨慎型、普通型和激进型三类;通过聚类分析和换道时间预测,以最小化换道纵向位移和行驶稳定性加权值之和为优化目标,同时以舒适性和安全性评价指标为约束条件,采用五次多项式进行最优换道轨迹规划;随后,使用遗传算法解决轨迹规划问题,基于Prescan、CarSim、MATLAB/Simulink仿真平台建立横纵向联合控制二自由度车辆动力学模型;最后,设计自车前车、目标车道前车和目标车道后车三种典型换道场景,并通过仿真实验评价不同驾驶风格下的换道轨迹规划效果和车辆轨迹跟踪控制效果.实验结果表明:在目标车道有车的场景下提出的融合驾驶风格的轨迹规划算法使得规划的换道轨迹增加了激进型驾驶风格的换道时长,同时减少了普通型和谨慎型驾驶风格司机的换道时长,进而能够确保换道过程的时效性、安全性和舒适性.

黄河流域地下水储量时空演变特征及影响因素分析

基于GRACE和GRACE Follow-On卫星及GLADS数据,考虑气候、植被等影响,构建了时间序列分解和随机森林算法耦合模型,揭示了2003—2022年黄河流域地下水储量时空变化特征及主要驱动过程。结果表明:近20 a黄河流域地下水储量显著减小,减小变率为-3.3 mm/a,其中汾河盆地和关中平原地区减小速度最快,变率为-15~-30 mm/a;气候呈暖干化趋势,植被绿度提高,蒸散发量显著增加;地下水储量的减小是植被覆盖增加、气温升高和蒸散发量增加共同作用导致地下水消耗量增加的结果;地下水储量空间变化主导因素差异显著,不同区域主导过程均由植被状况和气温的相互作用驱动。

基于V2V的智能车辆换道轨迹规划方法研究

针对传统车辆换道轨迹算法实时性差与换道效率低的问题,提出一种基于V2V的智能车辆动态自主换道轨迹规划算法。换道起始时刻,智能车辆通过V2V通信技术获取周围交通车辆运动状态信息,在换道条件允许的情况下,采用多项式函数对车辆换道过程的横纵向运动进行描述,通过构建基于双圆车体近似模型的避障算法筛选无碰撞的换道轨迹,以车辆换道舒适性、换道效率等换道性能指标构建多目标优化价值函数,通过求解约束化问题求得最优换道轨迹。为验证算法的有效性,利用CarSim和Matlab/Simulink等软件进行联合仿真验证。结果表明:对于不同的换道工况,基于V2V的换道轨迹规划算法比传统非V2V的换道轨迹规划算法所规划出的换道轨迹需要的换道空间更小,具有更高的换道效率。

基于斑马算法优化支持向量回归机模型预测页岩地层压力

针对陇东地区三叠系延长组7段(长7段)页岩孔隙结构复杂、非均质性强、地层压力预测精度较低等问题,提出了一种基于斑马算法优化支持向量回归机(ZOA-SVR)模型预测地层压力的方法,并在实际钻井中进行了应用,将预测结果与基于机器算法的模型和常规地层压力预测方法结果进行了对比。研究结果表明:①ZOA-SVR模型以实测地层压力数据为目标变量,优选与陇东地区长7段页岩地层压力数据关联度达到0.70以上的深度、声波时差、密度、补偿中子、自然伽马、深侧向电阻率、泥质含量等7个参数作为输入特征参数,设置训练样本数为40,交叉验证折数为5,初始化斑马种群数量为10,最大迭代次数为70,对惩罚因子和核参数进行优化并建模,参数优化后拟合优度指标R2达到0.942,模型预测的地层压力数据在训练集和测试集上的绝对误差均低于1 MPa,预测测试集地层压力数据与实测压力数据的平均相对误差为2.42%。②ZOA-SVR模型在研究区长7段地层压力预测中优势明显,比基于粒子群优化算法、灰狼算法和蚁群算法的模型具有更好的参数调节及优化能力,R2分别提高了0.209,0.327,0.142;比等效深度法、Eaton法、有效应力法预测的地层压力精度更高,相对误差分别降低了32.53%,15.31%,5.91%。③ZOA-SVR模型在实际钻井中的应用结果显示,研究区长7段地层压力在垂向上分布较稳定,泥页岩段的地层压力高于砂岩段,地层压力系数主要为0.80~0.90,整体上属于异常低压环境,与实际地层情况相符。

鲸鱼算法优化CNN-BiGRU-ATTENTION的车辆换道意图识别模型

针对时空多样特征、超参数敏感性影响车辆行为意图识别精度的问题,提出一种改进的CNN-BiGRU-ATTENTION混合换道意图识别模型。采用目标车辆轨迹序列和与周围车辆互动特征作为模型输入进行训练,实现考虑车辆动态变化状态的意图识别预测;使用鲸鱼优化算法对模型调整参数进行多目标寻优,降低模型调优难度;利用NGSIM数据集对模型进行评估校验。结果表明:所提出的WOA-CNN-BiGRU-ATTENTION模型与CNN-BiGRU-ATTENTION模型、Transformer模型相比,准确率分别提升了4.53%、0.97%,达到97.64%;WOA-CNN-BiGRU-ATTENTION模型在不同预判时间下的意图识别准确率最高,在换道前2.5 s的识别精度均能达到91%以上,证明模型具有较强的车辆换道意图识别性能。

高精度管道漏风量测试仪的设计及实验研究

现有的国产漏风量测试仪普遍存在测量精度低、自动化水平低的问题,为此,基于漏风量测试仪的原理,开展了漏风量测试仪的结构设计、系统设计及整机实验研究。首先,对漏风量的测量过程、喷嘴流量测量原理进行了研究,推导了喷嘴流量测量公式,并根据相关标准设计了角接取压方式的喷嘴结构,基于仪器操作便捷性要求,对整机结构进行了设计;其次,采用空气密度补偿公式完成了高精度流量自适应测量,设计了基于自锁式电磁阀的换向调零电路,采用换向调零法对压差传感器零点漂移进行了补偿,使用小波阈值去噪算法消除了信号噪声,提高了流量的测量精度;采用抗积分饱和比例积分微分(PID)算法对流量进行了闭环控制;最后,研制了管道漏风量测试仪样机,开展了压差与流量测量精度与重复性实验。实验及研究结果表明:该高精度漏风量测试仪工作状态稳定,压差测量精度为±0.4%,流量测量精度为±2%;该漏风量测试仪的测量精度优于国内同类仪器,符合国家标准要求,具有良好的市场前景。

中国植被碳源/汇时空演变特征及其驱动因素

植被生态系统对植被碳汇至关重要,是实现中国“碳中和”目标的重要路径之一。选择1981-2019年全球逐日NEP模拟数据产品,对1981-2019年中国植被碳源/汇时空演变进行分析,确定气候变化和人类活动对植被碳源/汇的影响区域,并量化生态修复治理工程对植被碳汇的成效。(1)通过使用BFAST模型监测NEP年际突变范围,确定2001年为时间断点,对比分析1981-2001年与2001-2019年NEP时空变化特征及驱动因素。(2)1981-2001年段植被碳汇大范围呈现递减趋势。2001-2019年,中国整体植被碳汇增加,尤其是北部地区NEP增长趋势显著。(3)1981-2001年中国北部地区植被固碳能力下降,受降水、辐射影响为主。2001-2019年,大部分地区NEP变化与降水相关性显著。(4)1981-2001年人类活动导致植被碳源/汇变化占总面积的4%,主要分布于东北地区和西南地区。2001-2019年中国植被碳源/汇变化由人类活动影响占比提高至26.23%,其中植被固碳能力提升占比25.22%。气候变化负向影响植被固碳能力较于1981-2001年减少约30%。说明人类活动在一定程度减少气候变化对植被的负面影响。(5)2019年相较于2001年净碳储量价值明显增长,大多地区生态修复与治理投资经济效率呈正比。此外,中国北部地区植被碳汇得到有效改善。因此,为了应对气候变化和保护生态环境,积极推进生态修复和治理投资,以实现经济、社会和环境的可持续发展。

基于相似测点对比的大坝变形监测数据粗差识别方法

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