吴剑锋
地下水模拟;地下水资源评价与管理;地下水污染治理优化技术;水文地质统计方法。
个性化签名
- 姓名:吴剑锋
- 目前身份:
- 担任导师情况:
- 学位:
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学术头衔:
博士生导师
- 职称:-
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学科领域:
水文学
- 研究兴趣:地下水模拟;地下水资源评价与管理;地下水污染治理优化技术;水文地质统计方法。
吴剑锋,男,1971年3月生。1992年毕业于河南理工大学(原焦作矿业学院)地质系,获学士学位;1992-1994年在江西省乐平矿务局工作,任助理工程师;1997年和1999年分别获得南京大学硕士和博士学位。2006年12月晋升为教授,2008年4月被聘为博士生导师。曾于2002-2003年在Alabama大学从事博士后访问研究。目前为地球科学与工程学院水科学系“地下水科学与工程”专业主任、中国水利学会地下水科学与工程专业委员会委员,自2009年开始任国外SCI源刊“Hydrogeology Journal”的副主编(Associate Editor),并为国外多个SCI刊物(Water Resources Research, Ground Water, Journal of American Water Resources Association, Hydrogeology Journal和Journal of Hydraulic Engineering等)的审稿人。
在国内外各类重要期刊上已发表论文近70篇,其中SCI、EI检索论文超过30篇。近年来主持的主要项目有:2项国家自然科学基金、1项教育部回国人员科研启动基金、1项中科院知识创新工程项目子项目、1项国家环保部污染源调查评价专题项目。目前承担我国第一个有关地下水研究的国家基础研究计划项目(973项目)“华北平原地下水演变机制与调控(2010.01-2014.12)”,为第3课题“深层含水层系统变异与地下水可更新能力演变机理(2010CB428803)”的副负责人。
研究方向:
地下水模拟
地下水资源评价与管理
地下水污染治理优化技术
水文地质统计方法
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963
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成果数
17
吴剑锋, Jianfeng Wu·Li Zheng·Depeng Liu
Hydrogeology Journal (2007) 15: 1265-1278,-0001,():
-1年11月30日
Gaoqing Plain is a major agriculture center of Shandong Province in northern China. Over the last 30 years, the diversion of Yellow River water for intensive irrigation in Gaoqing Plain has led to elevation of the water table and increased evaporation, and subsequently, adramatic increase in salt content in soil and rapid degradation of crop productivity. Optimal strategies have been explored, that will balance the need to extract sufficient groundwater for irrigation (to ease the pressure on diverting Yellow River water) with the need to improve the local environment by appropriately lowering the water table. Two simulation-optimization models have been formulated and a genetic algorithm (GA) is applied to search for the optimal groundwater development strategies in Gaoqing Plain, while keeping the adverse environmental impacts in check. Compared with the trial-and-error approach of previous studies, the optimization results demonstrate that using an optimization model coupled with a GA search is both effective and efficient. The optimal solutions identified by the GA will provide Gaoqing Plain with the blueprints for developing sustainable groundwater abstraction plans to support local economic development and improve its environmental quality.ción con las técnicas de ensayo-y-error de estudios anteriores, los resultados de esta optimización demuestran que usando un modelo de optimización acoplado con una búsqueda mediante GA, es tanto eficaz como eficiente. Las soluciones óptimas identificadas por el GA, proporcionarán las bases para desarrollar los planes de extracción sustentable delagua subterránea en las llanuras de Gaoqing, los cuales darán apoyo al desarrollo económico localy mejorarán su calidad medioambiental.
Over-abstraction, Yellow River, Groundwater recharge/, water budget, Simulation-optimization model
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吴剑锋, Jianfeng Wu a, b, Chunmiao Zheng b, *, Calvin C. Chien c, Li Zheng d
Advances in Water Resources 29(2006)899-911,-0001,():
-1年11月30日
This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm (MCSGA) and noisy genetic algorithm (NGA), for cost-effective sampling network design in the presence of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA) with a numerical flow and transport simulator and a global plume estimator to identify the optimal sampling network for contaminant plume monitoring. The MCSGA approach yields one optimal design each for a large number of realizations generated to represent the uncertain K-field. A composite design is developed on the basis of those potential monitoring wells that are most frequently selected by the individual designs for different K-field realizations. The NGA approach relies on a much smaller sample of K-field realizations and incorporates the average of objective functions associated with all K-field realizations directly into the GA operators, leading to a single optimal design. The efficacy of the MCSGA-based composite design and the NGA-based optimal design is assessed by applying them to 1000 realizations of the K-field and evaluating the relative errors of global mass and higher moments between the plume interpolated from a sampling network and that output by the transport model without any interpolation. For the synthetic application examined in this study, the optimal sampling network obtained using NGA achieves a potential cost savings of 45% while keeping the global mass and higher moment estimation errors comparable to those errors obtained using MCSGA. The results of this study indicate that NGA can be used as a useful surrogate of MCSGA for cost-effective sampling network design under uncertainty. Compared with MCSGA, NGA reduces the optimization runtime by a factor of 6.5.
Contaminant transport, Monitoring network design, Spatial moment analysis, Noisy genetic algorithm, Monte Carlo analysis, Uncertainty
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吴剑锋, Jianfeng Wua, b, Chunmiao Zhenga, *, Calvin C. Chienc
Journal of Contaminant Hydrology 77(2005)41-65,-0001,():
-1年11月30日
A new simulation–optimization methodology is developed for cost-effective sampling network design associated with long-term monitoring of large-scale contaminant plumes. The new methodology is similar in concept to the one presented by Reed et al. (Reed, P.M., Minsker, B.S., Valocchi, A.J., 2000a. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resour. Res. 36 (12), 3731-3741) in that an optimization model based on a genetic algorithm is coupled with a flow and transport simulator and aglobal mass estimator to search for optimal sampling strategies. However, this study introduces the first and second moments of a three-dimensional contaminant plume as new constraints in the optimization formulation, and demonstrates the proposed methodology through a real-world application. The new moment constraints significantly increase the accuracy of the plume interpolated from the sampled data relative to the plume simulated by the transport model. The plume interpolation approaches employed in this study are ordinary kriging (OK) and inverse distance weighting (IDW). The proposed methodology is applied to the monitoring of plume evolution during a pump-and-treat operation at a large field site. It is shown that potential cost savings up to 65.6% may be achieved without any significant loss of accuracy in mass and moment estimations. The IDW-based interpolation method is computationally more efficient than the OKbased method and results in more potential cost savings. However, the OK-based method leads tomore accurate mass and moment estimations. A comparison of the sampling designs obtained with and without the moment constraints points to their importance in ensuring a robust long-term monitoring design that is both cost-effective and accurate in mass and moment estimations. Additional analysis demonstrates the sensitivity of the optimal sampling design to the various coefficients included in the objective function of the optimization model.
Contaminant transport, Monitoring network design, Interpolation method, Moment analysis, Genetic algorithm, Massachusetts Military Reservation (, MMR),
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吴剑锋, Jiazhong Qian·Hongbin Zhan · Jianfeng Wu · Zhou Chen
Hydrogeology Journal (2009) 17: 1749-1760,-0001,():
-1年11月30日
A fracture-karst aquifer is a karst aquifer with afractured rock matrix, and its parameters are difficult to determine. Two sequential pumping tests in a fracturekarst aquifer system at the Zhangji well field of China are considered, one carried out before (in 2000) and one after (in 2005) the operation of a pumping station in the well field (2003-2005). The sequential tests serve multiple purposes. First, they provide a cross check of the parameters obtained. Second, they can be used to assess the effect of long-term groundwater exploitation of the aquifer. A three-dimensional finite-element transient flow model has been developed to simulate groundwater flow at the site. Generally good agreement has been found between the simulated and observed hydraulic heads for both tests. The hydraulic parameters obtained from the 2005 test are generally consistent with their counterparts from the 2000 test. However, a small but steady increase of hydraulic conductivities from 2000 to 2005 at the site has been observed. A 10-year prediction of groundwater resources has been made and indicates that the well field can accommodate the proposed 8.0×104m3/day exploitation rate under relative drought conditions without causinga steady decline of groundwater levels.
Numerical modeling·Hydraulic properties·Karst·Multi-well pumping tests · China
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【期刊论文】PGO: A parallel computing platform for global optimization based on genetic algorithm☆
吴剑锋, Kejing Hea, Li Zhengb, Shoubin Donga, Liqun Tangc, Jianfeng Wud, Chunmiao Zhenge
Computers & Geosciences 33(2007)357-366,-0001,():
-1年11月30日
This paper presents the design, architecture and implementation of a general parallel computing platform, termed PGO, based on the Genetic Algorithm (GA) for global optimization. PGO provides an efficient and easy-to-use framework for parallelizing the global optimization procedure for general scientific modeling and simulation processes. Along with a core optimization kernel built on a GA, PGO also includes a general input generator and an output extractor that can facilitateits easy integration with various scientific computing tasks. In this paper, we demonstrate the efficiency and versatility of PGO with two different applications: (1) the parallelization of a large scale parameter estimation problem associated with modeling water flow in a heterogeneous deep vadose zone; (2) the parallelization of a complex simulation-optimization procedure for searching for an optimal groundwater remediation design. PGO is developed as an open source code, and is independent of the computer operating system. It has been tested in a heterogeneous computing environment consisting of Solaris 9, Fedora Core 2 Linux, and Microsoft Windows machines, and is freely available for download from.
Parallel computing, High performance computation platform, Global optimization, The Genetic Algorithm
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吴剑锋, 林锦, 郑春苗, Calvin C. Chien
水利学报,2007,38(10):1236~1244,-0001,():
-1年11月30日
将遗传算法和变密度地下水流及溶质运移模拟程序SEAWAT耦合起来,开发了一个新的用于地下水模拟优化管理的通用程序——SWTGA。以求解变密度条件下地下水优化管理问题,从而为地下水管理决策者提供科学依据和技术支持。设计SWTGA时,建立了适用于变密度条件下地下水优化管理常见问题的目标函数的一般形式,同时设定了常用的约束条件。最后将SWTGA 程序应用于一个理想滨海含水层中地下水开采方案的优化设计,寻优之后获得了最佳开采方案,与未优化开采方案的对比显示优化结果合理可行,验证了SWTGA 模拟优化程序的有效性和可靠性。
最优化算法, 地下水, 数值模拟, 优化, 模型
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吴剑锋, 杨蕴, 吴吉春
吉林大学学报(地球科学版),2009,39(3):74~81,-0001,():
-1年11月30日
分别将禁忌搜索和遗传算法与地下水流模型MODFLOW 和地下水溶质运移模型MT3DMS相耦合,并将其应用于求解地下水资源优化管理模型。在概述两种智能算法基本原理和地下水管理模型组成的基础上,结合两个理想的应用实例,从优化结果和计算效率两个方面对禁忌搜索和遗传算法进行了对比分析。在两个实例中,禁忌搜索分别以高于遗传算法10 倍和27 倍的计算效率得到了减少抽水流量约160m3/d和节约治理成本约47万元的治理方案。结果表明,禁忌搜索在求解地下水管理模型中具有较好的应用前景。
禁忌搜索, 遗传算法, 地下水管理模型, 全局最优
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吴剑锋, 刘玲玲, 吴吉春
水文地质工程地质,2009,5:66~71,-0001,():
-1年11月30日
应用地质统计方法研究渗透系数场的空间变异性。利用MMR 含水层场地实测数据,通过去类分析、特异值处理、 正态变换,逐步逼近研究区渗透系数的稳健变差函数,得到三维渗透系数场的几何各向异性套合模型。在此基础上,采用普通克里格法和指示克里格法、高斯序列模拟法和指示序列模拟法分别对数据进行插值和条件模拟。最后结合具体的地质条件,对四种方法在渗透系数场生成中的应用进行对比分析和评价。
空间变异性, 变差函数, 克里格法, 条件模拟, GSLIB
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【期刊论文】基于区域Delaunay自动剖分的含水层参数反演
吴剑锋, 徐月平
地下水,2009,31(1):19~22,-0001,():
-1年11月30日
针对地下水有限元数值模拟中区域三角网格剖分复杂难以处理的情况,提出适合其特点的Delaunay 三角网格自动剖分方法,并对含有多个参数分区的含水层进行网格剖分,最后利用遗传算法和有限元程序相耦合来反演含水层水文地质参数。结果表明此方法可大大简化地下水数值模拟的前处理工作,并能提高有限元网格剖分的有效性和准确性,从而得到令人满意的数值模拟结果。
Delaunay 网格剖分, 遗传算法, 有限元, 水文地质参数, 反演
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吴剑锋, 丁东
工程勘察,2009,2:32~35,-0001,():
-1年11月30日
采用一种微粒群优化算法来识别承压完整井非稳定地下水运动Theis 公式中的水文地质参数。微粒群算法是一种新型的群体智能算法,它将每个个体看作在多维搜索空间中的一个没有重量和体积的微粒,并在搜索空间中以一定的速度飞行,该飞行速度由个体的飞行经验和群体的飞行经验进行动态调整。然后根据个体适应值大小运算,根据适应度函数对微粒的速度和位置进行进化,最终得到足够好的适应度值。本文采用微粒群算法可根据抽水试验资料快速反演Theis 公式近似解析解中的水文地质参数。实例计算结果表明该微粒群算法计算速度快,在水文地质逆问题求解中值得推广应用。
微粒群优化算法, Theis 公式, 参数识别, 解析近似解
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