Crop models are increasingly used to evaluate crop yields at regional/global scales. These applications require the integration and processing of very large data sets in order to explore the implications of land manag...
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Crop models are increasingly used to evaluate crop yields at regional/global scales. These applications require the integration and processing of very large data sets in order to explore the implications of land management options across spatially heterogeneous scales. These modeling involve the combination of large spatially explicit data sets for climate, biophysical and crop management variables as well as significant computational capacity for regional/global scale simulations. As a result, the application of crop models at regional/global scales is challenging due to the requirements for input data, calibration, validation and simulation setups appropriate for thousands to millions of spatial points. Not surprisingly, the implementation of these models across large areas using fine-scale grids can be limited by computational time requirements. To reduce the large computational load of an agroecosystem simulation process for regional and global scales, we developed an EPIC parallel computing framework (EPCF) to facilitate regional/global gridded crop modeling. The EPCF can make full use of the CPU resources of the workstation through parallel processing. For future users, only a few lines of additional code modification are needed to convert the single process code to parallelcomputing code. parallel processing in one machine makes it easy to handle the whole system without the overhead and expertise required for a distributed system. EPCF is a system that provides not only the ease of development but also cost efficiency.
Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calcula...
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Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework's performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallelcomputing approach. Moreover, an Advanced Micro Devices (AMD) Deep computing Unit (DCU)-parallel program is implemented into a water supply network simulation system;the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framew
The model predictive path integral (MPPI) control algorithm is applied to fixed-wing aircraft. Because MPPI imposes no restriction on the form of the cost function, arbitrarily complex maneuvers can be crafted through...
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ISBN:
(数字)9781624106095
ISBN:
(纸本)9781624106095
The model predictive path integral (MPPI) control algorithm is applied to fixed-wing aircraft. Because MPPI imposes no restriction on the form of the cost function, arbitrarily complex maneuvers can be crafted through cost function design. MPPI works by propagating (thousands of) trajectories forward in time using random control inputs. This procedure is done in parallel using a graphics processing unit (GPU). The optimal control is computed by weight-averaging the controls from these trajectories (with each weight corresponds to the respective trajectory cost). A nonlinear wind-axes aircraft model is used in the MPPI propagation step. This choice of model offers two benefits. First, the model remains nonlinear which is appropriate for aggressive flights. Second, the model does not require parameters. Prediction error due to parametric uncertainty can be eliminated. We demonstrate our method in a simulated air racing scenario. Despite the simulated result, the real-time feasibility is validated because 1) the implementation is done in a flight-capable software framework, and 2) the simulation is done in a high fidelity flight dynamics simulator. We show that MPPI offers superior performances compared to a typical waypoint guidance. These include a smaller altitude drop in tight turns, a better ability to stay on course, and a lower maximum load factor.
This paper is mainly for the design of new media data processing system. The research of new media data processing shows that the traditional database processing new media data is more and more huge, which causes the ...
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ISBN:
(纸本)9781665423168
This paper is mainly for the design of new media data processing system. The research of new media data processing shows that the traditional database processing new media data is more and more huge, which causes the operating system to do a lot of output and input operations (I / O) when querying, and the processing speed is slow. Through the analysis, the main reason that affects the new media data processing is the large amount of data. In this paper, Bayesian algorithm classifier model is used, and Map/Reduce function is used in the parallel computing framework. Multi computing resource nodes are used for parallel processing to effectively improve the speed of new media data processing.
The recent improvements in commercial space transportation have raised questions on methodology used for integrating space launched vehicles into the National Airspace System (NAS) regarding the risks to nearby aircra...
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ISBN:
(数字)9781624105951
ISBN:
(纸本)9781624105951
The recent improvements in commercial space transportation have raised questions on methodology used for integrating space launched vehicles into the National Airspace System (NAS) regarding the risks to nearby aircraft. Current safety regulation procedures are used to safely integrate launch vehicles in the NAS by closing large blocks of airspace to any vehicle, this restrictions are known as hazard areas. Regulations cause commercial aircraft to reroute around hazard areas increasing flight distance, causing delays, and raising overall cost per flight. Air space restrictions are designed to decrease risk to the public, where the area and time of restriction are based on space vehicle profile. This paper describes a methodology to dynamically construct a risk level map on nearby aircraft due to space launch operations. The impacted area is divided into multiple sections with each section dynamically evaluated under a risk level, which is a comprehensive index considering debris uncertainty. To speed up the evaluation process for the projectile model, a Graphics Processing Unit (GPU) based parallel computing framework is developed. Using a customized A* algorithm, an aircraft rerouting plan is generated under risk tolerance where a minimal time and distance trajectory can be found. Utilizing SpaceX Falcon 9 as a reference, rerouting models in this project are demonstrated to provide safe and efficient plans by comparing with the current method used.
Since its publication in 2014, the CFD Vision 2030 Study has become a touchstone against which CFD organizations of all types have measured their tools and techniques and set aspirations for their practices and proces...
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ISBN:
(数字)9781624105890
ISBN:
(纸本)9781624105890
Since its publication in 2014, the CFD Vision 2030 Study has become a touchstone against which CFD organizations of all types have measured their tools and techniques and set aspirations for their practices and processes. The study's vision of CFD in the year 2030 is one in which a single engineer will be able to manage many simulations on complex geometries within a time-critical period to a specified degree of accuracy. The study's authors identified five classes of impediments that would have to be overcome in the years between 2014 and 2030 to achieve the vision. Geometry modeling and mesh generation were cited as major, contributing factors to two impediments: lack of CFD simulation autonomy and reliability and lack of effective utilization of high performance computing platforms. This paper assesses progress in geometry modeling and mesh generation in the five years since the study's publication and imagines how further progress in those areas can make meshing invisible to the CFD practitioner.
This paper introduces a novel stochastic approach for the ground-delay-program planning under uncertainty, using chance-constrained optimization. The major advantage of the chance-constrained model is the ability to p...
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This paper introduces a novel stochastic approach for the ground-delay-program planning under uncertainty, using chance-constrained optimization. The major advantage of the chance-constrained model is the ability to provide robust solutions with user-defined service level. The approach is compared with the Ball etal. (''A Stochastic Integer Program with Dual Network Structure and Its Application to the Ground-Holding Problem,'' Operations Research, Vol.51, No.1, Feb.2003, pp. 167-171) model for selecting planned airport acceptance rates for airports in a metroplex, which is an interdependent system in close geographic proximity. The approaches were evaluated using real flight schedules and landing-capacity data from the New York City metroplex airports. Although the Ball etal. model was found to be more efficient, the chance-constrained model shows the ability to provide a quantized way to balance the solution's robustness and potential cost by choosing a proper service level. Moreover, the parallel-computingframework was demonstrated to be helpful in improving the computing efficiency, which suggests that deploying more computing resources would help solve a large-scale planning problem under uncertainty in the same framework.
Building Information Model/Modeling (BIM) upgrades the digitization of buildings from 2D to 3D and has become a common paradigm in architecture, engineering, construction, operations, and facility management (AECO/FM)...
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Building Information Model/Modeling (BIM) upgrades the digitization of buildings from 2D to 3D and has become a common paradigm in architecture, engineering, construction, operations, and facility management (AECO/FM) industry. However, embedding BIM in decision support systems is still a challenge because the BIM standard (Industry Foundation Classes, or IFC) is complex, and the geometries in BIM cannot be directly rendered in decision support systems, e.g., web and mobile-phone applications. Current efforts mainly focus on rendering BIM triangulation data, and limited studies investigate solutions solving quite long running time and enormously large memory usage in BIM triangulation process, especially in big BIM files. This study addresses this issue by introducing a parallel computing framework and providing an online geometry triangulation service. First, the reference relationships among the BIM objects were modeled as a graph according to the IFC specification. Second, the original large IFC file was split into several small independent IFC files in which all geometric objects that share the same shape representation were aggregated. Finally, the small separate IFC files were assigned to and triangulated in different computers in a-parallelcomputing cluster. Experiments showed that the proposed online service could greatly reduce memory usage and time consumption when triangulating the geometry of BIM objects. Processability has become a critical issue for BIM in the era of construction Big Data. The proposed scheme can triangulate big BIM files efficiently using limited memory and thus can dramatically improve the processability of BIM Big Data.
Fault prediction of industrial systems has been a hot research orientation in recent years, which allows the maintainer to know the operation conditions and the fault to be occurred in advance so as to reduce the risk...
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ISBN:
(纸本)9781538621653
Fault prediction of industrial systems has been a hot research orientation in recent years, which allows the maintainer to know the operation conditions and the fault to be occurred in advance so as to reduce the risk of fault and the economic loss. In general, association rules learning is one of the most effective methods in fault prediction of industrial systems, however, traditional methods based on association rules are not suitable for sparse time-series data that are common in industrial systems (e.g. transmission line data). Although some methods based on clustering to reduce the dimension of data have been proposed, these methods may lose some of the key rules from the dataset and reduce the effectiveness of the results. In order to solve the problem, we propose a novel algorithm called Multidimensional Time-series Association Rules(MTAR) in this paper, which can fully utilize the information and find out more valuable rules from multidimensional time-series data. Meanwhile, we implement the parallelization of the algorithm based on the parallel computing framework Spark, which can improve the performance of the algorithm greatly. Experiments are conducted on the transmission line dataset in Power Grid System to show the effectiveness and the efficiency of the proposed approach.
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