Improving water quantity simulation &amp; forecasting to solve the energy-water-food nexus issue by using heterogeneous computing accelerated global optimization method

Kan, Guangyuan; Zhang, Mengjie; Liang, Ke; Wang, Hao; Jiang, Yunzhong; Li, Jiren; Ding, Liuqian; He, Xu; Hong, Yang; Zuo, Depeng; Bao, Zhenxin; Li, Chaochao

doi:10.1016/j.apenergy.2016.08.017

Cited by 52 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The successful application of OpenACC (Kan, Zhang, et al 2016;Kan, Lei, et al 2017;Kan et al 2018;Kan, He, Ding, et al 27 / 31 2017) in CLTx leads to great improvement in computational efficiency for studying the MHD instability in Tokamak. The migration of CLTx from the CPU-MPI platform to GPU-OpenACC platform is relatively easy compared with completely rewriting the code into CUDA or OpenCL.…”

Section: Discussionmentioning

confidence: 99%

Acceleration of three-dimensional Tokamak magnetohydrodynamical code with graphics processing unit and OpenACC heterogeneous parallel programming

Zhang

Zhu

et al. 2019

International Journal of Computational Fluid Dynamics

View full text Add to dashboard Cite

In this paper, the OpenACC heterogeneous parallel programming model is successfully applied to modification and acceleration of the three-dimensional Tokamak magnetohydrodynamical code (CLTx). Through combination of OpenACC and MPI technologies, CLTx is further parallelized by using multiple-GPUs. Significant speedup ratios are achieved on NVIDIA TITAN Xp and TITAN V GPUs, respectively, with very few modifications of CLTx. Furthermore, the validity of the double precision calculations on the above-mentioned two graphics cards has also been strictly verified with m/n=2/1 resistive tearing mode instability in Tokamak.

show abstract

Section: Discussionmentioning

confidence: 99%

Acceleration of three-dimensional Tokamak magnetohydrodynamical code with graphics processing unit and OpenACC heterogeneous parallel programming

Zhang

Zhu

et al. 2019

International Journal of Computational Fluid Dynamics

View full text Add to dashboard Cite

show abstract

“…It provides a modelling framework for environmental hydrology and supports simulation, estimation, assessment and visualization of flow response to time series of rainfall and other drivers. The IHACRES model (which has been used in various studies [52][53][54]) catchment moisture deficit (CMD) version [55] is used here as it has more consistent physical meaning for the parameters. There are two stores in the IHACRES model: the nonlinear store for the generation of effective rainfall, which uses a nonlinear function to deal with the raw rainfall, as well as using accounting equations to calculate the CMD output, and the linear store which converts the effective rainfall into quick and slow flow using unit hydrographs.…”

Section: Rainfall-runoff Modelsmentioning

confidence: 99%

Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models

Guo

Zhang

et al. 2018

Water

View full text Add to dashboard Cite

Hydrologic models are essential tools for understanding hydrologic processes, such as precipitation, which is a fundamental component of the water cycle. For an improved understanding and the evaluation of different precipitation datasets, especially their applicability for hydrologic modelling, three kinds of precipitation products, CMADS, TMPA-3B42V7 and gauge-interpolated datasets, are compared. Two hydrologic models (IHACRES and Sacramento) are applied to study the accuracy of the three types of precipitation products on the daily streamflow of the Lijiang River, which is located in southern China. The models are calibrated separately with different precipitation products, with the results showing that the CMADS product performs best based on the Nash-Sutcliffe efficiency, including a much better accuracy and better skill in capturing the streamflow peaks than the other precipitation products. The TMPA-3B42V7 product shows a small improvement on the gauge-interpolated product. Compared to TMPA-3B42V7, CMADS shows better agreement with the ground-observation data through a pixel-to-point comparison. The comparison of the two hydrologic models shows that both the IHACRES and Sacramento models perform well. The IHACRES model however displays less uncertainty and a higher applicability than the Sacramento model in the Lijiang River basin.are frequently missing because of malfunctioning of devices [3]. Remote sensing [4] and modelling [5] of precipitation have become viable approaches to address these problems effectively and are often used as input data to hydrologic models.With regard to the development of remote-sensing technology, satellite-derived precipitation data are an attractive alternative in data-sparse regions because of the relatively high resolution and complete spatial coverage.

show abstract

“…Various authors have noted that the complexity of FEW system requires a nexus approach . Numerous authors in recent days (especially since the launch of a major USA National Science Foundation (NSF) funding program ) have proposed predictive models of the coupled FEW system . Many of these studies take an approach that emphasizes the complexity of the system.…”

Section: Few Modelsmentioning

confidence: 99%

Water quality monitoring for coupled food, energy, and water systems

Mickelson

Tsvankin

2017

Env Prog and Sustain Energy

View full text Add to dashboard Cite

The focus is on generating real time data for predictive models of food, energy, and water (FEW) systems. It is hypothesized that stressors affecting a FEW system universally impact the water quality of the system. Conversely, fine grain temporal and spatial data describing the water quality of a FEW system can be used to locate system stressors. A high level, predictive model of a FEW system is presented. It is noted that the dynamics of the system are driven by steady state operating characteristics and system stressors. An approach to generation of fine grained spatial temporal water quality data is discussed and an archetypical element of a sensor array is fabricated. The archetypical micro‐controlled sensor element is experimentally evaluated. The results are applied to determining the characteristics necessary of an element of a sensor arrays that is to be used in the prediction of the dynamics of a FEW system. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 165–171, 2018

show abstract

Improving water quantity simulation & forecasting to solve the energy-water-food nexus issue by using heterogeneous computing accelerated global optimization method

Cited by 52 publications

References 33 publications

Acceleration of three-dimensional Tokamak magnetohydrodynamical code with graphics processing unit and OpenACC heterogeneous parallel programming

Acceleration of three-dimensional Tokamak magnetohydrodynamical code with graphics processing unit and OpenACC heterogeneous parallel programming

Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models

Water quality monitoring for coupled food, energy, and water systems

Contact Info

Product

Resources

About