A gradient-descent adjoint method for the reconstruction of boundary conditions in a river flow nitrification model.,Environmental Science: Processes & Impacts

当前位置： X-MOL 学术 › Environ. Sci.: Processes Impacts › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A gradient-descent adjoint method for the reconstruction of boundary conditions in a river flow nitrification model.
Environmental Science: Processes & Impacts ( IF 4.3 ) Pub Date : 2020-01-20 , DOI: 10.1039/c9em00500e
Geovanny Gordillo ₁ , Mario Morales-Hernández ₂ , Pilar García-Navarro ₁

Affiliation

One of the reasons for the limited applicability of predictive water quality models is the lack of data from monitoring control stations that are required as input. In this context, the main novelty of the present work is the recovery of information on the state variables present in a water quality model through measured data at a target downstream location. The reconstruction of the upstream boundary condition is the goal of the present work. For this purpose, an adjoint-state method is developed to find the sensitivities of the functional with respect to variations on the upstream boundary conditions of the model. The resolution of both forward and backward problems ensures strong, accurate and reliable solutions in both steady state and unsteady scenarios. The different cases demonstrate that the method is able to reconstruct any observed distribution with little computational effort, including the heat balance with all its external inputs.

中文翻译：

梯度下降伴随方法在河水硝化模型中边界条件的重建。

预测水质模型适用性有限的原因之一是缺乏来自监控控制站的数据作为输入。在这种情况下，本工作的主要新颖之处是通过目标下游位置的测量数据来恢复有关水质模型中状态变量的信息。上游边界条件的重建是当前工作的目标。为此目的，开发了一种伴随状态方法，以找到功能相对于模型上游边界条件变化的敏感性。向前和向后问题的解决方案可确保在稳态和非稳态情况下均提供强大，准确和可靠的解决方案。

更新日期：2020-02-26

点击分享查看原文

点击收藏

阅读更多本刊最新论文