AI 中文总结
针对波能转换器阵列多学科设计优化问题，考虑不确定性，从多学科选决策变量与参数构建垂荡式点吸收器阵列。通过改变相关因素最小化单位体积功率，利用多项式混沌展开法处理不确定性，解决传统方法忽视多学科耦合及不确定性的局限。
AI 中文摘要
本文针对波能转换器阵列提出了不确定性下的多学科设计优化问题。通过从几何、流体动力学、布局和轨迹优化等耦合学科中选择决策变量和参数，构建了用于大规模电网发电的垂荡式点吸收器阵列，实现了装置与控制的协同设计。研究了多学科设计优化（MDO）应用于波能转换器农场布局优化的益处。通过改变波能转换器尺寸、阵列布局和控制增益，以最小化单位体积功率。在每次设计迭代中，基于多项式混沌展开（PCE）方法的回归处理电功率的不确定性。传统方法常忽略波能转换器的多学科耦合性质以及海浪条件和控制响应中的固有不确定性，导致设计在实际环境中表现不佳。本文利用PCE技术将不确定性直接纳入设计优化过程，量化不确定波环境下性能的变异性，解决了这一局限。
英文摘要
In this paper, a multidisciplinary design optimization problem under uncertainty is formulated for wave energy converter array. An array of heaving point absorbers for grid-scale energy production with decision variables and parameters chosen from the coupled disciplines of geometry, hydrodynamics, layout, and trajectory optimization thus resulting in a control co-design formulation of the plant and the control together. We study the benefits of MDO as applied to WEC farm layout optimization. We vary the wave energy converter (WEC) dimensions, array layout, and control gain to minimize the power per volume. Uncertainty in the electrical power is handled using regression based on polynomial chaos expansion (PCE) method at each design iteration. Traditional WEC farm design optimization approaches often neglect the multidisciplinary, coupled nature of WECs and the inherent uncertainty in ocean wave conditions and control responses. This leads to designs that may under perform in real-world environments. In this work, we address this limitation by incorporating uncertainty directly into the design optimization process using the technique of polynomial chaos expansion (PCE) to quantify the variability of the performance due to uncertain wave environment.
CommentsDraft of manuscript detailing methodology. Results are inprogress and will be added upon completion of the optimization experiments
