Jinkyoo Park

We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSSP as a sequential decision-making problem with graph representation of the state to consider the structure of JSSP. In solving the formulated problem, the proposed framework employs a GNN to learn that node features that embed the spatial structure of the JSSP represented as a graph (representation learning) and derive

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collec

The objective of this study is to improve the cost-effectiveness and production efficiency of wind farms using cooperative control. The key factors in determining the power production and the loading for a wind turbine are the nacelle yaw and blade pitch angles. However, the nacelle and blade angles may adjust the wake direction and intensity in a way that may adversely affect the performance of other wind turbines in the wind farm. Conventional wind-turbine control methods maximize the power pr

This paper describes a data-driven approach for real-time control of a physical system. Specifically, this paper focuses on the cooperative wind farm control where the objective is to maximize the total wind farm power production by using control actions as an input and measured power as an output. For real time, data-driven wind farm control, it is imperative that the optimization algorithm is able to improve a target wind farm power production by executing as small number of trial actions as p

Using a machine learning approach, this study investigates the effects of machining parameters on the energy consumption of a milling machine tool, which would allow selection of optimal operational strategies to machine a part with minimum energy. Data-driven prediction models, built upon a nonlinear regression approach, can be used to gain an understanding of the effects of machining parameters on energy consumption. In this study, we use the Gaussian Process to construct the energy prediction

Neutral boundary layer (NBL) flow fields, commonly used in turbine load studies and design, are generated using spectral procedures in stochastic simulation. For large utility-scale turbines, stable boundary layer (SBL) flow fields are of great interest because they are often accompanied by enhanced wind shear, wind veer, and even low-level jets (LLJs). The generation of SBL flow fields, in contrast to simpler stochastic simulation for NBL, requires computational fluid dynamics (CFD) procedures

The objective of this study is to develop a model-free optimization algorithm to improve the total wind farm power production in a cooperative game framework. Conventionally, for a given wind condition, an individual wind turbine maximizes its own power production without taking into consideration the conditions of other wind turbines. Under this greedy control strategy, the wake formed by the upstream wind turbine, due to the reduced wind speed and the increased turbulence intensity inside the

H- and T-shaped cross sections are known to be susceptible to rotational single-degree-of-freedom aerodynamic instabilities. Here, such self-excited aerodynamic response of a T-shaped cantilever structure is used to extract energy, which is then converted into electric power through an electromagnetic transducer. The complex fluid–structure interaction between the cantilever harvester and wind flow is analyzed numerically and experimentally. To study the dynamic response of the cantilever and es

This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions among the wind turbines in a wind farm. Such greedy control strategy, when an upstream wind turbine attempts to maximize its power production, can significantly lower the power productions of the downstream wind turbines and, thus, reduces the overall

This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions between the wind turbines in a wind farm. Because of wake interference, such greedy control strategy can significantly lower the power production of the downstream wind turbines and, thus, reduce the overall wind farm power production. As an alternati