Evaluation of an offshore wind farm computational fluid dynamics model against operational site data

Modelling wind turbine wake effects at a range of wind speeds and directions with actuator disk (AD) models can provide insight but also be challenging. With any model it is important to quantify the level of error, but this can also present a challenge when comparing a steady-state model to measurement data with scatter. This paper models wind flow in a wind farm at a range of wind speeds and directions using an AD implementation. The results from these models are compared to data collected from the actual farm being modelled. An extensive comparison is conducted, constituted from 35 cases where two turbulence models, the standard k-ε and k-ω SST are evaluated. The steps taken in building the models as well as processes for comparing the AD computational fluid dynamics (CFD) results to real-world data using the regression models of ensemble bagging and Gaussian process are outlined. Turbine performance data and boundary conditions are determined using the site data. Modifications to an existing opensource AD code are shown so that the predetermined turbine performance can be implemented into the CFD model. Steady state solutions are obtained with the OpenFOAM CFD solver. Results are compared in terms of velocity deficit at the measurement locations. Using the standard k-ε model, a mean absolute error for all cases together of roughly 8% can be achieved, but this error changes for different directions and methods of evaluating it

PRELIMINARY GEOTHERMAL INVESTIGATION IN THE BASIN OF KATERINI (NORTHERN GREECE)

Η παρούσα εργασία περιγράφει τα αποτελέσματα της προκαταρκτικής γεωθερμικής έρευνας που διενεργήθηκε από το ΙΓΜΕ στην λεκάνη της Κατερίνης (Β. Ελλάδα). Οι ερευνητικές εργασίες κατά κύριο λόγο περιελάμβαναν θερμομετρήσεις στην κεφαλή 73 υπαρχουσών υδρογεωτρήσεων, μέτρηση της θερμοκρασίας σε συνάρτηση με το βάθος στο εσωτερικό πέντε (5) γεωτρήσεων και χημικές αναλύσεις 18 δειγμάτων από επιλεγμένες θέσεις. Με βάση τα στοιχεία που προέκυψαν από την κατανομή των θερμοκρασιών, διακρίνονται τρεις επιμέρους περιοχές γεωθερμικού ενδιαφέροντος. Ως σημαντικότερη κρίνεται αυτή του Κάτω Αγίου Ιωάννη (νότια τμήμα της λεκάνης), όπου μετρήθηκαν θερμοκρασίες της τάξης των 27oC σε βάθος 340m. Εν τούτοις, για τη λεπτομερή διερεύνηση των γεωθερμικών συνθηκών στη λεκάνη Κατερίνης, απαιτείται περαιτέρω συστηματική έρευνα και κατασκευή τουλάχιστον μίας ερευνητικής γεωθερμικής γεώτρησης μεγάλου βάθους.This paper describes the results of the preliminary surface geothermal exploration conducted by IGME in the basin of Katerini (Northern Greece). It mainly regards temperature measurements at the wellhead of 73 wells and in the interior of five boreholes, as well as sampling and chemical analyses from 18 selected sites. Based on the collected data, three sub-regions of geothermal interest can be distinguished, the most important of which is the area of Kato Agios Ioannis (to the south of the basin), with temperatures around 27oC at the depth of 340m. Nonetheless, the detailed geothermal investigation of this basin requires further systematic research as well as the drilling of at least one deep geothermal exploration borehole

Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone

This work investigates the problem of simultaneous tracking and jamming of a rogue drone in 3D space with a team of cooperative unmanned aerial vehicles (UAVs). We propose a decentralized estimation, decision and control framework in which a team of UAVs cooperate in order to a) optimally choose their mobility control actions that result in accurate target tracking and b) select the desired transmit power levels which cause uninterrupted radio jamming and thus ultimately disrupt the operation of the rogue drone. The proposed decision and control framework allows the UAVs to reconfigure themselves in 3D space such that the cooperative simultaneous tracking and jamming (CSTJ) objective is achieved; while at the same time ensures that the unwanted inter-UAV jamming interference caused during CSTJ is kept below a specified critical threshold. Finally, we formulate this problem under challenging conditions i.e., uncertain dynamics, noisy measurements and false alarms. Extensive simulation experiments illustrate the performance of the proposed approach.Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning

In this work we propose a coverage planning control approach which allows a mobile agent, equipped with a controllable sensor (i.e., a camera) with limited sensing domain (i.e., finite sensing range and angle of view), to cover the surface area of an object of interest. The proposed approach integrates ray-tracing into the coverage planning process, thus allowing the agent to identify which parts of the scene are visible at any point in time. The problem of integrated ray-tracing and coverage planning control is first formulated as a constrained optimal control problem (OCP), which aims at determining the agent's optimal control inputs over a finite planning horizon, that minimize the coverage time. Efficiently solving the resulting OCP is however very challenging due to non-convex and non-linear visibility constraints. To overcome this limitation, the problem is converted into a Markov decision process (MDP) which is then solved using reinforcement learning. In particular, we show that a controller which follows an optimal control law can be learned using off-policy temporal-difference control (i.e., Q-learning). Extensive numerical experiments demonstrate the effectiveness of the proposed approach for various configurations of the agent and the object of interest.Comment: 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09 December 2022, Cancun, Mexic

Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents

In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.Comment: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 202