Steady-state Reynolds-averaged Navier-Stokes (RANS) simulations of urban wind fields and linear interpolation between computed wind directions, are validated against experimental wind sensor measurements. In comparison with linear interpolation, the effectiveness of Proper Orthogonal Decomposition (POD)-based reduced-order modeling is also evaluated with the objective of reducing data storage requirements while maintaining interpolation accuracy. The study focuses on a complex urban area of approximately 3 km2 surrounding the campus of the Technical University of Berlin. A total of 36 wind field simulations are conducted for inflow wind directions at 10 degrees intervals. Validation against long-term experimental wind measurements at nine locations within the urban area yields average deviations of 21.7% for relative wind speed, 0.58 m/s for absolute wind speed, and 28 degrees for wind direction. A standard linear interpolation approach is subsequently applied to estimate wind fields for various wind directions using an interpolation database of 12 simulated wind fields. The validation of the interpolated wind fields against experimental measurements results in average deviations of 29.7% for relative wind speed, 0.56 m/s for absolute wind speed, and 31 degrees for wind direction. It is further demonstrated that increasing the number of wind fields in the interpolation database beyond 12 does not lead to an improvement in estimation accuracy. Finally, POD-based interpolation is introduced as a reduced-order modeling approach aimed at substantially reducing data storage requirements. The accuracy of the wind field estimates obtained using POD-based interpolation is evaluated in comparison with standard linear interpolation. Using six POD modes, an estimation error of 6.3% is obtained, compared to 12% for linear interpolation based on six wind directions, this demonstrates an improved accuracy for an equivalent level of data storage using POD-based interpolation.

The paper discusses a 2.5-dimensional trajectory or route planning tool for the operation of unmanned aircraft systems (UAS) in urban environments that considers not only the distance travelled, but also the Global Navigation Satellite Systems (GNSS) performance (e.g., accuracy, integrity, continuity, and availability), separation to static and dynamic (i.e. traffic), energy expanded, time cost, impact of wind, impact of UAS noise, and various other criteria. GNSS availability will drive the necessity to include alternative navigation sensors to meet the required navigation performance necessary for urban operations. Furthermore, UAS operating in urban environments need to manage the prevailing urban wind effects like gusts and local vortices that constitute a high potential risk for failure and safety. This paper will discuss the tool’s general structure and then provide some resulting using a subset of the above criteria to find the optimal trajectory, namely time cost (distance and speed), wind, energy expanded and the expected GNSS navigation performance along the trajectory. The energy used will consider the wind within the urban environment.

Three-dimensional Reynolds-Averaged Navier-Stokes simulations are performed to calculate the wind field of a full-size urban district of 1 km ^2 around the campus of Technical University of Berlin with the inlet wind direction as a parameter. Two-dimensional snapshots of the simulation data set are used for model reduction by proper orthogonal decomposition (POD) to reduce the complexity of the system. The POD modes are then used to estimate a high resolution wind field from sparse velocity sensor measurements by using the Gappy POD as a data reconstruction method. The sensor measurements are taken from a simulation test case that is not included in the snapshot basis. The sensor placement problem that needs to be solved to find effective sensor locations is investigated using two different methods. The performance of the data reconstruction is then assessed by calculating the least-squares reconstruction error. An application of the estimated urban wind field is demonstrated by finding a wind-based minimum-energy path from a start to a final location for an operation of unmanned aerial vehicles. The wind-based path is compared to the shortest path for a reduced order model taking only 5 sensor measurements. An overall mean energy reduction of 5.5 % was calculated by the full-order model and of 3.9 % by the reduced order model. This suggests that trajectory planning may be performed on inexpensive reduced-order models of the urban wind field.

The paper discusses an unmanned aircraft (UA) trajectory planning tool for dense aerial operations in urban environments that considers not only the distance travelled, but also the Global Navigation Satellite Systems (GNSS) performance (e.g., accuracy and availability), separation to traffic and obstacles, energy expanded, time cost, impact of wind, impact of UA noise, and various other criteria. GNSS availability will drive the necessity to include alternative navigation sensors such as laser scanners, vision sensors and altimeters in the navigation mechanization to meet the required navigation performance necessary for urban operations. Furthermore, UAs operating in urban environments need to manage the prevailing urban wind effects like gusts and local vortices that constitute a high potential risk for failure and safety. Finally, this paper focusses on operations in the vicinity of transportation hubs (e.g., train stations, harbors, airports) where transfer of goods between UA and other modes of transportation is expected to result in an increased UA density, and thus the need for reliable separation methods of all participants. This paper will discuss the tool’s general structure and then provide some resulting using four of the above criteria to find the optimal trajectory, namely safe separation, time cost (distance and speed), energy expanded and the expected GNSS/alternative navigation performance along the trajectory. The energy used will consider the wind within the urban environment

A trajectory planning algorithm based on the traditional A* formulation is designed to determine the minimum-energy path from a start to a final location taking into account the prevailing wind conditions. To obtain average wind conditions in an urban environment, full-scale Reynolds-averaged Navier–Stokes simulations are first performed using OpenFoam® for various inlet wind directions on a computational model representing complex buildings on the campus of the Technical University of Berlin. The proper orthogonal decomposition (POD) modes of the full database are then calculated in an offline stage with the wind direction as a parameter. Next, the online reconstruction of the complete urban wind field is performed by Gappy POD using simulated pointwise measurements obtained by sparse sensors. Finally, the trajectory planning algorithm is applied to the reconstructed wind field and validated by comparison with the trajectory computed on the full-order computational fluid dynamics (CFD) model. The main conclusion is that the error made by calculating the energy requirements for a specific trajectory based on an inexpensive reduced-order model of the wind field instead of an expensive full-order CFD database is only a few percent in all investigated cases. Therefore, a reliable and trustworthy trajectory can be calculated from the inexpensive reduced-order model obtained with only a few velocity sensors. Furthermore, it is shown that the energy consumption along a trajectory could be reduced by up to 20% by taking the prevailing wind field into consideration instead of considering the shortest path.

A family of MEMS calorimetric wall shear stress sensors is experimentally and numerically investigated to determine their most significant design parameters. Fifteen sensor prototypes are first calibrated in a range of ±2Pa to generate an experimental database for validation of the subsequent numerical investigation. Then, a fully parameterized numerical setup is used to investigate the effect of three geometric design parameters, namely the cavity height, the cavity width, and the inter-beam distance, on amplitude and sensitivity of the sensor. This is done by building a surrogate model based on Gaussian Process interpolation (Kriging) in the four-dimensional space consisting of the three design parameters and the shear velocity in the flow. Thanks to this methodology, the calibration curves of all possible sensor designs in the investigated range can be estimated with an error of less than 2%. A detailed study of this model reveals that the most significant design parameters are the inter-beam distance and the cavity width, while the cavity height is found to be of minor importance.

UAVs operating in urban environments must manage the prevailing urban wind effects like gusts and local vortices that constitute a high potential risk for failure and safety. The present contribution focuses on how to make an UAV flight in cluttered urban wind conditions more efficient using a model reduction approach. A trajectory planning algorithm based on the traditional A* formulation was designed to determine the minimum-energy path from a start to a final location taking into account the prevailing wind conditions. In order to obtain average wind conditions in an urban environment, full-scaleRANSsimulationswere performed using the OpenFoam® CFD toolbox for various inlet wind directions on a computational model representing complex buildings on the campus of the Technical University of Berlin. The CFD simulations require high computational costs that make on-line calculations not feasible. Hence, the key ingredient of the present investigation is the on-line reconstruction of the complete urban wind field using a Gappy POD approach with measurements from sparsely distributed sensors. Specifically, a full database of the urban wind field is first simulated off-line using OpenFoam®. Then, a reduced-order estimate of the wind field is obtained using sparse sensor measurements by solving a linear combination of the POD modes that were previously calculated on the full database during the off-line stage. Finally, the trajectory planning algorithm is applied to the reconstructed flow field and validated by comparison with the trajectory that is calculated using the wind field of the full-order model obtained by CFD. The main conclusion is that the error made by calculating the wind field with an inexpensive reduced-order model instead of an expensive full-order CFD database is small, so that a reliable trajectory is obtained by using a wind field that is reconstructed by just a few sensors. Furthermore the energy consumption along a trajectory is reduced by taking the prevailing wind field of an urban area into consideration.

The paper discusses an unmanned aerial vehicle (UAV) trajectory planning tool that assesses areas in an urban environment with respect to Global Navigation Satellite Systems (GNSS) availability, separation to obstacles, energy expanded, and impact of wind. The tool determines what areas of the urban environment are navigable, i.e., where does have GNSS sufficient performance (accuracy, integrity, availability, and continuity) to support safe collision-free operation. The tool, furthermore, uses the input from sensors located throughout the city to estimate the local wind field. Based on the wind field and safe navigation areas, both an energy-based cost function and a speed adjustment scheme are adopted to determine the best, in terms of energy usage, trajectory from origin to destination. Preliminary results show that up to 18% of energy savings while navigating at a safe distance from the buildings.

The control of flow separation on aerodynamic surfaces remains a fundamental goal for future air transportation. On airplane wings and control surfaces, the effects of flow separation include decreased lift, increased drag, and enhanced flow unsteadiness and noise, all of which are detrimental to flight performance, fuel consumption, and environmental emissions. Many types of actuators have been designed in the past to counter the negative effects of flow separation, from passive vortex generators to active methods like synthetic jets, plasma actuators, or sweeping jets. At the Chair of Aerodynamics at TU Berlin, significant success has been achieved through the use of pulsed jet actuators (PJA) which operate by ejecting a given amount of fluid at a specified frequency through a slit-shape slot on the test surface, thereby increasing entrainment and momentum in a separating boundary layer and thus delaying flow separation. Earlier PJAs were implemented using fast-switching solenoid valves to regulate the jet amplitude and frequency. In recent years, the mechanical valves have been replaced by fluidic oscillators (FO) in an attempt to generate the desired control authority without any moving parts, thus paving the way for future industrial applications. In the present article, we present in-depth flow and design analysis which affect the operation of such FO-based PJAs. We start by reviewing current knowledge on the mechanism of flow separation control with PJAs before embarking on a detailed analysis of single-stage FO-based PJAs. In particular, we show that there is a fundamental regime where the oscillation frequency is mainly driven by the feedback loop length. Additionally, there are higher-order regimes where the oscillation frequency is significantly increased. The parameters that influence the oscillation in the different regimes are discussed and a strategy to incorporate this new knowledge into the design of future actuators is proposed.

The influence of the tip-to-tip distance on the mutually induced aerodynamic forces and moments of a pair of wings simulating the coupling of two aircraft into a compound configuration was numerically and experimentally investigated. The geometry consisted of two identical NACA 0021 rectangular wings with square tips. The numerical investigation was performed as a Reynolds-Averaged Navier Stokes Simulation (RANS) using the OpenFOAM® software package. The simulation was validated by wind-tunnel experiments, whereby the wings were equipped with four chordwise rows of pressure taps to measure the wall pressure. Records were made for different wing-tip distances and different angles of attack combinations. The investigation showed that the relative position of the wings has an influence on their aerodynamic forces and moments, which is relevant in the design of compound, high-altitude and long endurance (HALE) aircraft.