Recent Submissions
Updates on the OpenFAST lidar simulator
(2022) Guo, Feng; Schlipf, David; Zhu, Hailong; Platt, Andy; Cheng, Po Wen; Thomas, Florian
Lidar systems are able to measure the wind speed remotely by detecting the aerosol movement caused by wind. A nacelle-based lidar system scanning the wind in front of a wind turbine can provide a preview of the incoming wind before the wind interacts with the turbine. Implementing a realistic lidar simulator into the wind turbine aero-elastic simulation tool can be beneficial for various wind energy related fields, such as lidar-assisted control, load validation, and load monitoring. In previous work, a lidar simulation module has been integrated into the open-source aero-elastic simulation tool OpenFAST, covering already lidar characteristics like different scan patterns, the volume averaging along the beam, and the coupling with the nacelle motion due to turbine tower dynamics. This paper focuses on adding further features to the lidar simulation module of OpenFAST to make the lidar simulation more realistic: the evolving turbulence, the blade blockage effect, and the adjustable data availability. Further, the wind preview quality with the new features is assessed.
Wind field reconstruction using nacelle based lidar measurements for floating wind turbines
(2022) Gräfe, Moritz; Pettas, Vasilis; Cheng, Po Wen
In this work we investigate the influence of floater motions on nacelle based lidar wind speed measurements. The analysis is focused on wind field characteristics which are most relevant for performance analysis, namely rotor effective wind speed, shear and turbulence intensity. A numerical approach, coupling the publicly available in-house lidar simulation framework ViConDAR (Virtual Constrained turbulence and liDAR measurements) with an aeroelastic floating offshore wind turbine simulation is employed. Synthetic turbulent wind fields are generated with the open source turbulence generator TurbSim. The dynamics of the floating offshore wind turbine are simulated in the aeroelastic simulation code OpenFAST. Turbine dynamics and the corresponding synthetic wind field are then passed to the lidar simulation module of ViConDAR, which has been adapted for the consideration of turbine dynamics in all six degrees of freedom to simulate the lidar measurements under influence of motion. The simulation framework is demonstrated in a case study, simulating a lidar system on the nacelle of the IEA 15 MW turbine in combination with the WindCrete floater concept. Two different realistic lidar patterns are investigated under different metocean conditions. Different motion cases are examined individually and combined to evaluate the influence of rotational and translational degrees of freedom. Results show an increase of mean absolute error between lidar estimated and full wind field rotor effective wind speed of 0 to 25% depending on the environmental conditions. Observed overestimation of mean rotor effective wind speed was found to be in the region of up to 1%.
Power curve measurement of a floating offshore wind turbine with a nacelle-based lidar
(2022) Özinan, Umut; Liu, Dexing; Adam, Raphaël; Choisnet, Thomas; Cheng, Po Wen
Understanding the power performance of floating offshore wind turbines is essential for the economics of floating wind, which requires reliable wind speed measurements. This work investigates the use of nacelle-based lidar technology for power curve measurement of a floating offshore wind turbine. In a five-month long measurement campaign, a nacelle-based lidar was installed on top of a 2MW floating offshore wind turbine. It was used to reconstruct the wind field including a carrier-to-noise ratio threshold filter, an availability filter and a hard target filter for the one hertz line of sight measurements. The quality of lidar wind speed estimations were compared to nacelle-based sonic anemometer measurements in terms of their linear correlation and root mean squared error of ten minute averages. The correlation of rotor averaged wind speed estimation of lidar and sonic wind speed measurements showed a 0.97 coefficient of determination. Variability of correlation is investigated in terms of nacelle excitations to identify the uncertainty related to the use of nacelle lidars for floating wind applications. Findings show that the quality of correlation decreased with higher nacelle excitations, which are caused by both wind and waves. Consequently, wind speed estimations were used for power curve measurement to investigate the qualification of using a nacelle-based lidar for such an application. Although lidar measurements showed higher scatter, they provided a reasonable wind speed estimate and a similar power curve to sonic measurements.
Study on the use of air quality sensors for environmental epidemiology
(2026) Chacón-Mateos, Miriam; Scheffknecht, Günter (Univ.-Prof. Dr. techn.)
In the last century, extensive evidence on the adverse health effects of air pollution has been documented. However, significant gaps remain in the literature, particularly regarding the effects of air pollution on vulnerable populations, such as individuals with chronic respiratory diseases. These groups are disproportionately affected by air pollution, even at pollutant concentrations below current regulatory standards. Furthermore, most epidemiological studies focus on high-income countries, leaving low- and middle-income countries (LMICs) underrepresented, partly due to limited air quality data, infrastructure, and financial resources. This imbalance highlights the need for cost-effective solutions to reduce environmental health inequalities.
Low-cost air quality sensors (AQSs) have emerged as a promising technology to address these gaps by expanding monitoring coverage and improving exposure assessment where conventional networks are lacking. However, their practical application encounters substantial challenges concerning data accuracy, environmental interferences, and sensor aging, which necessitate ongoing efforts to enhance calibration and data correction methods.
This study investigates the feasibility of using cost-effective AQSs in epidemiological research, with the focus on PM2.5 and NO2 measurements, two key urban air pollutants strongly linked to respiratory morbidity and mortality. For that purpose, stationary indoor and outdoor sensor systems were built and a quality-assurance methodology as well as different calibration techniques - including regression and machine learning models - were tested in a pilot study with patients suffering from chronic obstructive pulmonary disease (COPD) or asthma.
The measurements took place in the houses of seven volunteer participants in Stuttgart for 30 days. Low-cost thermal dryers were developed and evaluated to reduce the influence of hygroscopic growth and fog in outdoor PM2.5 sensors. The co-location of the sensors with reference-grade instruments took place two weeks prior deployment in indoor (laboratory) and outdoor (hotspot station) environments. Moreover, sensor performances were evaluated according to the EU Data Quality Objectives (DQOs) set in the EU Directives 2008/50/EC and 2024/2881 on ambient air quality and cleaner air for Europe. Sensor data validation during deployment was done via NO2 diffusion samplers, time-activity protocols and outdoor air quality data from official monitoring stations.
Results showed that after calibrations with univariate linear regression (ULR) and implementing a low-cost dryer for outdoor use, the PM2.5 sensors (Alphasense OPC-R1) can meet DQOs for indicative measurements at concentrations higher than 16 μg/m³, with substantial unit-to-unit variability, especially among sensors calibrated indoors. For NO2 measurements, the sensor NO2-B43F from Alphasense was used, and multiple linear regression (MLR) and machine learning (ML) algorithms, including random forest regressor (RFR), support vector regressor (SVR), and artificial neural networks (ANN), were evaluated as calibration models. In addition, different time aggregation intervals of the training data (1, 5, 10 and 15 minutes) were assessed. Results showed that ANN trained with 10 min averaging time provided the most robust performance during deployment.
The pilot study combined calibrated sensor data with patient health surveys, spirometry tests, and hourly activity logs to estimate inhalation rates and potential inhaled doses. Results underscored the predominance of indoor exposure (83% of total time) and demonstrated that using activity-adjusted inhalation rates and indoor AQS data to calculate the potential dose can reduce exposure , compared to the use of generic inhalation rate values and outdoor air quality data. Source apportionment and indoor/outdoor (I/O) ratio analyses revealed the influence of ventilation
behaviours and specific activities (e.g. cooking, candle burning) on indoor air quality.
Overall, affordable air quality sensor systems (AQSSs) can deliver valuable data for exposure studies, reduce misclassification risks inherent in relying solely on outdoor monitoring stations, and foster epidemiological research to combat health research disparities across the world.
Cost-based mooring designs and a parametric study of bridles for a 15 MW spar-type floating offshore wind turbine
(2022) Pan, Qi; Cheng, Po Wen
This paper highlights a cost-based design method for feasible and cost-effective mooring configurations. A parametric study of mooring designs considering bridle impact is performed for a 15 MW spar-type floating offshore wind turbine (FOWT). This site-specific FOWT is based on the preliminary model from EU funded H2020 Project COREWIND. Without running numerical simulations, the cost-based design method generates 1443 mooring configurations with normalized costs ranging from 1.00 to 1.667. Six configurations with the lowest normalized costs are selected for OpenFAST simulations. The ultimate test results verify the structural strength of mooring configurations. Bridle designs cause significant impact on the ultimate mooring tensions and floater motions in surge, sway and yaw, which is clearly influenced by the mass ratio of a bridle to a mooring line. When the mass ratio decreases from 13% to 5%, peak mooring tensions increase by up to 17%. The influence of bridle designs on mooring tension fatigue is less prominent and the effect of mass ratio on mooring fatigue varies with wind speed. Near the rated wind speed, deviations in mooring fatigue loads are within 5% for all six configurations. This cost-based method promotes feasible and economical mooring designs. The parametric study of bridle designs provides solid support for optimal mooring designs of spar-type FOWTs.
Efficient multibody modeling of offshore wind turbines with flexible substructures
(2022) Steinacker, Heiner; Lemmer, Frank; Raach, Steffen; Schlipf, David; Cheng, Po Wen
Offshore wind turbines, especially floating wind turbines, are often simulated assuming rigid substructures to obtain computationally efficient simulation models for preliminary parameter variation studies. This causes large errors in the determination of coupled natural frequencies and internal loads, particularly with increasing turbine sizes. Finite Element models for flexible substructures were developed by several researchers, often resulting in a high simulation effort. In this paper, a modally reduced Finite Element model, precomputed by the SubDyn module of OpenFAST, is directly included in the generalized Equation of Motion of the Simplified Low Order Wind turbine model SLOW. The approach was tested with the DTU10MW reference wind turbine mounted on a flexible monopile. It shows a high agreement with the former beam-based Multibody System in the calculated coupled natural frequencies and steady state results both for the linear and nonlinear model. A basis has been established to integrate flexible bodies of any shape even into computational efficient Multibody Systems of reduced order, such as SLOW, without coupling of two modules as in OpenFAST. This might improve numerical stability due to unified equations of motion.
A parametric study of the mooring system design parameters to reduce wake losses in a floating wind farm
(2022) Mahfouz, Mohammad Youssef; Hall, Matthew; Cheng, Po Wen
Wake effects inside a conventional fixed bottom wind farm decrease the power produced by the downwind turbines, hence decreasing the farm’s annual energy production (AEP). However, floating offshore wind turbines (FOWTs) have the ability to relocate their positions laterally through surge and sway motions. This flexibility provides a new degree of freedom (DOF) in the floating wind farm layout, which can be used to decrease the aerodynamic interactions inside the floating wind farm and hence decrease the wake losses. The lateral movement of FOWTs can be passively controlled by the mooring system design. The mooring system’s restoring characteristics allows the FOWT to only move within a specific area in the x-y plane known as the watch circle. Current state of the art mooring system designs are following oil and gas design basis where the floating platforms are not allowed to have large lateral displacements. In this work, we use full factorial design to analyse the effect of different mooring system design parameters on the ability of the floater to relocate its position. The analysis shows that each design parameter has a different way of affecting the FOWT’s response. The mooring lines’ headings control which wind directions cause the biggest displacements in the crosswind direction. The smaller the lines’ diameters the higher the displacements of the FOWT. Finally, the longer the line length the smaller the mooring system’s stiffness and hence the larger the FOWT’s displacement. The results of this study can be used as the basis for floating wind farm optimization, in which the wind turbines are allowed to passively relocate their positions according to the wind speed and wind direction.
Impact of turbulent inflow and orography on the low-frequency noise sources of a wind turbine
(2022) Wenz, Florian; Lutz, Thorsten; Krämer, Ewald
A high-fidelity process chain is used to numerically investigate the low-frequency emissions from a generic wind turbine under turbulent inflow conditions. It consists of the Computational Fluid Dynamics (CFD) solver FLOWer, which is coupled to the multi-body simulation software SIMPACK to take unsteady aeroelastic effects into account. A realistic flow field for the complex terrain of Perdigão, Portugal is obtained by including the orography and vegetation in the simulation and using a precursor simulation with E-Wind to generate a site- and situation-specific inflow. The acoustic emissions are calculated with the Fflowcs-Williams-Hawkings (FW-H) acoustic solver ACCO. The simulations show that the tower emits noise caused by the blade-tower interaction (BTI) equally strong in all directions. To the sides of the turbine, this contribution is dominant, regardless of the turbulent inflow. The blades, on the other hand, emit significantly more noise under turbulent inflow, especially in streamwise direction, where they become the dominant source. The main noise mechanism here is the low-frequency part of the inflow turbulence (IT), followed by the BTI. The flow field in Perdigão causes additional large-scale variations in IT noise over time.
Extending GPRat to support distributed computing
(2025) Niederhausen, Tim
Gaussian Process (GP) regression is a popular tool in various fields, especially in domains that require the confidence of resulting predictions, for example, system identification. Running exact GPs on larger datasets is often impractical due to its cubic runtime performance and quadratic memory use. Existing software in this area is often focused on providing approximate solutions for these big data problems. The GPRat project is an existing C++ implementation for exact GP regression that provides shared-memory parallelism using the popular HPX framework. However, for larger datasets, the memory available on typical desktop or server systems is not enough to fulfill the algorithm’s memory requirements.
In this work, we address these limitations by developing distributed approaches for all algorithms in GPRat. This includes hyperparameter optimization and the computation of predictions, either with uncertainty or with a full posterior covariance matrix. We also research and implement different strategies for distributing data and computation tasks over multiple nodes.
As part of this work, we also investigate the performance of these implementations in a multi-node cluster and a single-node context with multiple NUMA domains. Our results show that speedups can be achieved in the single-node context but that the high costs of data transmission between nodes significantly reduce the speedup provided by additional computing resources. The distributed approach successfully handles datasets that would be intractable for single nodes, but more work is necessary to reduce communication costs.
Biointelligent design of edible gellan- and plant protein-based hybrid prototypes for cultivated meat containing fat spheroids
Wollschlaeger, Jannis O.; Fahmy, Ahmed R.; Jekle, Mario; Heine, Simon; Kluger, Petra Juliane
With the growing global population, the demand for meat continues to rise, and alternative approaches to the conventional meat production, must be explored. Cultivated meat (CM), produced from animal cells outside the animal, offers a promising solution but remains in early development. Initial CM products will likely be hybrid constructs with a high proportion of plant-based biomaterials. This study evaluated five biomaterials for their suitability for a hybrid product manufactured via extrusion-based 3D printing. The polysaccharide gellan gum (GG) and two plant proteins (soy and pea) were investigated individually and as GG-protein blends. Their rheological behavior, storage stability, and frying performance were systematically analyzed. Pure plant protein biomaterials exhibited higher firmness and shape retention during frying, whereas GG-based biomaterials showed thermoreversible melting behavior upon heating. Coloring the protein-based biomaterials with red beet powder had no significant effect on their rheological properties or frying behavior. All GG-based inks, as well as the colored protein inks, demonstrated satisfactory printability using extrusion-based 3D printing. To exploit the melting properties of GG as a fat-mimicking design element, a CAD model was developed featuring a protein-based "muscle shell" with embedded "fat domains" composed of GG. These domains were further enriched with adipogenically differentiated bovine adipose-derived stem cells (bASC) spheroids. Finally, the texture of the CM-hybrid prototypes was compared to three commercially available meat alternatives, both before and after frying. While the prototypes exhibited a softer texture, their structural integrity during frying and modular design highlight their potential suitability in the rising field of CM-hybrid products.