Fog conditions at the offshore wind farm Horns Rev 2 were photographed on 16 April 2018. In this study, we present the results of an analysis of the meteorological conditions on the day of the photographs. The aim of the study was to examine satellite images, meteorological observations, wind turbine data, lidar data, reanalysis data, and wake and blockage model results to assess whether wind farm blockage was a likely cause for the formation of fog upstream of the wind farm. The analysis indicated the advection of warm and moist air mass from the southwest over a cool ocean, causing cold sea fog. Wind speeds at hub height were slightly above cut-in, and there was a strong veer in the shallow stable boundary layer. The most important finding is that the wake and blockage model indicated stagnant air mass arcs to the south and west of the wind farm. In the photographs, sea fog is visible in approximately the same area. Therefore, it is likely that the reduced wind triggered the sea fog condensation due to blockage in this area. A discrepancy between the blockage model and sea fog in the photographs appears in the southwest direction. Slightly higher winds might have occurred locally in a southwesterly direction, which may have dissolved sea fog. The wake model predicted long and narrow wind turbine wakes similar to those observed in the photographs. The novelty of the study is new evidence of wind farm blockage. It fills the gap in knowledge about flow in wind farms. Implications for future research include advanced modeling of flow phenomena near large offshore wind farms relevant to wind farm operators.

Small voids in the absorber layer of thin-film solar cells are generally suspected to impair photovoltaic performance. They have been studied on Cu(In,Ga)Se2 cells with conventional laboratory techniques, albeit limited to surface characterization and often affected by sample-preparation artifacts. Here, synchrotron imaging is performed on a fully operational as-deposited solar cell containing a few tens of voids. By measuring operando current and X-ray excited optical luminescence, the local electrical and optical performance in the proximity of the voids are estimated, and via ptychographic tomography, the depth in the absorber of the voids is quantified. Besides, the complex network of material-deficit structures between the absorber and the top electrode is highlighted. Despite certain local impairments, the massive presence of voids in the absorber suggests they only have a limited detrimental impact on performance.

Brain organoid technology has transformed both basic and applied biomedical research and paved the way for novel insights into developmental processes and disease states of the human brain. While the use of brain organoids has been rapidly growing in the past decade, the accompanying bioengineering and biofabrication solutions have remained scarce. As a result, most brain organoid protocols still rely on commercially available tools and culturing platforms that had previously been established for different purposes, thus entailing suboptimal culturing conditions and excessive use of plasticware. To address these issues, we developed a 3D printing pipeline for the fabrication of tailor-made culturing platforms for fluidically connected but spatially separated brain organoid array culture. This all-in-one platform allows all culturing steps—from cellular aggregation, spheroid growth, hydrogel embedding, and organoid maturation—to be performed in a single well plate without the need for organoid manipulation or transfer. Importantly, the approach relies on accessible materials and widely available 3D printing equipment. Furthermore, the developed design principles are modular and highly customizable. As such, we believe that the presented technology can be easily adapted by other research groups and fuel further development of culturing tools and platforms for brain organoids and other 3D cellular systems.

- In recent years, the aviation industry has witnessed a substantial integration of power electronics technology within Aircraft Microgrids (AMs). Consequently, the extension of electrical wiring networks has expanded, resulting in heightened intricacies within these systems. Therefore, the identification and location of faults in wiring networks have become an important topic in AMs to guarantee the safety of the electrical power systems. Time Domain Reflectometry (TDR) is widely used to locate and recognize electric wire faults. However, soft fault location on complex electrical networks using TDR is perplexing due to its weak effect on the reflected signal. Moreover, the existence of noise in the environment can worsen the TDR's performance. In this paper, a new approach based on TDR, along with the Subtractive Correlation Method (SCM) and Neural Network (NN), is proposed. The TDR response of the complex wiring network is determined using a newly proposed model based on the Finite Difference Time Domain (FDTD) method. The validity of the proposed model is established through experimentation including two distinct cable types also the introduced model notably enhances computational efficiency, a fact substantiated by our experimental findings and an extensive benchmarking against recent publications. These evaluations collectively underscore a significant reduction in computational time. Then, the reflected signal undergoes processing through SCM, a technique employed to amplify the subtle influence of the soft fault in two scenarios: one accompanied by noise and the other noise-free. Furthermore, NN is used to handle the inverse problem of localizing and characterizing the soft faults by their exact resistance values. Even within noisy environments, the proposed methodology excels in accurately locating and characterizing soft faults with a high degree of precision, all in real-time diagnostic scenarios.

This paper presents the variation of identified modal parameters of an offshore wind turbine (OWT) over one year of continuous monitoring. The considered OWT is a Haliade 150–6 MW GE turbine on a jacket substructure at the Block Island Wind Farm in Rhode Island, U.S. The turbine was instrumented with a monitoring system including accelerometers, strain gauges, inclinometers, and a data acquisition system with automatic data transfer to cloud storage. Continuous vibration measurements of the OWT have been collected over one year from April 2021 to April 2022. An automated output-only operational modal identification approach is developed to extract modal parameters of the OWT over this one-year period using windows of 10-min data. The identified modal parameters of the first few vibration modes show variability and correlation with several ambient and operational conditions. It is observed that the identified natural frequencies and damping ratios of the first side-to-side (SS) mode are stable over time and have very small variation, but those of the first fore-aft (FA) mode show significant variability. The second FA/SS modes can only be identified with reasonable accuracy when the turbine is in idle condition. Similar to the first FA/SS modes, the second FA mode has larger variability in frequencies and damping ratios than the second SS mode because of the aerodynamics effects. The system identification results over one full year verify the effectiveness and robustness of the proposed approach for long-term monitoring of this OWT and serve as the reference database.

Recently, microneedle-based sensors have been introduced as novel strategy for in situ monitoring of biomarkers in the skin. Here, in-plane silicon microneedles with different dimensions and shapes are fabricated and their ability to penetrate skin is evaluated. Arrays with flat, triangular, hypodermic, lancet and pencil-shaped microneedles, with lengths of 500–1000 μm, widths of 200–400 μm and thickness of 180–500 μm are considered. Fracture force is higher than 20 N for all microneedle arrays (MNA) confirming a high mechanical stability of the microneedles. Penetration force in skin-simulating hydrogels, excised rat abdominal skin and porcine ear skin is at least five times lower than the fracture force for all MNA designs. The lowest force for skin penetration is required for triangular microneedles with a low width and thickness. Skin tissue staining and histological analysis of rat abdominal skin and porcine ear skin confirm successful penetration of the epidermis for all MNA designs. However, the penetration depth is between 100 and 300 μm, which is considerably lower than the microneedle length. Tissue damage estimated by visual analysis of the penetration hole is smallest for triangular microneedles. Penetration ability and tissue damage are compared to the skin prick test (SPT) needle applied in allergy testing.

Offshore wind energy is playing an increasingly vital role in the clean energy transition around the world, and improved reliability of wind turbine structures is necessary for the long-term success and efficiency of renewable energy. Increased reliability would reduce costs associated with maintenance due to breakages and in turn reduce the levelized cost of energy for offshore wind energy sources. Structural health monitoring methods can be used to predict breakages and extend lifetimes by continuously monitoring instrumented structures. This paper presents system identification and model updating of a 6 MW offshore wind turbine using vibration measurements under varying operational conditions. The turbine is monopile-supported and instrumented with strain gauges and accelerometers at several elevations along the tower and monopile. Effective stiffness of soil springs in the model are updated to match modal-predicted natural frequencies and mode shapes of the first two modes with those identified from measurements at different operating conditions. A deterministic and probabilistic (Bayesian) approach to model updating are compared. The sensitivity of identified modal parameters and the updated model parameters are investigated with respect to operational and environmental conditions such as wind speed. Results show that deterministic model updating can match modal parameters with high accuracy across datasets and environmental conditions. Bayesian model updating results successfully estimate the posterior distribution of updating model parameters with an increasing degree of certainty as more data is used.

This talk discusses sensing methods and reconstruction techniques for characterizing sound fields over large spatial domains. We focus specifically on reconstructing reverberant soundfields in rooms and enclosures. Two complementary approaches are presented in the talk: on the one hand, we present a method that leverages on modeling the spatio-temporal and statistical properties of enclosed sound fields via wave expansions, enabling to estimate the sound field over a large volume of space. On the other hand, we present a Physics Informed Neural Network, which learns the governing wave equation using a small set of measured data and enables to meaningfully interpolate and extrapolate the sound field to any position where no observations are available. Experiments in real rooms are presented, including an opera hall and an auditoria for oral communication. The experiments successfully demonstrate the volumetric acquisition and three-dimensional characterization of the sound fields. The presented techniques can be of relevance in applications pertaining to immersive and navigable audio, architectural acoustics and heritage preservation.

This work introduces a method that employs physics-informed neural networks to reconstruct sound fields in diverse rooms, including both typical acoustically damped meeting rooms and more spaces of cultural significance, such as concert halls or theatres. The neural network is trained using a limited set of room impulse responses, integrating the expressive capacity of neural networks with the fundamental physics of sound propagation governed by the wave equation. Consequently, the network accurately represents sound fields within an aperture without requiring extensive measurements, regardless of the complexity of the sound field. Notably, our approach extends beyond sound pressure estimation and includes valuable vectorial quantities, such as particle velocity and intensity, resembling classical holography methods. Experimental results confirm the efficacy of the proposed approach, underscoring its reconstruction accuracy and computational efficiency. Moreover, by enabling the acquisition of sound field quantities in the time domain, which were previously challenging to obtain from measurements, our method opens up new frontiers for the analysis and comprehension of sound propagation phenomena in rooms.