Influence of the structural integrity management on the levelized cost of energy of offshore wind: a parametric sensitivity analysis

The levelized cost of energy (LCoE) is an important measure to quantify the macro-economic efficiency of an offshore wind farm and to enable a quantitative comparison with other types of energy production. The costs of the structural integrity management - which is required to ensure an adequate lifetime reliability of the turbine support structures - are part of the operational expenditures of an offshore wind farm. An optimization of the structural integrity management may reduce the operational expenditures and consequently the LCoE. However, the effect of the structural integrity management on the LCoE is hardly known. To investigate this effect, this paper presents a sensitivity analysis of the LCoE of a generic offshore wind farm. The probabilistic models of the parameters influencing the LCoE are based on a literature study including an explicit model for the structural integrity management. The analysis reveals that LCoE may potentially be reduced if an optimization of the structural integrity management enables a service life extension

Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients

BACKGROUND: Network inference of gene expression data is an important challenge in systems biology. Novel algorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases such as rheumatoid arthritis (RA), in which activated synovial fibroblasts (SFBs) play a major role. Since the detailed mechanisms underlying this activation are still unclear, simultaneous investigation of multi-stimuli activation of SFBs offers the possibility to elucidate the regulatory effects of multiple mediators and to gain new insights into disease pathogenesis. METHODS: A GRN was therefore inferred from RA-SFBs treated with 4 different stimuli (IL-1 β, TNF- α, TGF- β, and PDGF-D). Data from time series microarray experiments (0, 1, 2, 4, 12 h; Affymetrix HG-U133 Plus 2.0) were batch-corrected applying ‘ComBat’, analyzed for differentially expressed genes over time with ‘Limma’, and used for the inference of a robust GRN with NetGenerator V2.0, a heuristic ordinary differential equation-based method with soft integration of prior knowledge. RESULTS: Using all genes differentially expressed over time in RA-SFBs for any stimulus, and selecting the genes belonging to the most significant gene ontology (GO) term, i.e., ‘cartilage development’, a dynamic, robust, moderately complex multi-stimuli GRN was generated with 24 genes and 57 edges in total, 31 of which were gene-to-gene edges. Prior literature-based knowledge derived from Pathway Studio or manual searches was reflected in the final network by 25/57 confirmed edges (44%). The model contained known network motifs crucial for dynamic cellular behavior, e.g., cross-talk among pathways, positive feed-back loops, and positive feed-forward motifs (including suppression of the transcriptional repressor OSR2 by all 4 stimuli. CONCLUSION: A multi-stimuli GRN highly concordant with literature data was successfully generated by network inference from the gene expression of stimulated RA-SFBs. The GRN showed high reliability, since 10 predicted edges were independently validated by literature findings post network inference. The selected GO term ‘cartilage development’ contained a number of differentiation markers, growth factors, and transcription factors with potential relevance for RA. Finally, the model provided new insight into the response of RA-SFBs to multiple stimuli implicated in the pathogenesis of RA, in particular to the ‘novel’ potent growth factor PDGF-D

Global observations of aerosol-cloud-precipitation-climate interactions: Global observations of aerosol-cloud-precipitation-climateinteractions

Cloud drop condensation nuclei (CCN) and ice nuclei (IN) particles determine to a large extent cloud microstructure and, consequently, cloud albedo and the dynamic response of clouds to aerosol-induced changes to precipitation. This can modify the reflected solar radiation and the thermal radiation emitted to space. Measurements of tropospheric CCN and IN over large areas have not been possible and can be only roughly approximated from satellite-sensor-based estimates of optical properties of aerosols. Our lack of ability to measure both CCN and cloud updrafts precludes disentangling the effects ofmeteorology fromthose of aerosols and represents the largest component in our uncertainty in anthropogenic climate forcing.Ways to improve the retrieval accuracy include multiangle and multipolarimetric passive measurements of the optical signal and multispectral lidar polarimetric measurements. Indirect methods include proxies of trace gases, as retrieved by hyperspectral sensors. Perhaps the most promising emerging direction is retrieving the CCN properties by simultaneously retrieving convective cloud drop number concentrations and updraft speeds, which amounts to using clouds as natural CCN chambers. These satellite observations have to be constrained by in situ observations of aerosol-cloud-precipitation-climate (ACPC) interactions, which in turn constrain a hierarchy of model simulations of ACPC. Since the essence of a general circulation model is an accurate quantification of the energy and mass fluxes in all forms between the surface, atmosphere and outer space, a route to progress is proposed here in the form of a series of box flux closure experiments in the various climate regimes. A roadmap is provided for quantifying the ACPC interactions and thereby reducing the uncertainty in anthropogenic climate forcing

Fatigue Reliability Based on Predicted Posterior Stress Ranges Determined from Strain Measurements of Wind Turbine Support Structures

In the present paper, an approach for updating the continuous stress range distribution of a welded connection of a wind turbine support structure with predicted information from strain measurements is presented. Environmental conditions, such as wind or, in offshore fields, waves and currents, in combination with rotor excitations generate cyclic stresses affecting the reliability of welded joints of the support structure over the service life. Using strain measurements, these conditions can be monitored, and the resulting stress ranges, under consideration of measurement, mechanical and material uncertainties, can be reconstructed. These stress ranges can be used as an input for updating the prior probability density function (PDF) of the stress ranges predicted by the overall dynamics and a detailed design analysis. Applying Bayesian probability theory and decision theoretical implications, the predicted posterior probability density of the stress ranges is calculated based on the design information and uncertainties. This approach is exemplified, and it is shown how the predicted stress ranges and the design stress ranges are distributed. The prior and the predicted posterior stress ranges are used for a reliability calculation for potentially entering a pre-posterior decision analysis

Reconstructing Stress Resultants in Wind Turbine Towers Based on Strain Measurements

Support structures of offshore wind turbines are subject to cyclic stresses generated by different time-variant random loadings such as wind, waves, and currents in combination with the excitation by the rotor. In the design phase, the cyclic demand on wind turbine support structure is calculated and forecasted with semi or fully probabilistic engineering models. In some cases, additional cyclic stresses may be induced by construction deviations, unbalanced rotor masses and structural dynamic phenomena such as, for example, the Sommerfeld effect. Both, the significant uncertainties in the design and a validation of absence of unforeseen adverse dynamic phenomena necessitate the employment of measurement systems on the support structures. The quality of the measurements of the cyclic demand on the support structures depends on (a) the precision of the measurement system consisting of sensors, amplifier and data normalization and (b) algorithms for analyzing and converting data to structural health information. This paper presents the probabilistic modelling and analysis of uncertainties in strain measurements performed for the purposes of reconstructing stress resultants in wind turbine towers. It is shown how the uncertainties in the strain measurements affect the uncertainty in the individual components of the reconstructed forces and moments. The analysis identifies the components of the vector of stress resultants that can be reconstructed with sufficient precision

INFLUENCE OF THE STRUCTURAL INTEGRITY MANAGEMENT ON THE LEVELIZED COST OF ENERGY OF OFFSHORE WIND : A PARAMETRIC SENSITIVITY ANALYSIS

The levelized cost of energy (LCoE) is an important measure to quantify the macro-economic efficiency of an offshore wind farm and to enable a quantitative comparison with other types of energy production. The costs of the structural integrity management - which is required to ensure an adequate lifetime reliability of the turbine support structures - are part of the operational expenditures of an offshore wind farm. An optimization of the structural integrity management may reduce the operational expenditures and consequently the LCoE. However, the effect of the structural integrity management on the LCoE is hardly known. To investigate this effect, this paper presents a sensitivity analysis of the LCoE of a generic offshore wind farm. The probabilistic models of the parameters influencing the LCoE are based on a literature study including an explicit model for the structural integrity management. The analysis reveals that LCoE may potentially be reduced if an optimization of the structural integrity management enables a service life extension

Prevalence, serovars, phage types, and antibiotic susceptibilities of Salmonella strains isolated from animals in the United Arab Emirates from 1996 to 2009

The aim of this study was to give some insights into the prevalence, serovars, phage types, and antibiotic resistances of Salmonella from animal origin in the United Arab Emirates. Data on diagnostic samples from animals (n = 20,871) examined for Salmonella between 1996 and 2009 were extracted from the databases of the Central Veterinary Research Laboratory in Dubai and from typed strains (n = 1052) from the Robert Koch Institute, Wernigerode Branch in Germany and analyzed for general and animal-specific trends. Salmonella was isolated from 1,928 (9 %) of the 20,871 samples examined. Among the 1,052 typed strains, most were from camels (n = 232), falcons (n = 166), bustards (n = 101), antelopes (n = 66), and horses (n = 63). The predominant serovars were Salmonella Typhimurium (25 %), Salmonella Kentucky (8 %), followed by Salmonella Frintrop (7 %), and Salmonella Hindmarsh (5 %). When analyzed by animal species, the most frequent serovars in camels were Salmonella Frintrop (28 %) and Salmonella Hindmarsh (21 %), in falcons Salmonella Typhimurium (32 %), in bustards Salmonella Kentucky (19 %), in antelopes Salmonella Typhimurium (9 %), and in horses Salmonella Typhimurium (17 %) and S. Kentucky (16 %). Resistance of all typed Salmonella strains (n = 1052) was most often seen to tetracycline (23 %), streptomycin (22 %), nalidixic acid (18 %), and ampicillin (15 %). These data show trends in the epidemiology of Salmonella in different animal species which can be used as a base for future prevention, control, and therapy strategies

Location factors and ecosystem embedding of sustainability-engaged blockchain companies in the US. A web-based analysis

While many digital technologies provide opportunities for creating business models with an impact on sustainability, some technologies, especially blockchain applications, are criticized for harming the environment, e.g. due to high energy demand. In our study, we present a novel approach to identify sustainability-focused blockchain companies and relate their level of engagement to location factors and entrepreneurial ecosystem embeddedness. For this, we use a large-scale web scraping approach to analyze the textual content and hyperlink networks of all US companies from their websites. Our results show that blockchain remains a niche technology, with its use communicated by about 0.6% of US companies. However, the proportion of sustainable blockchain firms is significantly higher than in the overall firm population. Additionally, we find that blockchain companies with an intensified focus on sustainability have, at least quantitatively, a more intensive embedding in entrepreneurial ecosystems, while infrastructural and socio-economic location factors hardly play a role

Multibeam bathymetry raw data (Kongsberg EM 122 working area dataset) of RV MARIA S. MERIAN during cruise MSM82/2, South Atlantic Ocean

The multibeam echo sounder (MBES) data was collected from the 28.04.2019 (23:19 UTC) to the 07.05.2019 (16:42 UTC) with a Kongsberg EM122 during the transit from Montevideo (Uruguay) to Las Palmas (Canary Islands, Spain) in the Southern Atlantic Ocean. The deep-water MBES EM122 operates with an acoustic frequency of 12 kHz and a beam opening angle of 2° x 2° and up to 432 beams. The raw and unprocessed bathymetric and backscatter data is stored in Kongsberg format (*.all), each containing up to 30 min of data. The therein included time, motion and position data (WGS84, geographic) was measured by the Kongsberg Seapath system on board. During the acquisition the echo sounder was monitored as well as settings and filter adjusted according to the environment. The swath opening angle was set between 65° and 70°. As the data was collected during transit, no specific survey was performed. Sound velocity profiles (SVP) were measured on the 01.05.2019 (XSV probe) and 04.05.2019 (with CTD) and applied in the MBES acquisition software SIS. The SVP data is not part of this submission but can be extracted from the .all files. To improve the bathymetric data before the first SVP measurement, an synthetic SVP extracted from the World Ocean Atlas 2009 (Levitus, 2013) using the software Sound Speed Manager (developed by the UNH Center for Coastal and Ocean Mapping and NOAA Coast Survey Development Laboratory (CSDL))