dataesr/scientific-paragraphs-categorization huggingface dataset:
4. Finally, the switching on a bifurcated branch is performed using the mode injection method  (Seydel, 1994) : at the first step of a bifurcating branch, the eigenvector corresponding to the zero eigenvalue is computed and used as an alternative prediction. For more information, the whole program is described in full details in Le  Grognec and Le van (2008). Alternatively, finite element computations will also be performed using Abaqus software, for comparison purposes. Again, incremental calculations are needed so as to take into account the material nonlinearities, and use is also made of arc-length (Riks) methods in order to handle possible limit points. In addition, imperfections are introduced so as to trigger the buckling phenomenon and follow the post-critical equilibrium path. To ease implementation, a perturbation force technique is adopted rather than other alternatives including geometric imperfections.
In both cases, the analytical solutions are validated by comparison against numerical finite element results obtained with an in-house shell finite element program. This finite element code can handle plasticity and finite transformations, and it is able to compute the elastic/plastic bifurcation loads during an incremental procedure thanks to branch-switching methods. Additional computations are performed using a beam finite element model on Abaqus software, for completeness purposes. Analytical solutions are shown to be in very good agreement with numerical results, as long as the same modes are naturally considered (the geometric and material parameters have been retained so that the lateral-torsional buckling happens in the first instance, before any other buckling phenomenon). As a conclusion, analytical (and possibly closed-form) solutions for the critical bending moments of lateral-torsional buckling of elastoplastic beams are likely to be achieved and to give satisfactory results for any cross-sectional shape.
Coal Power Plant Materials and Life Assessment The three-level approach to RLA is also recognized and proposed as the best method for residual life estimation by Paddea et al. [5]. It comprises the use of three stages to assess the actual state of pressure equipment components: 1. Stage 1 is the calculation stage, based on the equipment history, taking into account operational pressure, temperature, incidental events, and the number of startups and shutdowns. It requires the compilation of relevant information, which can be obtained from technical drawings, original material list, material properties, database of operation conditions, and a detailed database of failures that occurred during the life of the equipment.
Data Collection The data to be collected at the start of the assessment are split into five parts  ( 1). Duration of service, number of start-ups and shut-downs, material specification pair records, information from temperature sensors, strain gauges, etc. , are some e ples of the information required for L1 assessment. Some of the information may not be available to perform the creep RLA calcul In this case, assumptions are made to assist in the L1 assessment. The mandatory mation required for L1 assessment, as well as the possible assumptions used for th culation, are summarized in Table  1. L1 approach comprises mainly three steps: 1. Data collection; 2. RLA calculations; 3. Inspection plan development.
4. Step 4-determine the wastage rate of the component The component is subjected not only to creep damage but also to wall thinning due to oxidation and external corrosion. The wastage rate data are important, as continual metal loss results in an ongoing stress increase due to reduction in wall thickness. This will have an impact on the overall residual creep life calculation. The internal metal loss rate can be determined using measurements of oxide thickness. The latter is related to the metal wastage via the Pilling-Bedworth Ratio (PBR). When an oxide is formed at the metal/oxide interface, the volume change due to the formation of the oxide can be expressed as PBR by Equation (3)  [23]. For the case of boiler tube, pipe and header, the main oxide formed is magnetite. The PBR for magnetite scale is 2.
Level 1 The preliminary assessment, based on the accessible information, was carried out in L1. For L1, only the inspection records for periods 1 and 2 are available as summarized in Table  2. Hence, the wastage rate, the hoop stress and the tube metal temperatures were Sensors 2023, 23, 2163 13 of 20 estimated according to the calculation approach, described in steps 1-4 in Section 3. 1. 2. , and the data for both periods. The results for periods 1 and 2 are summarized in Table  3. The time to rupture, the creep life fraction consumed, and the estimated residual life were calculated according to steps 4-8 from Section 3. 1. 2. The results are summarized in Table  4. All the calculations in steps 1-8 were performed using an internally developed Python-based code. Two methods for computation of average and minimum properties are used: LMP creep model from the standard API 530 and MHP creep model from the standard PD 6525 (please refer to Appendix B for more details)  [26, 28].
In particular, the axisymmetry of the incoming flow plays an important role in the redistribution of the turbulence levels in the turbine wake  (Chamorro and Porté-Agel, 2009). As explained in the two previous paragraphs, considering realistic conditions for the incoming flow and wave-induced motions is crucial to estimate the performance of floating wind turbines. It is also clear today that atmospheric turbulence cannot be treated as a purely random phenomenon characterized only by its intensity. To address this problem, the strategy commonly used in the wind energy community is based on the generation of synthetic turbulence with a target spectral content representative of the atmosphere (Kelley and  Jonkman, 2007; Mann, 1998). This approach, implemented in the TurbSim turbulence generator of the FAST aeroelastic code of the National Renewable Energy Laboratory (NREL), has been improved to account for particular phenomena such as low-level jets or Kelvin-Helmholtz instabilities that can develop in the atmosphere under certain conditions  (Kelley and Jonkman, 2007).
This database of temporally wellresolved vector fields generated in the cross-section of the flow are then used as unsteady inlet conditions for the load computation. After a description of the experimental setup (Sect. 2), details on the scaling between the model and a realscale prototype will be discussed (Sect. 3). The flow without a model and the mean wake are then presented (Sect. 4). The velocity fields are reconstructed at a high sampling frequency using stochastic estimation (Sect. 5) before being coupled to FAST simulations to evaluate the power produced by a downwind floating wind turbine submitted to the wake under surge motion (Sect. 6).
Transparent film was added at the walls to reduce the light diffusion. The time delay between the two pulses was dt PIV = 300 µs. Image analysis was performed using a standard cross-correlation multi-pass algorithm with a final interrogation window size of 32 × 32 px 2 using Dantec Dynamics software. The size of the final region of interest was about 3 D × 2 D. The sampling frequency was f PIV = 14. 1 Hz, and 14 000 snapshots were acquired for each case. The number of snapshots was doubled for one case to estimate the statistical convergence. Constant temperature anemometry (CTA) measurements were also performed simultaneously with the PIV. A total of 12 hot-wire (HWA) sensors were distributed spanwise downstream of PIV measurement plane and at two heights (0. 47 and 1. 25 D). The sampling frequency for the sensors was f HWA = 15 kHz.
(  2021 ): for a near-wake flow downstream of a nacelle and tower model, the cumulative eigenvalues reached about 40 % after N m = 15 modes, whereas it is about 50 % for the present study for the same number of modes. Spatial POD modes obtained from the database are first presented here. This corresponds to the analysis performed in  Raibaudo et al. (2022). In particular, maps of the spatial modes of the streamwise velocity component U for x/D = 8. 1 and profiles along the spanwise y direction and for two streamwise positions are presented in Fig. 6. Profiles are considered at the hub height (z/D = 0. 74) and for different frequencies of surge motion. Modes' signs are adapted for a better comparison. A more complete analysis of the POD modes for the floating wind turbine can be found in  Raibaudo et al.
Only the turbulent boundary layer and the far-wake for the fixed model are shown here, but the stochastic estimation was performed for the full database. By considering surge motion, as its impact on the mean quantities is limited, the reconstructed profiles are similar to those presented in Fig. 9b. For all of the cases, the reconstructed velocities are reduced in amplitude by the POD-LSE process, but their main properties are conserved. A spectral analysis of the reconstructed streamwise velocity is presented in Fig. 10. Premultiplied PSD values of the reconstructed velocity are compared with those estimated from the original PIV velocity, truncated with the same number of POD modes, and with a hot-wire sensor located at y/D = 0 and z/D = 1. 25 (no. 10 in Fig. 1 ). The spectrum of the reconstructed velocity is consistent at low frequency with the PIV and follows the dynamics behavior of the HWA sensor well.
All extrapolation and interpolation operations are performed using bivariate B-spline functions  (Dierckx, 1981). As a reference, generic turbulent velocity fields are also generated using TurbSim  (Kelley and Jonkman, 2007) , the turbulence generation tool provided with FAST. They correspond to velocity fields with prescribed boundary conditions and turbulence intensity, although without any coherence with respect to their dynamics. For the boundary layer, the power law exponent is α = 0. 15 and the surface roughness length is z r = 0. 01 m. Four turbulence intensity profiles are chosen for these fields: for the first three datasets, the turbulence intensities at the hub are set to 5 %, 7 % and 10 %. The last dataset follows the reference Normal Turbulence Model (NTM) in IEC 61400-1 requirements  (International Electrotechnical Commission, 2006).
Class C is chosen from this model, corresponding to the minimal turbulence class and a turbulence intensity of 12 % at the hub. The other parameters are the same as the fields from experiments (U hub,P = 10 m s -1 , N p,y = N p,z = 31). Simulations are performed using FAST, as implemented in the software QBlade (v0. 963), developed by the Technical University of Berlin  (Marten et al. , 2015). The tipspeed ratio (TSR) is fixed at 6. 6, under the optimal value of 8 for this wind turbine. The full-scale sampling frequency https://doi. org/10. 5194/wes-8-1711-2023 Wind Energ. Sci. , 8, 1711-1725, 2023  for the simulations and the aerodynamic model is F s,FAST = 100 Hz. The simulation time is T s,FAST = 6400 s (larger than the requirements of 10 min) to ensure statistical convergence. Global and local variables, such as thrust, torque or strain gauges on the blades can be obtained.
The RMS power obtained with experimental fields also matches the simulations with TurbSim datasets with similar turbulence levels. It should be noticed that the produced power is lower than 5 MW due to the choice of the TSR. In a second step, the ability of the present method to account for flow configurations more complex than the atmospheric boundary layer (ABL) alone is demonstrated by conducting FAST calculations of the performance of a wind turbine located in the wake of a first FOWT that is itself subjected to a surge motion. Results are shown in Fig. 12. For a fixed upstream FOWT (F + m = 0. 0), the power produced for the second turbine decreased by 87 % compared with the unperturbed turbine. This difference is consistent with the power loss found in previous studies for an array of two turbines or inside farms  (Ceccotti et al.
The attention of the reader is drawn to the fact that, in this section, the FAST calculations are performed over a single full-scale period of 1 h, which is not long enough to obtain statistically significant results nor draw any definite conclusion on the performance of a wind turbine subjected to the wake of FOWT and the impact of the surge motion of the latter. To demonstrate this and the influence of the choice of the averaging period compared with the typical timescales of both the ABL and the FOWT wake, statistics of the available power (AP) directly computed from the PIV velocity fields are shown in Fig. 13. Figure  13a  shows the distribution of the instantaneous AP estimated over the entire inflow database. Besides the fact that the range of variations is quite large, there is a clear trend in the median AP, with an increase with surge frequencies from 0 to F + m = 0.
Conclusions A new method for generating inflow conditions for load simulations for wind turbines in complex configurations has https://doi. org/10. 5194/wes-8-1711-2023 Wind Energ. Sci. , 8, 1711-1725, 2023 been presented. It relies on interfacing the simulation code (FAST in the present case) with stereoscopic PIV wind tunnel measurements performed in a cross-section of the flow. This new approach has been tested in various flow configurations, including an unperturbed ABL flow and the wake of an upstream wind turbine (modeled by a porous disk in the wind tunnel) submitted to a surge motion, replicating the wave-induced movement of the FOWT platform. The incoming motion was imposed with a fixed amplitude at four different frequencies. The boundary layer, turbulence levels and large coherent structures of the turbulent flow upstream of the model are taken into account in this study and match those of a realistic atmospheric flow.
Moments maps were constructed using the spectral-cube software 1 and the standard procedure as described in  Ponomareva et al. (2021). We created a cubelet from the MIGHTEE H field centred on NGC 895. The cubelet was then smoothed to a circular beam of 20 and clipped at the 3 level. The resulting mask was then applied 1 https://spectral-cube. readthedocs. io/en/latest/ to the original resolution cubelet to account for the low column density extended H emission. Lastly, the remaining noise pixels were masked out by hand.
Optical data We use the deepest optical dataset that overlaps with MeerKAT images in this region of the sky, from the Hyper Suprime-Cam Subaru Strategic Program Survey (HSC-SSP)  Aihara et al. (2018). This survey makes use of the Subaru 8. 2 m telescope providing images with sub-arcsec resolution in the broad bands g, r, i, z and y. We downloaded mosaics for each of the bands, including a wide enough field around NGC 895 using the hscmap browser hosted on the survey web page: https://hsc-release. mtk. nao. ac. jp/doc/. We used the second data release of the survey  (Aihara et al. 2019). We calculated the limiting surface brightness of these mosaics following the technique applied by  Román et al. (2020) , their appendix A. The 3 values in 10 × 10 boxes are 30. 2, 29. 6, 29. 4, 28. 7 and 27.
At this level, the galaxy has a diameter of 115 kpc at our adopted distance of 34. 3 ± 1. 1 Mpc. This is ∼ 2. 7 times the optical diameter of 43 kpc derived from the g-band color image. The H gas distribution, as seen in Fig. 1  reveals an H -poor central region. Figure  2  shows the global H profile of NGC 895. It has been derived from the primary-beam corrected cubes. We compare the global profile of NGC 895 as obtained from MeerKAT, GBT and HIPASS observations. It is clear from Fig. 2  and Fig. 3  that NGC 895 is asymmetric, with more H on the receding than on the approaching side. The H line flux derived from the global profile of NGC 895 as measured from the MeerKAT data is 52. 1 ± 0. 3 Jy. km s -1. This is in agreement with the GBT and HIPASS fluxes of 50. 51 Jy. km s -1  (Springob et al.
H Kinematics The velocity field map of NGC 895 is shown in Fig. 3. The map shows a different curvature of the iso-velocity contours on the approaching and receding side, which suggests the presence of kinematic asymmetry  (Khademi et al. 2021). We use an extended version of the tilted-ring model by  Rogstad et al. (1974)  implemented in the TIlted RIng FIitting Code TiRiFiC  (Józsa et al. 2007 ) to model the kinematics and the gas distribution in NGC 895. TiRiFiC uses a  2 minimization technique to fit a tilted-ring model directly to a data cube instead of a velocity field. This makes TiRiFiC less prone to beam smearing effects and represents more accurately complex kinematical features, such as a warp, compared to the classic method of fitting to a velocity field. TiRiFiC models a rotating gas disc as a set of concentric annuli, each described by the user-supplied input parameters such as the central coordinates (XPOS, YPOS), the rotation curve (VROT), the position angle (PA), the inclination (INCL), the thickness of the disk (Z0), and potentially many more.
To make a reasonable initial guess for the TiRiFiC model parameters, we use the Fully Automated TiRiFiC (FAT) software  Kamphuis et al. (2015) , which is a GDL wrapper around TiRiFiC. We used the beta version of FAT, which allows for radial variation of the intrinsic velocity dispersion. FAT takes an H data cube as input and automatically estimates the following free parameters; 1) surface brightness, 2) position angle, 3) inclination 4) rotational velocity, 5) systemic velocity, 6) scale height 7), intrinsic dispersion and 8) central coordinates. FAT fits each parameter ring by ring and finally smooths the parameters with a polynomial of order 0, 1, 2, 3, 4 or 5. FAT models the H disc as two halves and fits rings across each half. The procedure employed by FAT for finding the initial estimates of the free parameters and the flow-chart for fitting is discussed in detail in  Kamphuis et al.
(2015). The results from FAT were then inputted to TiRiFiC, allowing us to adjust the initial estimates manually and, if necessary, add parameters not implemented by FAT. Unlike TiRiFiC, FAT is fully automated and no parameter tweaks are required to optimize the results. The output from FAT is a very good starting point for TiRiFiC. TiRiFiC is only necessary if we want to fit non-axisymmetric features not captured by FAT, or if we want to fit the galaxies as one disk (i. e. , not as a receding and approaching halves). The stopping criteria for TiRiFiC are reached by visual inspection of various plots of the model and the observed data cube (Position-velocity diagrams, residual velocity field, moment maps, and channel maps). Based on this, we judge whether certain model features are required to better reproduce the observations or not.
g. channel 2146 km s -1 ) clearly pointing North, in the direction of the companion J022138. This suggests the present gravitational interaction between those two objects. As we will see in the next section, this is also the companion showing an excess of light in the outer parts, another indication of possible interaction in the past. Fig. 4 (right)  shows TiRiFiC results for the inclination, position angle and rotation velocity of NGC 895. At a radius of ∼ 16 kpc, we see a major shift in the position angle, which is interpreted as being due to a warp. Figure  5  shows the position-velocity diagram of NGC 895 along the major axis and minor axis, with the data (brown) overlaid on the model (blue). We see that the model is consistent with the data. From the position-velocity diagram, we see no traces of extraplanar gas, which suggest that the arms we see in NGC 895 are asymmetric features induced by an interaction.
Star formation & stellar mass estimates The star formation and stellar mass of NGC 895 were obtained by a multi-wavelength SED fitting analysis exploiting the spectral energy distribution (SED) fitting code  CIGALE (v. 2022. 1. Boquien et al. 2019 , as applied to multi-wavelength photometry by  Małek et al. 2018). In order to perform the fit, it is important to have access to the best multi-wavelength compilation of aperture-matched photometry. For this reason, we used the multi-wavelength matched photometry available from DustPedia  (Bianchi et al. 2018 ) combined with our MeerKAT measurements and VLA radio continuum flux measurements. The best fit model is shown in Fig. 6. This was obtained by assuming the following models and parameters. We used a Chabrier initial mass function (IMF) with delayed star formation history and nebular emission (we also tried fitting the SED with an additional star formation burst, but our results did not change, so an additional burst is not needed to explain the results); we used the standard attenuation law presented in Charlot &  Fall (2000)  with the interstellar medium (ISM) and birth cloud slope of the attenuation equal to -0.
7. We used the modified dust template by  Draine & Li (2007). CIGALE allows performing the fit by exploiting different SED templates and emission models, e. g. AGN torus emission. We performed a run with the AGN module, but the estimated AGN fraction, defined as a ratio of the AGN luminosity to the sum of the AGN and dust luminosities, was lower than 0. 3% (0. 24±0. 42). The probability distribution function characterized by a single peak around the AGN fraction equal to 0. It means that even with an extensive range of possibilities for the AGN module, CIGALE did not find even one solution with a significant AGN contribution larger than 0. 5%. In the broadband spectral energy distribution fitting, the AGN fraction lower than 15% is usually too uncertain to be considered a real AGN contribution.
2023), was determined using the new scaling relations between WISE mid-infrared galaxy photometry and well determined stellar masses from SED modeling optical-infrared photometry provided by the Galaxy And Mass Assembly (GAMA) Survey 2 data release 4 (DR4 Catalogue of the GAMA-KiDS-VIKING survey of the southern G23 field  (Driver et al. 2022) ). Fig. 7  shows the relationship between the star formation rate and the stellar mass for galaxies obtained by  Jarrett et al. (2023). The grey line shows the galaxy sample from the 2MASS redshift survey (2MRS,  Huchra et al. 2012 ) and the Spitzer Survey of Stellar Structure in Galaxies (S4G,  Meidt et al. 2014). The black circle indicates the location of NGC 895 with respect to the main sequence. NGC 895 lies in the middle of the main sequence with no excess star fomation.
Optical properties We analyse the deep optical images of the H -detected satellites discussed previously: J022138 and J022042. Photometry in all the available optical bands, and structural properties when possible, are provided in Table  3. First, we analyse J022042. In Fig. 8  we show an image with the optical data available in the HSC-SSP dataset. The chance presence of an adjacent star is problematic for the analysis of the lower surface brightness regions in J022042 and problematic in general for a photometric analysis. To obtain cleaner photometry, we use DECALS. 78, 1. 17, 1. 56, 1. 95, 2. 34, 4. 69 ) mJy/beam. Right: TiRiFiC best-fit solutions for the inclination (top panel), the position angle (middle panel) and the rotation curve (bottom panel). data in which stars are modelled and subtracted from the images, allowing the effect of the adjacent star to be significantly reduced.
(2019)  but are only available for J022138 as J022042 could not be reliably measured in WISE images. We complemented our optical photometric measurements against the photometry provided by the Herschel Extragalactic Legacy Project  (Vaccari 2016; Shirley et al. 2019 Shirley et al. , 2021 , HELP) DR1 Merged Catalogue 3  and found that the two photometric datasets were in good agreement for the bands we could measure ourselves, with HELP providing a few extra data points. The best fit model for J022138 is shown in Fig. 10. We measure the star formation rate of 0. 004 ± 0. 001 M /yr for J022138. This value is comparable to those of isolated dwarf irregular galaxies with typical star formation rates, such as WLM (0. 006 M /yr), IC 1574 (0. 005 M /yr), and AndIV (0. 003 M /yr)  (Lee et al. 2009).