dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, a numerical investigation using Ansys Fluent software is performed to study the flow pattern, the indoor thermal comfort, and the air freshness generated in a full-scale ventilated office room occupied by a sitting occupant. The performance of a multi-cone ceiling diffuser equipped with lobed inserts is assessed and compared with a traditional multi-cone ceiling diffuser. The indoor thermal comfort is evaluated using the predicted percentage of dissatisfaction (PPD) and the draft rate (DR) indicators. To assess the air freshness inside the office room, the mean age of air (MAA) is evaluated. After validating the simulation, the effect of the supply flow rate on the airflow pattern, the thermal comfort, and the air freshness, are investigated. Velocity contours show that recirculation zones in the domain result from the interaction between the supplied air and the thermal plume above the occupant's head.
Finally, no obstacles, such as furniture, were introduced inside the CFD office room. All geometries were built using SolidWorks software. Creating a high-quality mesh is one of the most critical factors that must be considered to ensure simulation accuracy. In the present work, the two main difficulties during the meshing step were (1) the complex geometry of the diffuser, which becomes more complex when adding lobed inserts, (2) the considerable difference between the diffuser and the office dimensions, and (3) the presence of the occupant in the domain. These may lead to a large mesh aspect ratio and/or skewness in the region near the diffuser, which causes convergence problems and affects the results' accuracy. To avoid these problems; first, the diffuser and the zone around the occupant were meshed using tetrahedral cells, whereas the duct and the office were meshed using cartesian hexahedra cells (see Fig.
3 ). Second, two transition zones with tetrahedral cells were created between the duct and the diffuser, and between the diffuser and the room. The cell sizes in the vicinity of the solid surfaces (walls, occupant) as well as the supply and the exhaust were refined using the inflation option with y + < 1. The commercial CFD software Ansys Fluent was used to study and simulate the airflow characteristics, temperature, and MAA distribution patterns inside the office room. The airflow used in the simulation is considered as steady, incompressible, and turbulent. Beside the averaged Navier-Stokes equations, the energy equation was also solved to calculate air temperature fields inside the office room. The Simple algorithm was used for coupling pressure and velocity. The second-order upwind differencing scheme was chosen as the discretization scheme for the convection terms of each governing equation.
Conclusions In the present work, numerical simulations were conducted using Ansys Fluent software to study the thermal comfort and air freshness in an office room. Two cases were considered: a first case with a ceiling round diffuser (assigned as CD), and a second case with the same diffuser equipped with lobed inserts (CDLI case). The simulations were performed with several supply air rates. A userdefined scalar and user-defined function were used to solve the mean age of air (MAA), and thermal comfort indices (PPD, DR) equations, respectively. A good agreement was found between the CFD results and the experimental data. The analysis shows that the flow exits from the diffuser as a jet attached to the ceiling and slides along the latter. This jet is 1. 25 times larger in the CDLI case compared to the CD case.
The second phase of the ESRF upgrade program did not only provide a new storage ring (Extremely Brilliant Source, EBS) but also allowed several beamlines to be refurbished. The BioSAXS beamline (located on port BM29) was upgraded with a new wiggler source and a larger detector. All analysis software has been rewritten to cope with the increased data flux and continues to provide beamline users with reduced and pre-processed data in real time. This article describes FreeSAS, an open-source collection of various small-angle scattering analysis algorithms needed to reduce and analyze BioSAXS data, and Dahu, the tool used to interface data analysis with beamline control. It further presents the data-processing pipelines for the different data acquisitions modes of the beamline, using either a sample changer for individual homogeneous samples or an inline size-exclusion chromatography setup.
Introduction Small-angle scattering (SAS) provides information on the shape of macromolecules on the nanometre scale and is particularly suited for biological samples thanks to a large range of suitable buffer conditions. Unlike single-crystal diffraction or nuclear magnetic resonance (NMR) which offer atomic resolution, BioSAS provides only information on the envelope of macromolecules: it can be used to validate the relative position of large structures in the assembly of biological complexes  (Schroer & Svergun, 2018). Structural biologists perform small-angle scattering (SAXS) experiments to validate the size and the shape of their protein or complex under study. Since most beamline users are biologists, they are neither synchrotron nor SAXS specialists. A full analysis of their data is thus of crucial importance to them  (Basu et al.
, 2019). Whereas most BioSAXS beamlines throughout the world have focused on the graphical user interface for helping their users with data analysis  (Basham et al. , 2015; Classen et al. , 2010) , the BM29 beamline from the ESRF  (Pernot et al. , 2013)  has focused on fully automated data analysis which provides users not only with the reduced curves and pre-analyzed data but also with all metadata and parameters needed to reprocess their data and get them published according to the relevant guidelines  (Trewhella et al. , 2017). BM29 had an automated pipeline for data analysis which was based on EDNA  (Incardona et al. , 2009)  and used the ATSAS2  (Petoukhov et al. , 2012)  software underneath. While the outcome of these processings was very appreciated by beamline users, the system was already close to the maximal throughput possible in terms of performances.
The new EBS source (Extremely Brilliant Source;  Chaize et al. , 2018)  of the ESRF not only provides a higher brilliance but also new wiggler sources for the former bending-magnet beamlines which triggered the re-build and the upgrade of most of them. The BM29 BioSAXS beamline was rebuilt in 2019 and now features a two-pole wiggler source and a new Pilatus3 2M detector designed to be mounted in vacuum  (previously 1M). This has lead to a substantial increase in data rate, mainly due to the larger detector. This paper is divided into two main parts and starts with a presentation of the tools developed for online data analysis of SAXS data -FreeSAS and Dahu, the job scheduler. Section 3 presents the three different pipelines used at the beamline: the common pipeline for the reduction of scattering images, the pipeline used with the sample changer, and the one used with the size-exclusion chromatography setup.
Pipelines  (Kieffer, 2020 (Kieffer, -2022) )  are heavily optimized for running on the beamline hardware and thus cannot easily be reused despite their MIT licence. Finally, this code is interfaced to the control software, BLISS  (Guijarro et al. , 2020 ), via TANGO (Go ¨tz et al. , 2003)  and uses a simple task scheduler, Dahu, already used at the TRUSAXS beamline  (Narayanan et al. , 2022).
Small-angle scattering analysis tools, FreeSAS FreeSAS is a Python library (van  Rossum, 1989 ) containing SAS analysis tools available both via a Python API and from the command line interface. It does not claim to be as complete as the ATSAS counterpart  (Manalastas-Cantos et al. , 2021) , but is free, released under the liberal MIT licence (i. e. it can be included into commercial products), all source code is available publicly on github  (Kieffer et al. , 2021) , and it is open to external contributions. Table  1  summarizes this comparison. Despite Python being an interpreted language, FreeSAS is performance-oriented and most of the processing is performed via Cython  (Behnel et al. , 2011)  extensions, written in C and compiled, to obtain the required performances. FreeSAS has been made available and packaged independently from Dahu (the online analysis tools) and from the processing pipelines so that scientists can reprocess their data and compare their results with those of other analysis software like ScA ˚tter  (Rambo, 2017).
The current release, FreeSAS 0. 9, supports Python 3. 6 to 3. 9. 2. 1. 1. SAS plotting. The FreeSAS command line tool (provided by the FreeSAS suite), Fig. 1 , provides a way to plot a semi-logarithmic representation of the SAS curve: I = f(q), where q = 4 sin()/ is the amplitude of the scattering vector, is half the scattering angles, the wavelength and I the recorded intensity at a given q, alongside some basic analyses which are described in the next sections. The program takes as entry a three-column ASCII file containing q, I and. 2. 1. 2. Guinier-region fitting. The first analysis performed on BioSAS data is to determine the radius of gyration, R g , of the solvated macromolecule and the forward-scattering intensity, I 0  (Guinier & Fournet, 1955). Based on the Taylor expansion of the scattering curve at q = 0, R g is obtained from the the linear regression of log[I(q)] = log(I 0 ) À 1/3(qR g ) 2 on the proper q-range, at small angles, called the Guinier region.
, 2017). The implementation in FreeSAS, called free bift, is based on Cython  (Behnel et al. , 2011)  and uses BLAS  (Blackford et al. , 2002)  and multi-threading for performances. Despite their different approaches, free bift provides similar results to those of DATGNOM  (Petoukhov et al. , 2007)  from ATSAS which uses a Tikhonov regularization. 2. 1. 4. Other tools available in FreeSAS. (i) cormapy evaluates radiation damage by comparing couples of frames using the correlation-map (CorMap) algorithm implemented from the publication by  Franke et al. (2015). (ii) subpycomp rotates and flips bead models to overlay them prior to merging them  (Brennich et al. , 2016). (iii) extract ascii generates three-column [q, I avg and (I)] text files (with headers) for compatibility with thirdparty tools.
Following ESRF's data policy  (Dimper et al. , 2019) , the default file format used at the BioSAXS beamline has changed to the hierarchical data format, HDF5  (The HDF Group, 2000 -2021) , for acquisition, analysis and archival of data. This is part of the FAIR principle  (Wilkinson et al. , 2016)  where the data portal  (ESRF, 2011 (ESRF, -2022) )  provides Findability, HDF5 provides Accessibility, extract ascii provides Interoperability, hoping this data is Reused after the embargo period. Table  1  provides some equivalence for program names provided by FreeSAS and ATSAS packages. The latter toolbox is built on top of several decades of experience acquired by the EMBL-Hamburg group and features some 60 programs to perform BioSAS data analysis. FreeSAS cannot compete on the number of features but tries to offer a clean Python interface to build pipelines in an efficient manner and fully open (both for usage and modification).
A dimensionless Kratky plot is presented in the lower-left plot. beamline at the ESRF  (Brennich et al. , 2016). The parallelization model used in EDNA is based on Python threads and forking processes which was wasting resources in serializing and inter-process communication. The Dahu job manager was designed for the low latency constraints of the TRUSAXS beamline  (Narayanan et al. , 2022) , where some jobs need to be processed within a dozen of milliseconds. Batch queuing systems, like SLURM  (Yoo et al. , 2003) , are very efficient at distributing heavy jobs, but none was optimized for reduced scheduling time (or low latency). The Tango interface  (Go ¨tz et al. , 2003)  implemented in Dahu was kept similar to the one in EDNA in order to ease the transition. The scheduling of jobs is performed via a shared queue and only a few workers are running simultaneously in different threads.
Thus the code runs actually in parallel only in sections where the Global Interpreter Lock from Python (GIL) is released, like in Cython extensions  (Behnel et al. , 2011)  from FreeSAS or in the OpenCL code from pyFAI  (Kieffer & Ashiotis, 2014). 2. 2. 1. Dahu job. The Dahu job manages the execution of one Dahu plugin (see next subsection) and provides a unique identifier which gives access to the status and output of the processing. Jobs see their input and output saved onto disk, which allows offline reprocessing in case of an issue during online data analysis. 2. 2. 2. Dahu plugins. Dahu plugins implement the processing logic of the different pipelines. Written in simple Python and fairly independent of the Dahu framework, those plugins are often written or modified by beamline scientists themselves.
Data analysis pipelines There are two main experiments performed at the BioSAXS beamline -using the sample changer (referred to as SC) or the inline-chromatography setup (referred to as HPLC since it uses a high-pressure liquid chromatograph). Thus, two analysis pipelines were built, one for each of these experimental modes. The common part, mainly dealing with azimuthal integration, is integrated into a pre-processing pipeline called integrate multi-frame. Since the Pilatus3 2M detector is controlled by the LIMA software  (Petitdemange et al. , 2018) , raw images are now saved in an HDF5 file format  (The HDF Group, 2000 -2021)  with ten or 100 frames per file (depending on the acquisition mode, Fig. 2 ). HDF5 is not only imposed by the ESRF data policy  (Dimper et al. , 2019)  but also offers numerous benefits such as compression, faster data-access, symbolic links to data sets stored in other files, etc.
, 2015). (2) Buffer frames are averaged together and subtracted from the sample-averaged frame (restarting from 2D raw frames). (3) Azimuthal integration of the subtracted frame with pyFAI  (Kieffer et al. , 2020). (4) Guinier analysis with the associated linear regression of log[I(q)] versus q 2 providing R g and logðI 0 Þ at low q as default plot. (5) Dimensionless Kratky plot: (qR g ) 2 I/I 0 versus qR g to assess the flexibility of the macromolecule. (6) Porod  (Glatter & Kratky, 1982)  and Rambo-Tainer invariants  (Rambo & Tainer, 2013)  calculation to assess the molecular volume and molecular mass of the sample. (7) Indirect inverse Fourier transform using the BIFT algorithm  (Vestergaard & Hansen, 2006)  provides the pair distance distribution function p = f (r). (8) Transfer of reduced data to ISPyB for BioSAXS (compatibility layer with legacy ISPyB).
The final stage of this pipeline is to register those results into the ISPyB for BioSAXS database  (De Maria Antolinos et al. , 2015)  (https://exi. esrf. fr), and make them instantly available to the user via the data portal  (ESRF, 2011 (ESRF, -2022)). Once data are registered into the data portal (https://data. esrf. fr), they receive a unique DOI which can be referred to when users are publishing results to the SASBDB  (Kikhney et al. , 2020). The same data are shared with the BsxCuBE3 control software via a memcached key-value database for instant feedback on the user interface.
(6) Analyse each fraction with a similar pipeline to the one presented in Section 3. 2: Guinier plot, Kratky plot, BIFT analysis. (7) Finally, export data and send them to ISPyB for BioSAXS. Multivariate analysis is performed to extract the signal of the macromolecule from background scattering, and provides a hint on how many components have been separated in the chromatography. Gavish & Donoho (2014)  provide the number of singular values/vectors which are to be saved after the SVD decomposition The first singular vector contains mainly background scattering, the following ones contain signal from the separated components, and subsequent ones account only for noise. Unfortunately, those singular vectors, beyond the first one, do not look like scattering curves since SVD does not enforce the positivity of those extracted  singular vectors during the decomposition  (Mak et al.
Outlook The foreseeable future should see a better integration of BioSAXS data into the data portal with better web visualization capabilities of the processed HDF5 files. The buffer averaging and subtraction in HPLC mode is not (strictly) exact since it is based on integrated curves which have been normalized. It should be possible to weight properly those curves to obtain an average which is exactly the same as if one would have averaged or subtracted 2D frames and integrated the result (discussed in Appendix B). Future algorithmic work will focus on ab initio shape reconstruction, based on the DENSS  (Grant, 2018)  which is currently too slow to run with real-time constraints at the beamline.
Conclusion This document introduces the FreeSAS and the Dahu software packages which are used, respectively, to analyse BioSAS data and control online data analysis. These two packages are used at the BioSAXS beamline at ESRF, combined with others, to provide complete data-analysis pipelines. The three pipelines described in this contribution have been used in production since 2020, and provide real-time feedback of ongoing experiments to the user. All metadata, all parameters and all references to the source data are recorded together with the processed data into single HDF5 files, which offers not only convenient storage but allows also reproducible science following the FAIR principle.
B1. Integrate before averaging The code can be represent in these four lines of Python code: Intensitiesareobtaine d fromanarithmeticaverag e ofalreadyweightedcurve s. Uncertaintiesareobtainedfromaquadraticavera geofun certaintiespropag atedduringintegration. This method does not take into account the fact that some curves did have more weight than others during integration. To exemplify the issue, let us consider the averaging of two buffer data sets: one with 100 frames and the second only with a single frame. Since those buffers are all equivalent, the uncertainties for the first data set will be ten times smaller [(100) 1/2 ] than for the second. When merging those data sets with this method, the uncertainties of the first data set are negligible compared with the second, and the propagated uncertainties will be 40% smaller than the second data set, while in reality it should be ten times smaller, [(101) 1/2 ]! As a rule of thumb, the numerical values obtained with this method are correct when all normalization factors are of similar magnitude.
Framework description This section briefly describes the frameworks used for the comparisons. Furthermore, it details their numerical setups, subgrid-scale (SGS) models and grid structuredness. 3. 1 waLBerla waLBerla  [9, 10]  is a massively parallel multi-physics framework developed at Friedrich-Alexander University Erlangen-Nuremberg (FAU) with focus on simulations using the lattice Boltzmann method. Its specific software design combined with the code generation framework lbmpy  [18]  allows for highly optimised, performance-portable solutions on block-structured grids for various application areas, like phase-field simulations, and particle-laden flows  [19, 20]. It supports shared and distributed memory parallelism with OpenMP and MPI, respectively, automated SIMD vectorisation, and the execution on NVIDIA graphics cards.
For the wind turbine simulations in the present study, we use our recently added holistic actuator line model (ALM) approach  [13] , which provides a mutual code base for CPU and GPU. Not only does this approach conserve the performance-portability of waLBerla, but it also reduces the maintenance efforts. For the LBM setup, we chose a D3Q27 stencil and a cumulant collision model with a Smagorinsky SGS model.
CASTOR CASTOR is a so-called free-wake, lifting-line flow solver based on a vortex filament discretisation of the wake and the wind-turbine blades. The underlying N -body problem is solved on GPUsystems using the CUDA programming language to increase the computational performance. It has been validated against wind tunnel measurement  [22]. In the present study, CASTOR acts as a reference for blade force distributions: this kind of solver is supposed to be accurate for blade force predictions and intrinsically accounts for tip-losses. Furthermore, CASTOR is based on a liftingline approach, which allows a fair comparison with actuator-line solvers, as the same limitations are expected, e. g. , no three-dimensional effects such as stall delay are accounted for. The present implementation follows the work of Sebastian et al.
Wake velocity and turbulence intensity profiles Next, we will look at the velocity profiles at several downstream positions in the wake. Figure  2  shows a globally satisfying agreement between the solvers. The transition to a turbulent wake occurs at a similar location for the wider kernels in SOWFA and waLBerla-wide. Even nearly Gaussian profiles are observed in the far wake (x/D ⩾ 9). waLBerla-wide predicts a slightly higher velocity deficit at x/D = 9. Using the thinner kernel, the waLBerla-thin velocity profiles tend to be smoother, indicating an earlier transition to a turbulent wake. Martinez-Tossas et al. [30]  predict this kind of behaviour, reasoning that with a narrower kernel thinner and more unstable tip vortices emerge. In the far wake (x/D = 11), the impact of the force spreading kernel is low.
This postulation is consistent with the observed turbulence intensity profiles in Figure  3. We define the axial turbulence intensity as the ratio of the root-mean-square velocity fluctuations to the mean velocity, sampled in a space-fixed reference frame at every simulation time step. It does include coherent structures such as the tip vortices. In the near-wake at x/D = 3, waLBerla-thin results in a higher turbulence level near the rotor blade tips than waLBerla-wide. As shown in Appendix A, reducing the time step also delays the transition to a turbulent wake. More investigations are required to understand this phenomenon. In particular, using strictly identical force spreading methods is mandatory before drawing any definitive conclusions. In terms of code-to-code turbulence intensity profiles comparisons, there are noticeable differences.
Code performances In this section, we compare the performance of NS-based and LBM-based ALM solvers for wind turbines in a parallel environment. In particular, we investigate their weak and strong scaling behaviour and their overall performance. All simulations run on the Topaze supercomputer at  CCRT/CEA and follow the setup described in Section 4. Topaze has 864 compute nodes based on 2 AMD Milan@2. 45GHz (AVX2) CPUs with 64 cores per CPU. Furthermore, it includes an accelerated partition with 48 compute nodes with 4 Nvidia A100 GPUs each. Firstly, we focus on the performance aspects of waLBerla and its holistic ALM approach. Figure  4  (left) depicts the weak scaling of waLBerla for CPU and GPU runs and clearly shows that the excellent behaviour of waLBerla, e. g. , reported in  [10] , was not compromised by the ALM implementation.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  824158  (EoCoEII). The  Science of Making Torque from Wind  ( TORQUE 2022 ) Journal of Physics: Conference Series  2265 (2022 )  022027 IOP Publishing doi:10. 1088/1742-6596/2265/2/022027The   Science of Making Torque from Wind  ( TORQUE 2022 ) Journal of Physics: Conference Series  2265 (2022 )  022027 IOP Publishing doi:10. 1088/1742-6596/2265/2/022027.