dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS AND DISCUSSION In this section, the description and discussion of the numerical simulations corresponding to the unforced and excited scenarios are presented. First, the unforced flame results are given in Section 3. 1, where the characterization of the ICF overall flame structures, the inherent fluctuating temperature and OH mass fraction in Sections 3. 1. 1 and 3. 1. 2, are respectively given. Finally, the excited flame response is discussed in Section 3. 2, where the fluctuating forced flame structures, the frequency analysis of the flame surface area fluctuation and the flame describing function in Sections 3. 2. 1, 3. 2. 2 and 3. 2. 3, are respectively presented. All the simulations presented here were developed in parallel, with Linux-64 SuSE, Intel(R) Xeon(R), at Ansys cluster, using between 100 to 180 nodes to run Fluent.
ACKNOWLEDGMENTS This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant Agreement nr 766264. This work was performed while L. F. Figueira da Silva  was on leave from the  Institut Prime (CNRS, France). The authors also gratefully acknowledge the support provided by  Conselho Nacional de Desenvolvimento Científico e Tecnológico ,  CNPq , under the  Research Grant  No. 403904/2016-1.
Numerical examples In this section, we evaluate the numerical performance of the HHO method on a complete panel of numerical test cases. We focus on the ( , 0, 1, )-Carreau-Yasuda law (5 5) (corresponding to the powerlaw model) with values of the exponent ranging from 1. 25 to 2. 75. Our implementation relies on the SpaFEDTe library (cf. https://spafedte. github. io https://spafedte. github. io).
The evolved carbon dioxide were detected by an on-line TCD calibrated by the peak area of known pulses of CO 2. The phase composition of catalysts was investigated by X-ray diffraction analysis. Prior the measurements, the investigated samples were reduced in hydrogen stream (5% H 2 -95% Ar) at 500 °C for 2 h. The diffraction patterns were collected using a PANalytical X'Pert Pro MPD diffractometer in Bragg Brentano reflection geometry. The diffractometer was equipped with Cu Kα radiation source (λ = 1. 5418 Å). Data was collected in the 2θ range of 5°-90° with a step size of 0. 0167° and exposure per step of 27 s. Because the raw diffraction data contain some noise, the background during the analysis was subtracted using the Sonneveld, E. J. and Visser algorithm. The data were then smoothed using a cubic polynomial function.
Reported here are new data on the structural and catalytic properties of a series of mono-component cobalt and bi-component Co-Ce catalysts supported on SBA-15 (Santa Barbara Amorphous-15)). The catalysts performance has been evaluated by tests on combustion of methane, propane, and n-hexane. It was established that the preparation of the Co-Ce catalysts by the 'twosolvent' technique does not significantly change the mesoporous structure, however, its pores are clogging with the Co and Ce guest species. Cobalt and cerium are uniformly distributed and preferentially fill up the channels of SBA-15, but oxide agglomerates located on the surface are observed as well. The highest activity of the mono-component cobalt sample is explained by its higher reducibility as a result of lower interaction of the cobalt oxide with the SBA-15.
Catalyst Characterization The obtained materials were characterized by SAXS, N 2 adsorption-desorption, X-ray diffraction, and TEM. Small angle X-Ray scattering (SAXS) data were collected on a "SAXSess mc 2 " instrument (Anton Paar, Graz, Austria), using a line-collimation system. This instrument is attached to an ID 3003 laboratory X-Ray generator (General Electric, Ahremburg, Germany) equipped with a sealed X-Ray tube (PANalytical, λ Cu, Kα = 0. 1542 nm) operating at 40 kV and 50 mA. Each sample was introduced into a "Special Glass" capillary for liquids and liquid crystals (Φ = 1. 5 and 2. 0 mm for micellar solutions and liquid crystals, respectively), or between two sheets of Kapton ® for materials, then placed inside an evacuated sample chamber, and exposed to X-Ray beam. Scattering of X-Ray beam was registered by a CCD detector (Princeton Instruments, Trenton, NJ, USA 2084 × 2084 pixels array with a 24 × 24 µm 2 pixel size) at 309 mm distance from the sample.
Using SAXSQuant software (Anton Paar), the 2D image was integrated into one-dimensional scattering intensities I(q) as a of the magnitude of the scattering vector q = (4π/λ) sin(θ), where 2θ is the total scattering function angle. Thanks to a translucent beam-stop allowing the measurement of an attenuated primary beam at q = 0, all measured intensities can therefore be calibrated by normalizing the attenuated primary intensity. Data were then corrected for the background scattering from the cell and for slit-smearing effects by a desmearing procedure from SAXSQuant software, using the Lake method. Nitrogen adsorption-desorption isotherms were carried out at -196 • C within a wide relative pressure (P/P 0 ) range varying from 0. 010 to 0. 995 with a volumetric adsorption analyzer TRISTAR 3000 manufactured by Micromeritics.
Before measurements, the samples were outgassed under vacuum (pressure = 0. 13 mBar) at 25 • C for 16 h. The specific surface area of each sample was calculated by the Brunauer-Emmett-Teller (BET) method  [34]. The pore diameter and the pore size distribution were determined from the adsorption branch of the corresponding isotherm using the Barret-Joyner-Halenda (BJH) method  [35]. Wide angles X-Ray diffraction patterns were collected on a BRUKER D8 ADVANCE A25 operating with Cu Kα radiation (Kα = 0. 154,18 nm) and equipped with the LynxEye XE-T high resolution energy dispersive 1-D detector. The XRD powder patterns were collected at 25 • C within the range 3 < 2θ < 70, step = 0. 018 • 2θ. The phases identification has been realized with the X'PertHighscore software (PANalytical) and the PDF-4+ 2020 database from the International Centre for Diffraction Data (ICDD).
Temperature-programmed reduction (TPR) and temperature-programmed oxidation experiments are described in  [6]. Oxygen-TPD data were obtained by using of oxygen gas analyzer Teledyne Mod. 802 (paramagnetic principle). The samples pre-treatment consisted of heating in flow of 5% O 2 in N 2 at 450 • C for 6 h, then cooling down to room temperature in the same gaseous mixture. The applied heating rate was 7. 5 K min -1 , gas flow rate: nitrogen, 500 mL min -1. Temperature-programmed reduction by propane (C 3 H 8 /TPR) was carried out consequently after the O 2 -TPD by using a gas mixture of 0. 125 vol. % of propane in nitrogen (500 mL min -1 ). The gas analysis was performed by on-line gas analyzers (Maihak-Sick Mod. 710S, SICK AG, Hamburg, Germany, 2012) CO/CO 2 /O 2 and by an on-line gas analyzer for total organic content: Horiba, Model FIA-510, HORIBA Ltd, Kyoto, Japan, 2012, (equipped with flame ionization detector), mass-flow control system (Bronkhorst High-Tech B.
This electrochemical system comprises: two superposed disk-shaped platinum grids  [18, 19]  as the working electrode (WE2 in Fig. 1 ), a platinum rod as the auxiliary electrode and a Ag/AgCl (in KCl 3M) reference electrode (Dri-Ref5). The Pt grids are sealed off by means of two sheets of insulating adhesive with a pre-cut hole of 5 mm diameter. The connection between the Pt electrode and the setup is achieved by means of carbon fibers, also enclosed between the adhesive strips. A plastic sheet of circa 400 µm of thickness is inserted to allow a safe distance between the Pt grids and the mercury electrode. This setup is connected to an EmStat3+ (Palmsens BV, The Netherlands) and piloted by PStouch software installed on a tablet device that was operating with a battery.
Acknowledgments. We acknowledge the support of the  French INSU/EC2CO program  for the 2017-2019 period. JPP and ER acknowledge the financial support of the  European Union 's  Horizon 2020 Research and Innovation Programme  under the Grant Agreement No. 776804 -H2020-SC5-2017  (project acronym NEXT). JG acknowledges  SATT Sayens  for financial support. Claire Genois  is acknowledged for ICP-MS measurements and  Hela Smati  for the preliminary results she obtained during her master training.
The dimensions (thickness (th), width (w), and length (L)) and the weight of the specimens were measured precisely to calculate the amount of calcium initially present in the specimen. An epoxy resin, EUROKOTE 48-20, was applied to the mortar specimens to cover 5 of the 6 surfaces, leaving only the top surface (corresponding to the surface from the core of the original 4 cm × 4 cm × 16 cm mortar specimen) exposed to the biodeterioration test as shown in Figure  1. Two thin lines (1 mm) of resin were also applied to the edges of the exposed surface in order to delimit the solution flow from upstream of the specimen to downstream and to preserve a reference of the initial thickness for further observations, by SEM for example (data not presented in this paper). Materials 2020, 13, x FOR PEER REVIEW 5 of 24 OPC mortar was systematically used in every campaign (except for campaign III, which was not taken into account for the PI calculations) as a reference material.
The PIs of CEM V and BFSC were 84% and 80%, respectively. These materials could be considered to have relatively similar performances, with a slight increase in resistance (depending on the nature of the materials) compared to Portland cement. CAC materialswith silica-and calcium aluminate-based better than Portland materials against the biogenic attack. The PIs of CAC-S and CAC-A were 67% and 23%, respectively, showing the strong influence of the reactive aggregate on the performance of the mortar. The reproducibility of the results was assessed using the results for the same material from different campaigns. Although it was highly unlikely that exactly the same material would have the same PI in different campaigns due to the experimental variations, a confidence interval (CI) was calculated based on the variation observed from the experimental data and reported in Table  7.
The CI gives a zone in which the materials can be identified according to their performance. The reproducibility of the results was assessed using the results for the same material from different campaigns. Although it was highly unlikely that exactly the same material would have the same PI in different campaigns due to the experimental variations, a confidence interval (CI) was calculated based on the variation observed from the experimental data and reported in Table  7. The CI gives a zone in which the materials can be identified according to their performance.
Acknowledging such uncertainty, a 2-step global model for n-dodecane  [19]  and a 1-step model for methane  [20]  have been derived using Bayesian inference, allowing a quantification of the accuracy of predicted quantities. Likewise, the study of Iavarone et al. [21]  derives a reduced char combustion model using the Boundto-Bound (B2B) framework, in which uncertainty bounds for predicted features are adjusted based on the experimental uncertainty  [22]. A comparison between the Bayesian inference approach and the deterministic B2B framework to deal with model uncertainty in combustion applications has been discussed in the work of Frenklach et al. [23]. Several works have considered uncertainty quantification (UQ) in reactive flows, a wide effort focuses on characterizing and reducing kinetic models while accounting for uncertainty  [24] [25] [26] [27] [28] [29] [30].
Global scheme calibration results using Bayesian inference This section discusses three different calibration exercises of the model parameters: (i) only using the flame speed observations, (ii) only using thermal thickness observations, and (iii) relying on both observation datasets. For the latter calibrated model, the sensitivity to the artificial noise variance is also discussed. The parameters' posterior is not obtained as a closed-form expression, and its sampling requires appropriate methods. We rely on a Markov Chain Monte Carlo (MCMC) method to generate samples from the posterior probability distribution function in this work. The MCMC algorithm randomly generates a sequence of samples (states), where the next state of the chain is conditioned on the current state, creating a so-called Markov chain.
Acknowledgments The authors wish to thank  V. Moureau  for his help on setting up the YALES2 case. This work has benefited from the financial support of the  LabEx LaSIPS  ( ANR-10-LABX-0032-LaSIPS ) managed by the  French National Research Agency  under the " Investissements d'avenir" program  ( ANR-11-IDEX-0003 ). The study was performed using HPC resources from the  Mésocentre computing center of CentraleSupélec and École Normale Supérieure Paris-Saclay  supported by  CNRS  and  Région Île-de-France  (http://mesocentre. centralesupelec. fr/).
In order to overcome some of the simplifications needed to define flow-boundary specifications of individual components, several attempts have been made to couple two components of the engine. In some works, RANS or LES solvers were used for the combustion chamber and URANS solvers for the turbine  (Salvadori et al. , 2012; Insinna et al. , 2014; Jacobi et al. , 2017). Nonetheless, these approaches still lead to strong hypotheses on the information provided from one code to the other, implying approximations on the mass-flow rate, total energy and turbulent quantities. Using a single LES solver to solve the full system constitutes a high potential solution to avoid these approximations  (Duchaine et al. , 2017; Miki et al. , 2018; Thomas et al. , 2019; Miki et al. , 2020). Such multi-component LES simulations for example accurately represent the critical unsteady high temperature spots issued from the combustion chamber that impact on the turbine blades.
In the case of this simulation separate in-code instances are automatically generated for static and rotating sub-domains. Each instance is coupled internally using the CWIPI library from ONERA  (Refloch et al. , 2011; Duchaine et al. , 2015). Since there are two different rotational speeds, the integrated simulation FULLEST utilises three instances of AVBP to run as illustrated by Figure  2 : one for the static sub-domain, one for the fan sub-domain, and one for the impeller sub-domain. The different instances for the stand-alone simulations are colour-coded according to Figure  1. Details concerning the mesh used for each simulation are provided in Table  1  along with the wall-clock time per physical millisecond for the given number of cores. All meshes are unstructured and solely made of tetrahedral elements.
Once the solution is converged after approximately 20 ms, the gas air (represented by a single species in AVBP) is transformed into a mixture of N2, O2 and the species: CO, CO2 and H2O, which are set to zero. Then fuel is injected in a gaseous form inside the vaporiser for about 5 ms until a recirculation zone of mixed fuel and air is generated in the combustion chamber. The flow is then ignited by adding an energy deposition that mimics a spark. To finish, the simulation is converged after a transient period of about 25 ms with a flame properly positioned in the flame-tube. Data is then acquired for 50 additional milliseconds for flow analysis which corresponds to about 5 convective flow-through times which is enough to converge root-mean-square (RMS) values.
Generation of the integrated domain and associated mesh The static sub-domain for FULLEST is composed of the OGVs, the bypass duct, the radial and axial diffusers as well as the combustion chamber as illustrated in Figure  1e. Furthermore, it is geometrically divided in aft and forward disconnected regions by the impeller sub-domain. The mesh and initial solution of the fan sub-domain, the forward region of the static sub-domain (from fan outlet to compressor inlet) and the impeller sub-domain can be easily constructed by azimuthal repetition of sectoral meshes using the in-house package for manipulating unstructured computational grids HIP  (Müller, 1999). The tool HIP is able to merge all replicated surfaces creating a single mesh and associated solution file for all 360°sub-domains. In fact, only the mesh for the fan subdomain from FAN + OGV is conserved intact.
TREEPART was built to account for the local computing machine topology at the hardware level and minimise global communications. In fact it uses one-side MPI 3 communications for all exchanges and only relies on Parmetis for low level graph decompositions. TREEPART can be used at runtime within the code or off-line to generate a parallel mapping file called el2part (each element of the mesh is mapped to a given number of partitions) than can be read by AVBP to perform the simulation reducing initialisation times and ensuring reproducibility. An illustration of the final partitioning is shown in Figure  4  (left).
Besides, it enables a fair investigation of the impact of such an integration since they now have the same periodicity for each component. In this work, this procedure saves about 8 million core hours with respect to an initialisation from scratch and in addition generates a data-base of stand-alone component predictions. Without considering the optimisations for parallel computing and the ability of the code to perform such simulations, the main challenge to overcome has been the generation of the 2,100 million cells mesh for FULLEST. This mesh has been generated in several steps to surpass present memory and software limitations encountered in our work. The generation of the mesh for some of the sub-domains such as the fan, the OGVs and the impeller takes advantage of the initial periodic mesh which is simply repeated azimuthally using the in-house package for manipulating unstructured computational grids HIP  (Müller, 1999).
Acknowledgments The authors would like to warmly thank  T. Quirante  and  N. Vieira-Nobre  from  Akira Technologies  for providing the geometry and operating conditions, and  N. Buffaz  and  A. Paillassa  from  Safran Helicopter Engines  for their technical support on the generation of the reduced-count geometry of the compressor and for performing its respective RANS simulations. The authors also gratefully thank  G. Exilard  and  S. Richard  from  Safran Helicopter Engines  for his help during the setup of the micro-perforations in the combustion chamber.
II. SIMULATION DETAILS A. Molecular dynamics setup Molecular dynamics (MD) simulations of periodic cubic boxes containing 10 000-15 000 atoms were performed in LAMMPS  45  with the USER-DRUDE 46 package enabled. Starting configurations and force field were prepared using the fftool  47  and Packmol 48 utilities and then converted to the polarizable force field using the polarizer and scaleLJ tools. 49 A cut-off of 12 Å was considered for pair potentials with tail corrections applied. Electrostatic energies were evaluated using the particle-particle particle-mesh (PPPM) method with an accuracy of 10 -5. Regarding simulation conditions, the time step was 1 fs and the pressure was kept at 1 bar, while different temperatures were chosen for different systems according to the availability of experimental data for comparison.
After 2 ns equilibrations, 10 ns trajectories of liquid systems were generated in the N pT ensemble using conventional and temperature-grouped  50  Nosé-Hoover thermostat and barostat. The crystal structure of EAN was modeled for 1 ns after the relaxation of the box. The structure factor of liquid systems was calculated using the TRAVIS software,  51, 52  with atomic form factors or cross sections corresponding to X-ray and neutron scattering, S(q) = N i=1 N j=1 x i x j f i (q)f j (q)H ij (q) N i=1 x i f i (q) 2 (1) H ij (q) = 4πρ 0 rmax 0 r 2 (g ij (r) -1) sin(qr) qr dr (2) Dynamic properties were evaluated from the trajectories according to recent recommendations. 53 The Green-Kubo relation was used to evaluate shear viscosity using the off-diagonal components of pressure tensor from the equilibrium trajectory, η = V kT ∞ 0 p xy (t)p xy (0) dt (3) The tail of the autocorrelation function (ACF) of Green-Kubo relation was smoothed with the following exponential decay function to eliminate the noise at long times, S f ACF (t) = a exp(-t β ) (4) where a and β are obtained from fit to the non-oscillatory decay section of the ACF.
46, 56  It was recently shown  50  that such dual-thermostat method may not ensure perfect equipartition between the all molecular DOF, and a new thermostat handling the molecular center-of-mass motion and the intramolecular DOF separately was proposed. We implemented this temperature-grouped Nosé-Hoover thermostat in the LAMMPS  45  molecular dynamics package (as part of the USER-DRUDE package).
9 The linearity of the hydrogen bond was confirmed experimentally by X-ray diffraction on the crystal. 68 By means of the empirical potential structure refinement (EPSR) applied to X-ray and neutron scattering data on liquid EAN, several groups  [4] [5] [6] [7]  proposed the formation of a bent H-bond, with a length of 2. 4 Å and an angle of 109°, where three hydrogen atoms of the ammonium head group share one oxygen atom of the anion. This is a result of the pres-ence of a Lennard-Jones site of diameter 2. 5 Å on each hydrogen atom in the force field used for the EPSR. Repeating the same procedure with a model where hydrogen atoms are embedded into the neighbouring nitrogen atom (σ H = 0. 8 Å), followed by MD simulation with a threebody term included, led to a linear, directional hydrogen bond with a distance of 1.
ACKNOWLEDGMENTS This work was supported by an  IDEX Lyon Fellowship  ( ANR-16-IDEX-005 ) and by  ANR  project  LIQUI2D  ( ANR-18-CE09-0018 ). Computational tasks were performed using resources from  GENCI-IDRIS  (Grant  2019-A0070800609 ) and on the  Pôle Scientifique de Modélisation Numérique (PSMN)  at  ENS de Lyon.
The longer acquisition time for Bi L3 edge than for the Co ones was due to the small Bi edge jump measured in transmission mode, which was around 0. 035 compared to 2. 0 at the Co K edge. In order to improve the S/N ratio, 110 spectra recorded in 250 ms were merged at the Bi L3 edge against 50 spectra at the Co K edge. Normalization and energy calibration of the data were performed using the Python interface developed at the ROCK beamline  [17, 19]. Cobalt and bismuth speciations were obtained thanks to the multivariate curve regression with alternating least-square (MCR-ALS) analysis  [20]  using the MCR-ALS 2. 0 Matlab toolbox developed by Tauler's group  [21]. This method, which has been described in details for time-resolved Quick-XAS data in references  [19, 22, 23]  permits to isolate from the data set the spectra of each pure species participating to the reaction and its concentration profile.
The outcome files obtained using the MCR-ALS 2. 0 Matlab toolbox for the different catalysts are given in Supplementary Material (SM). The EXAFS analysis at the Bi L3 edge of the catalysts after reduction and air passivation was performed using the Athena and Artemis software  [24]. The reaction products were monitored by Raman Spectroscopy using a 532 nm KAISER Raman spectrometer equipped with a probe head located at the outlet of the capillary reactor  [25]. The Scanning Transmission Electron Microscopy (STEM) analyses were carried on a double corrected analytical JEOL TEM 200 CF operated at 200 kV. The elemental maps acquisitions (256 x 256 pixels) were carried out using the Energy Dispersive Xray Spectrometer (EDX) Centurio 100 detector with a scanning speed of 0. 05 ms/px, whilst applying a drift correction every 60 seconds.
The measurements were performed in presence of H2 or syngas (1 mbar) at the temperature ranging from ambient to 315 ºC. The XPS spectra were analyzed by fitting the Shirley-type function with the Casa XPS software. In order to visualize clearly the localization of the promoter, TEM and NAP-XPS experiments were performed with the model CoBi/CNT catalysts containing 2 wt. % Bi.