dataesr/scientific-paragraphs-categorization huggingface dataset:
Relaxed scans were systematically performed at the B2PLYP-D3/6-311 + G(2d,p). Tunneling effect, based on a one-dimensional asymmetric Eckart potential, was taken into account for reactions involving a H-atom transfer. Usually, this approach leads to accurate transmission coefficients for temperatures above 500 K  [ 26 , 27 ]. Statistical factor involving external symmetries (previously removed from rotational partition functions) and optical isomers of reactants and transition states (TS) were also considered to correctly compute the rate constants. Finally, high-pressure limit rate constants were computed from canonical transition state theory and fitted over the temperature range 50 0-20 0 0 K, using a three parameters Arrhenius equation ( Eq. 2 ):  (2)  All these calculations, as well as thermodynamic data k ∞ ( T ) = A T n e -E RT ( S • 298 K and C • p ( T ) ) and NASA polynomials determination were automatically performed with the in-house code THERMROT  [25].
Even if enthalpies of formation can be computed from THERMROT using atomization energies, we evaluated them from isodesmic reactions using experimental data found in the CCCBDB  [28]  data base or in the active thermochemical tables (ATcT  [29]  ). Pressure effects for sensitive reactions, such as H-atom additions, β-scissions or H-atom transfers were processed by means of RRKM-Master equation using the MESS code of the PAPR suite  [30]  and fitted with the PLOG formalism used in the Chemkin Pro software  [31]. Lennard-Jones parameters were computed using the formalism developed by Tee et al. [32]  which requires the knowledge of critical pressure (P C ), temperature (T C ) and the acentric factor ( ω). For unknown data (T C , P C and ω), an estimation has been performed from the group-contribution method proposed by Marrero and Gani  [33].
e. , from termination between two radicals. The rate coefficients of these barrierless reactions have been deduced from correlations proposed in the EXGAS software  [36]. Initiations can also occur by bimolecular H-atom abstraction with O 2 , according to the reaction R1: CH 3 NCO + 3 O 2 = • CH 2 NCO + HO 2 • (R1) In accordance with the work of Zhou et al. [37]  , who investigated hydrogen atom abstraction from allylic sites by O 2 , only the triplet state of O 2 has been considered here. Indeed, they showed that the singlet state had no kinetic influence due to its low concentration at temperatures corresponding to combustion applications. As mentioned by Zhou et al. , such reaction involves the formation of complexes related to the reactant (RC) and the product (PC). In order to take into account the effect of these complexes, we performed a complete calculation of the potential energy profile ( Fig.
Initiation reactions of CH 3 NCNCH 3 As for methyl isocyanate, the unimolecular initiations are written in the reverse direction (termination). For the C-H bond breaking, we used a correlation from EXGAS software  [36]  , while for the methyl group cleavage we retained a value equal to 10 12 cm 3 mol -1 s -1 , close to that proposed by Faravelli et al. [40]  or Davis et al. [41]  for the combination of • CH 3 and • CHCCH 2 radicals (formation of 1,2-butadiene). This unimolecular initiation leads to formation of • CH 3 and CH 3 NCN • (R3). For the latter radical, we considered the terminations with H-atoms as well as disproportionations with small radicals. The rate constants are based on analogies with similar reactions proposed by Tsang for allyl radical • C 3 H 5  [42]. Due to the unsaturated structure of the carbodiimide, several concerted reactions can be envisaged.
Pyrolysis of methyl isocyanate As previously mentioned, very few studies have been carried out on the thermal decomposition of isocyanates. From the best of our knowledge, the experimental study of Blake and Ijadi-Maghsoodi  [17]  is the only one on methyl-isocyanate presenting species profiles. Pyrolysis experiments were performed in a closed reactor for temperatures ranging from 700 to 821 K and pressures from 55 to 300 Torr. Blake and Ijadi-Maghsoodi only give the distribution of species for a temperature of 799K and an initial pressure of 150 torr. This experiment was simulated using the Chemkin Pro software with an isothermal batch reactor model. The results obtained are shown in Fig. 16. Overall, the results obtained are in good agreement with the experimental data. The increase in total pressure ( P) is well reproduced by the model, as shown in Fig.
16  e and 16 f). These results are not consistent with the rest of the simulations, which are in agreement with the experiments. In fact, Blake and Ijadi-Maghsoodi  [17]  put forward an experimental problem related to the analytical part. At the end of the reaction, the products (and the unconsumed reactant) are recovered in a cold trap but a reaction occurs between HCN and MIC in this trap, leading to the formation of a new product. This  problem was confirmed by the authors, who performed specific experiments where HCN and MIC were mixed at 723K  [17]. At this temperature, no reaction occurs in the reaction vessel but appears in the analytical section of the apparatus. In order to take into account this effect, we sought to rationalize the experimental data. To "correct" the experimental data of methyl isocyanate, we fitted the experimental HCN profiles using a polynomial regression in order to obtain a continuous function representing its concentration as a function of time.
Next, we calculated the difference between the simulated values of HCN and those obtained from the experimental polynomial. We assumed that this difference was mainly due to the HCN fraction reacting with the MIC in the trap and we added this difference to the experimental MIC values. These "corrected data" are represented by open squares in Fig. 16 e. We performed the same procedure to modify the experimental data of HCN, assuming that the difference between the experimental and simulated amounts of MIC corresponded to the fraction reacting with HCN. Even if this approach can be questionable, Fig. 16  e and f show a clear improvement and a good consistency between experimental and simulated data, both for CH 3 NCO and HCN (squares and curves in Fig. 16 e  and f ). CO, HCN and H 2 are the main products of the MIC pyrolysis and their formation can be mostly explained by the propagation step showed in Fig.
18. Carbon monoxide is also produced from the addition of hydrogen atom to CH 3 NCO. Similarly, HCN is formed from the decom-position of N,N'-dimethylcarbodiimide, and to a lesser extent from H-addition. Although the corresponding fluxes are lower, they explain the higher amount of CO and HCN observed compared to H 2 ( Fig. 16 b, c  and f ). The good agreement between simulations and experimental results shows that the branching ratio between the different reaction pathways is well modeled by the mechanism. Among minor products, carbon dioxide ( Fig. 16 b ) is correctly described by the model, supporting its formation by the proposed pericyclic reaction. Similarly, the mole fraction of CH 3 NCNCH 3 is consistent with the experimental data ( Fig. 16 d ) even though the quantities formed are small.
At this level, it is noteworthy that 320 datasets were extracted over 80 ms from the numerical simulation. 3 The post-processing first consists in extracting the variables fluctuations ( ) 1 by substracting the corresponding baseline values ( ) 0. Subsequently, the various terms of Eq. (  8 ) , which are detailed in the first and second columns of Table  3  , are computed at each location and time step. Volume integration then yields the various contributions that appear in Eq. (9). Finally, the third column of Table  3  reports their time-averaged values, normalized by the time-averaged value of the disturbance energy, i. e. , α j = D j / E d , thus quantifying their respective weights in the total disturbance energy variation. It is noteworthy that three contributions display significant growth-rate values: (i) D s 1 and (ii) D comb Q , which are positive and associated to entropy and direct combustion source terms, and (iii) 3 This corresponds to 8 imposed oscillation periods, with 40 points per period.
5 S 0 L for the largest value η = 2. 01. Such a value of u RMS is not irrelevant to actual combustion devices. Flow velocities (and associated levels velocity fluctuations) in actual combustors may indeed be far greater than the laminar flame propagation velocity. In this respect, it is also noteworthy that inlet flow reversal may even be triggered by combustion instabilities as it occurs during flashback processes  [43]. For the simulations post-processing, the dataset characteristics were kept the same as those retained in Section 3 , i. e. , 320 datasets gathered over eight imposed oscillation periods. Section 4. 1 first settles some definitions that are introduced to characterize the flame dynamics. Then, Section 4. 2 is devoted to the analysis of the DEB sensitivity according to this framework.
Acknowledgments The quality of the manuscript has benefited from the comments made by the reviewers. The authors gratefully acknowledge  A. K. Mohamed ,  O. Dessornes ,  J. Anthoine ,  P. Villedieu ,  L. Jacquin  from  ONERA , and  T. Coué  from  DGA  for their support, as well as  ONERA  and  AID  (Contract  DGA 2020 65 0022 ) for financial support.
2017). The PIV image is processed with Lavision Davis software to obtain velocity vectors. Particles are determined by subtraction of sliding background (50 Pixels) and then particle intensity is normalized over 9 pixels. Multipass PIV + PTV calculation with hybrid cross-correlation starting from window size of 64×64 pixels to window size 16×16 Pixels with symmetric shift with 50% overlap and 3 passes. Vectors are deleted if the correlation value is less than 0. 4 or vectors lie away from average by 3 times the standard deviation (deletion of 1-2% of vectors). Empty vectors are filled up with interpolation among the neighboring vectors. Then a weak filter with a second-order polynomial fit (9 points) is applied. With these operations, the raw image is converted to velocity vector over space and time i.
Although these calculations might sterile, the trends allow corroboration of the results of catalyst A showing that by adding CF 3 groups in para positions the catalytic efficiency improves (compare Ph and Ph(p-CF 3 )). Ph(p-CF 3 ) is the ideal system for predictive catalysis research, because from an experimental point of view Ph(p-CF 3 ) is easily achieved in the lab (vide infra). Steric maps by the SambVca2. 1 package  38  were carried out to see how they affect the groups located in the cyclopentadienone, specifically the phenyls ortho to the keto group, as well as the para substitution thereof. However, Figure  S38  confirms, with the steric maps and percent buried volume (% V Bur ), that no significant difference is observed for the systems. More interestingly, the systems studied in the previous computational work,  27  where substituents were bonded directly to the cyclopentadienone, generated no difference in steric hindrance.
Accession Codes CCDC 2247199-2247202 contain the supplementary crystallographic data for this paper. These data can be obtained free of charge via www. ccdc. cam. ac. uk/data_request/cif, or by emailing data_request@ccdc. cam. ac. uk, or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; fax: +44 1223 336033.
A short historical account of the first 10 years of development of the PPR78 model The development of groups 1 to 21 of the PPR78 model has been described in a series of articles presenting both the methodology for estimating the G-BIPs (i. e. , the A kl and B kl values involved in the E ij expression given in Eq. (  41 )) and the predicted binary phase diagrams, arranged by chemical family (see Table  1 ). To achieve this goal, a comprehensive two-phase fluid equilibrium database was constituted; approximately 90,000 data were collected. Note that only the data deemed to be unquestionably doubtful were left aside to fit model parameters. The work conducted from 2004 to 2009 was aimed essentially to develop groups present in anhydrous petroleum fluids (i. e. , groups 1 to 15). The ability of the PPR78 model to represent the phase envelopes of synthetic petroleum fluids such as natural gases, gas condensates and heavy oils was shown in Jaubert et al.
While phase-equilibrium data used in previous studies devoted to adding a new group to PPR78 were essentially VLE data, many water-containing systems exhibit liquid-liquid phase split, and consequently, a consequent number of LLE data were included in the fitting database. Quantitatively, contrasting results were obtained, and it can be considered that the PPR78 model cannot predict the phase equilibria of aqueous systems in both the subcritical and supercritical domains for both low and high water contents with high precision. From a qualitative point of view, the conclusions are different. Global phase equilibrium diagrams (GPEDs)  [98]  and critical loci in particular were used to evaluate the capacity of the model to reproduce the global phase behaviour of a given system. The GPEDs of aqueous systems modelled by PPR78 show a good qualitative prediction of critical loci and, more generally, of the types of phase behaviours (according to the classification of Van Konynenburg and Scott  [99, 100] ).
Schematically speaking, A kl parameters are used to describe the VLE data, while B kl are used for the calorific data. The redetermination of all G-BIPs over a database including the VLE/ LLE and calorific data now confirmed this insight. It was observed that it is possible to maintain the model accuracy of the PPR78 model in terms of the VLE/LLE and critical data while dramatically improving the prediction of calorific (h M and c M P ) data, as illustrated in Fig. 6. The PPR78 model with refitted parameters was named E-PPR78 (where E stands for enhanced) to emphasize the change in fitting procedure. More details regarding the mutation of PPR78 to E-PPR78 can be found in a recent article  [102]. Since this transition, all G-BIP values of all subsequent groups were estimated systematically on both VLE/LLE and calorific data.
= 780 G-BIPs, characterizing the interactions between the N g = 40 molecular groups defined in the model. Approximately 126 000 VLE/LLE data and 34 000 calorific data were used. On average, E-PPR78 shows a mean absolute percent error of approximately 9% on the VLE/LLE data and approximately 50% on the calorific data (Eqs. 1, 5, 10, 13, 16, 22, 30-31, 36, 39).
Comparison with other EoS approaches In a recent paper, Piña-Martinez et al. [46]  compared different CEoSs following a strict methodology relying on the use of a reference database for model evaluation, gathering 200 binary systems and various types of data (critical, VLE/LLE, VLLE, calorific, etc. )  [104]. Four different models were considered in their study: -The PR-Van Laar model, which is similar to the E-PPR78 model except that the E 12 (T) parameter of the excess function is not estimated from a GC method but from Eq. (  43 ). This is derived from the GC-based expression and is obtained by assuming that each molecule of a binary system 1 + 2 consists of a single molecular group. E 12 (T) = A 12 ( T 0 T ) ( B 12 A 12 -1 ) (43) In the PR-Van Laar model, BIPs A 12 and B 12 are fitted to the experimental data.
Two operating conditions are computed here, namely MRB18b and MRB26b, which correspond to a main jet equivalence ratio of 1. 8 and 2. 6, respectively. For both cases, bulk velocities of the main jet, ''slot 1'' and ''slot 2'' streams are equal to 105 m/s, 15 m/s and 20 m/s, respectively. The high shear between the jet and ''slot 1'' induces fast mixing and the formation of the multi regime characteristics. A nonreactive flow configuration, called COLD, has also been measured under the same operating conditions as MRB18b  [31]. Available experimental data include the velocity field measured from PIV  [31]  and the thermochemical flame structure given by Raman/Rayleigh/CO-LIF diagnostics  [28]. From spatially resolved measurements of temperature, mixture fraction, and major species mass fraction, Butz et al.
10 ) yields better prediction than the premixedbased tabulation for 𝑌 𝐶𝑂 prediction in the outer flame region. This counter intuitive result is explained by two reasons. First, the nonpremixed flame tabulation adopted by Group G  [41, 42]  includes both of the steady and unsteady straining flamelets. The method captures the whole process of the flamelet straining and extinction, and results in a chemical database that is very similar to the premixed flame tabulation, especially when the straining rate is relatively low. Second this method does not employ artificial flame thickening, which biases the CO prediction as discussed previously.
Group 𝐶𝐶 = 𝑇 𝑡 × 𝑛 (2) where T, t, and n are the core time, the physical time, and the number of cells. Being the only code to take advantage of structured connectivity, the PsiPhi code is much more efficient than OpenFoam and Yales 2 (𝐶𝐶 = 28). The cost of simulations C and D is also affected by the use of a compressible code that limits the time step to the acoustic scale. The differences between the other groups may be explained by the different categories of the turbulent combustion model. The efficiencies reached by simulations A, B, and E, which are based on a geometrical description of the turbulent flame propagation, are comparable (𝐶𝐶 = 202; 343; 353). Simulations D and G, which used a presumed PDF formalism, presents a similar CPU cost (𝐶𝐶 = 226; 524). Simulations obtained with the reactor-based approaches (groups C, F and H) are slightly more expensive as they require the local resolution of a detailed chemistry reactor model on each grid cell (𝐶𝐶 = 648; 666; 682).
Acknowledgments We are grateful for the opportunities provided by the TNF workshop (https://tnfworkshop. org). The simulations performed by  EM2C-CNRS  was granted access to the HPC resources from  IDRIS ,  TGCC  and  CINES  under the allocation A0052B10253 made by  GENCI  ( Grand Equipement National de Calcul Intensif)  and HPC resources from the  Mesocentre computing center of CentraleSupelec  and  Ecole Normale Supérieure Paris-Saclay  supported by  CNRS  and  Région Ile-de-France  (http://mesocentre. centralesupelec. fr/). Vincent Moureau  and  Ghislain Lartigue  from  CORIA lab  are acknowledged for providing the YALES2 code. The author from  Jiangsu University  acknowledges the support by the  Natural Science Foundation of China (NSFC)  under Grant No. 91741117. KTH simulations were performed on resources provided by the  National Academic Infrastructure for Supercomputing in Sweden  (NAISS) at NSC and PDC super-computing centers.
The authors from TUDa acknowledge funding by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) - Projektnummer 325144795. Work from  ULB  has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  714605  and  Marie Skłodowska-Curie  grant agreement No  801505. A. Péquin acknowledges the financial support of the  Fonds National de la Recherche Scientifique (FRS-FNRS). J. C. M. and N. S. acknowledge the financial support from  Mitsubishi Heavy Industries, Ltd. ,  Takasago, Japan. Z. L. and N. S. acknowledge the financial support from the  EPSRC  (grant number  EP/S025650/1 ). The work of Z. X. C. is supported by the  NSFC  (grant number  52276096 ) and the  Emerging Interdisciplinary-Young Scholars Project ,  Peking University , the  Fundamental Research Funds for the Central Universities.
Acknowledgments: The authors would like to highly acknowledge the  Higher Education Commission Pakistan  for partial support under the project  NRPU/8350. We also extend our sincere appreciation to the Researchers Supporting Project Number ( RSP2023R79 ) at  King Saud University, Riyadh ,  Saudi Arabia. Additionally ,  Brigitte Vigolog  would like to thank the  Microscopy, Microprobes , and  Metallography  ( 3M ) platform at the  Institut Jean Lamour (IJL, Nancy, France)  for its TEM and SEM facilities. We also acknowledge partial funding of the  Ajman University , Grant ID: DGSR ref. 2022-IRG-HBS-5.
The absolute in-cylinder pressure is obtained by its equalization with the average absolute pressure at the intake, Pin, at CAD after the intake valve opening. The charge duration for the ignition coil was set to 2 ms. For all the data presented below, the Spark Ignition Timing (SIT) was optimized to obtain the maximum Indicated Mean Effective Pressure (IMEP), with a covariance, COV IMEP , below 5%. All gaseous flows except air, are measured and controlled using Brooks thermal mass flowmeters precision to ensure control of the equivalence ratio. The uncertainty for NH 3 , N 2 , CO flow is about ± 0. 9% of the full scale (100 NL/min) and for H 2 , CO 2 , CH 4 it is about ± 1% of the full scale which is respectively 50, 37 and 5 NL/min. For air, an EMERSON F025S was used with an uncertainty of ± 0.
Kinetics simulation setup Numerical simulations were carried out using the premixed laminar aH 2 + bCO + cCH 4 + dCO 2 + eN 2 + ( a 2 + b 2 + 2c + 3 4 x ) (O 2 + 3. 76N 2 ) + xNH 3 → ( a + 2c + 3 2 x ) H 2 O + (b + c + d)CO 2 + ( e + 3. 76 ( a 2 + b 2 + 2c + 3 4 x ) + x 2 ) N 2 (1) flame-speed calculation module PREMIX of ANSYS CHEMKIN-PRO  [36]  to estimate the laminar flame speed and adiabatic flame temperature in engine conditions. Simulations were carried out with an average number of 1000 meshes on a 10 cm grid, a curvature of 0. 1, and a gradient of 0. 05 with 5 continuations. The selected mechanism is the one of Okafor et al. [37]  since it is one of the only ones able to simulate producer gas/ammonia blend. This mechanism is based on the GRI-Mech 3. 0  [38]  for the carbon chemistry, well suited for methane and natural gas and on the mechanism of Tian et al.
Results and Discussion To validate the implementation of reacting flow models in JAGUAR, simulations from 1D to 3D flames have been performed. For some simulations, JAGUAR results are compared with results obtained with the reference solver AVBP  (Schonfeld and Rudgyard 1999; Gourdain et al. 2009 ) developed by CERFACS. AVBP solves exactly the same reacting NSE for a multi-species gas with the same transport model. All AVBP simulations were carried out using the TTGC scheme  (Colin and Rudgyard 2000)  for convective fluxes without artificial viscosity for a fair comparison with JAGUAR. For diffusive fluxes, AVBP uses a finite element scheme of order 2  (Colin and Rudgyard 2000). 1D cases are also compared to the reference chemistry code CANTERA  (Goodwin et al. 2017). In this section, only a 1D premixed flame and a 3D turbulent premixed flame will be presented whereas a 2D laminar burner has also been considered whose results are gathered in Appendix C for completeness.
Presentation of the case The Cambridge burner  (Sweeney et al. 2011a ) is an academic configuration which is composed of two concentric tubes surrounding a central bluff-body as shown on Fig. 8  where burner dimensions and injected streams are illustrated. Inner (indexed i) and outer (indexed o) inlet streams contain CH 4 /Air Fig. 8  Sketch of the Cambridge burner  (Sweeney et al. 2011a ) with associated dimensions and injected streams. The white area corresponds to the computational domain. mixtures that are controlled independently in terms of bulk velocity U b,i/o and equivalence ratio ϕ i/o. The last stream is a co-flow (indexed cf ) of air (ϕ cf = 0) at U b,cf = 0. 4 m. s -1 used to isolate the flame from ambient perturbations. Finally, all injected streams work at T 0 = 298 K. This burner was widely studied experimentally by changing the different levels of CH 4 /Air stratification (variations of ϕ i and ϕ o ) and swirl providing an abundant experimental database of fifteen different configurations denoted from SwB1 to SwB15  (Sweeney et al.