Simultaneous high-speed spectroscopy and 2-color pyrometry analysis in an optical compression ignition engine fueled with OME X -diesel blends

[EN] E-fuels are a very attractive way for improving the well-to-wheel emissions of CO 2 in internal combustion engines. In the particular case of compression ignition engines, the Oxymethylene dimethyl ether (OME X ), an e-fuel with nearly soot-free combustion under mixing-controlled conditions, is a good candidate for the replacement of fossil fuels. However, the Lower Heating Value of OME X is nearly half of the diesel fuel, which means that much longer injection durations are required in the real engine. In addition, the very low viscosity and lubricity of OME X can damage the injection system if used pure, but it can be an interesting fuel when blended with conventional diesel. Thus, the main objective of this paper is to evaluate the potential of OME X -diesel blends to bypass these OME X limitations whilst keeping low soot formation trends. For this purpose, a single cylinder optical diesel engine at part load was employed. The soot production for the different fuel blends was analyzed by applying three different high-speed imaging techniques: natural luminosity, flame spectroscopy and 2-color pyrometry. Natural luminosity analysis showed that the flame light intensity scales with diesel fraction up to 30% of diesel in the blend. The spectroscopy analysis has revealed that soot formation of OME X fuel is almost null. When blended with diesel at 50%, although soot formation is still lower than for pure diesel, higher soot levels are obtained in the last stages of the cycle as a consequence of the longer injections required.This work was partially funded by Generalitat Valenciana through the Programa Santiago Grisola (GRISOLIAP/2018/142) program.Pastor, JV.; García Martínez, A.; Micó, C.; De Vargas Lewiski, F. (2021). Simultaneous high-speed spectroscopy and 2-color pyrometry analysis in an optical compression ignition engine fueled with OME X -diesel blends. Combustion and Flame. 230:1-13. https://doi.org/10.1016/j.combustflame.2021.111437S11323

Uncertainty quantification analysis of the biological Gompertz model subject to random fluctuations in all its parameters

[EN] In spite of its simple formulation via a nonlinear differential equation, the Gompertz model has been widely applied to describe the dynamics of biological and biophysical parts of complex systems (growth of living organisms, number of bacteria, volume of infected cells, etc.). Its parameters or coefficients and the initial condition represent biological quantities (usually, rates and number of individual/particles, respectively) whose nature is random rather than deterministic. In this paper, we present a complete uncertainty quantification analysis of the randomized Gomperz model via the computation of an explicit expression to the first probability density function of its solution stochastic process taking advantage of the Liouville-Gibbs theorem for dynamical systems. The stochastic analysis is completed by computing other important probabilistic information of the model like the distribution of the time until the solution reaches an arbitrary value of specific interest and the stationary distribution of the solution. Finally, we apply all our theoretical findings to two examples, the first of numerical nature and the second to model the dynamics of weight of a species using real data.This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-P.Bevia, V.; Burgos, C.; Cortés, J.; Navarro-Quiles, A.; Villanueva Micó, RJ. (2020). Uncertainty quantification analysis of the biological Gompertz model subject to random fluctuations in all its parameters. Chaos, Solitons and Fractals. 138:1-12. https://doi.org/10.1016/j.chaos.2020.109908S112138Golec, J., & Sathananthan, S. (2003). Stability analysis of a stochastic logistic model. Mathematical and Computer Modelling, 38(5-6), 585-593. doi:10.1016/s0895-7177(03)90029-xCortés, J. C., Jódar, L., & Villafuerte, L. (2009). Random linear-quadratic mathematical models: Computing explicit solutions and applications. Mathematics and Computers in Simulation, 79(7), 2076-2090. doi:10.1016/j.matcom.2008.11.008Dorini, F. A., Cecconello, M. S., & Dorini, L. B. (2016). On the logistic equation subject to uncertainties in the environmental carrying capacity and initial population density. Communications in Nonlinear Science and Numerical Simulation, 33, 160-173. doi:10.1016/j.cnsns.2015.09.009Dorini, F. A., Bobko, N., & Dorini, L. B. (2016). A note on the logistic equation subject to uncertainties in parameters. Computational and Applied Mathematics, 37(2), 1496-1506. doi:10.1007/s40314-016-0409-6Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., & Roselló, M.-D. (2019). Analysis of random non-autonomous logistic-type differential equations via the Karhunen–Loève expansion and the Random Variable Transformation technique. Communications in Nonlinear Science and Numerical Simulation, 72, 121-138. doi:10.1016/j.cnsns.2018.12.013Calatayud, J., Cortés, J. C., & Jornet, M. (2019). Improving the approximation of the probability density function of random nonautonomous logistic‐type differential equations. Mathematical Methods in the Applied Sciences, 42(18), 7259-7267. doi:10.1002/mma.5834Casabán, M.-C., Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., Roselló, M.-D., & Villanueva, R.-J. (2016). Probabilistic solution of the homogeneous Riccati differential equation: A case-study by using linearization and transformation techniques. Journal of Computational and Applied Mathematics, 291, 20-35. doi:10.1016/j.cam.2014.11.028Hesam, S., Nazemi, A. R., & Haghbin, A. (2012). Analytical solution for the Fokker–Planck equation by differential transform method. Scientia Iranica, 19(4), 1140-1145. doi:10.1016/j.scient.2012.06.018Lakestani, M., & Dehghan, M. (2009). Numerical solution of Fokker-Planck equation using the cubic B-spline scaling functions. Numerical Methods for Partial Differential Equations, 25(2), 418-429. doi:10.1002/num.20352Mao, X., Yuan, C., & Yin, G. (2005). Numerical method for stationary distribution of stochastic differential equations with Markovian switching. Journal of Computational and Applied Mathematics, 174(1), 1-27. doi:10.1016/j.cam.2004.03.016Casabán, M.-C., Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., Roselló, M.-D., & Villanueva, R.-J. (2017). Computing probabilistic solutions of the Bernoulli random differential equation. Journal of Computational and Applied Mathematics, 309, 396-407. doi:10.1016/j.cam.2016.02.034Kegan, B., & West, R. W. (2005). Modeling the simple epidemic with deterministic differential equations and random initial conditions. Mathematical Biosciences, 195(2), 179-193. doi:10.1016/j.mbs.2005.02.004Cortés, J.-C., Navarro-Quiles, A., Romero, J.-V., & Roselló, M.-D. (2017). Full solution of random autonomous first-order linear systems of difference equations. Application to construct random phase portrait for planar systems. Applied Mathematics Letters, 68, 150-156. doi:10.1016/j.aml.2016.12.015Cortés, J. C., Navarro‐Quiles, A., Romero, J., & Roselló, M. (2019). (CMMSE2018 paper) Solving the random Pielou logistic equation with the random variable transformation technique: Theory and applications. Mathematical Methods in the Applied Sciences, 42(17), 5708-5717. doi:10.1002/mma.5440Dorini, F. A., & Cunha, M. C. C. (2011). On the linear advection equation subject to random velocity fields. Mathematics and Computers in Simulation, 82(4), 679-690. doi:10.1016/j.matcom.2011.10.008Slama, H., El-Bedwhey, N. A., El-Depsy, A., & Selim, M. M. (2017). Solution of the finite Milne problem in stochastic media with RVT Technique. The European Physical Journal Plus, 132(12). doi:10.1140/epjp/i2017-11763-6Hussein, A., & Selim, M. M. (2013). A general analytical solution for the stochastic Milne problem using Karhunen–Loeve (K–L) expansion. Journal of Quantitative Spectroscopy and Radiative Transfer, 125, 84-92. doi:10.1016/j.jqsrt.2013.03.018Hussein, A., & Selim, M. M. (2019). A complete probabilistic solution for a stochastic Milne problem of radiative transfer using KLE-RVT technique. Journal of Quantitative Spectroscopy and Radiative Transfer, 232, 54-65. doi:10.1016/j.jqsrt.2019.04.034Cortés, J.-C., Jódar, L., Camacho, F., & Villafuerte, L. (2010). Random Airy type differential equations: Mean square exact and numerical solutions. Computers & Mathematics with Applications, 60(5), 1237-1244. doi:10.1016/j.camwa.2010.05.046Bekiryazici, Z., Merdan, M., & Kesemen, T. (2020). Modification of the random differential transformation method and its applications to compartmental models. Communications in Statistics - Theory and Methods, 50(18), 4271-4292. doi:10.1080/03610926.2020.1713372Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2020). Constructing reliable approximations of the probability density function to the random heat PDE via a finite difference scheme. Applied Numerical Mathematics, 151, 413-424. doi:10.1016/j.apnum.2020.01.012Laird, A. K. (1965). Dynamics of Tumour Growth: Comparison of Growth Rates and Extrapolation of Growth Curve to One Cell. British Journal of Cancer, 19(2), 278-291. doi:10.1038/bjc.1965.32Nahashon, S. N., Aggrey, S. E., Adefope, N. A., Amenyenu, A., & Wright, D. (2006). Growth Characteristics of Pearl Gray Guinea Fowl as Predicted by the Richards, Gompertz, and Logistic Models. Poultry Science, 85(2), 359-363. doi:10.1093/ps/85.2.35

An Experimental Study on Diesel Spray Injection into a Non-Quiescent Chamber

[EN] Visualization of single-hole nozzles into quiescent ambient has been used extensively in the literature to characterize spray mixing and combustion. However in-cylinder flow may have some meaningful impact on the spray evolution. In the present work, visualization of direct diesel injection spray under both non-reacting and reacting operating conditions was conducted in an optically accessible two-stroke engine equipped with a single-hole injector. Two different high-speed imaging techniques, Schlieren and UV-Light Absorption, were applied here to quantify vapor penetration for non-reacting spray. Meanwhile, Mie-scattering was used to measure the liquid length. As for reacting conditions, Schlieren and OH* chemiluminescence were simultaneously applied to obtain the spray tip penetration and flame lift-off length under the same TDC density and temperature. Additionally, PIV was used to characterize in-cylinder flow motion. Results were compared with those from the Engine Combustion Network database obtained under quiescent ambient conditions in a high pressure high temperature vessel. Because of the air flow induced by piston movement, in-cylinder conditions in the two-stroke engine during the spray injection are highly unsteady, which has a significant impact on the spray development and interference on the spray visualization. From the comparison with quiescent data from the Engine Combustion Network, air flow induced by piston movement was found to slow down tip penetration. Moreover, both ignition delay and lift-off length under unsteady flow conditions show less sensitivity with ambient temperature than that of quasi-steady conditions.This work was partially funded by the Government of Spain through COMEFF Project (TRA2014-59483-R). In addition, the authors acknowledge that some equipment used in this work has been partially supported by FEDER project funds (FEDER-ICTS-2012-06), framed in the operational program of unique scientific and technical infrastructure of the Ministry of Science and Innovation of Spain. The authors want also to express their gratitude to CONVERGENT SCIENCE Inc for their kind support for this research.Pastor, JV.; García-Oliver, JM.; García Martínez, A.; Zhong, W.; Micó Reche, C.; Xuan, T. (2017). An Experimental Study on Diesel Spray Injection into a Non-Quiescent Chamber. SAE International Journal of Fuel and Lubricants. 10(2):1-13. https://doi.org/10.4271/2017-01-0850S11310

An Optical Engine Used as a Physical Model for Studies of the Combustion Process Applying a Two-Color Pyrometry Technique

[EN] This work describes an experimental installation for the investigation of the combustion and injection processes. This installation is based on a two-stroke direct injection diesel engine with a total displacement of 3 L and a cylinder head equipped with three quartz windows. The windows are optical accesses that allow studying the process of injection, the atomization and evaporation of the fuel jet in an inert atmosphere (nitrogen), and the combustion process in a reactive atmosphere (ambient air). Additionally, the application of a two-color pyrometry technique to measure soot formation in this facility is presented. A methodological study is carried out regarding the influence of the dynamic range of the detectors and the wavelengths used. Maps of KL2C, flame temperature, and error probability are presented. The use of cameras with high dynamic range provides better results since the system seems to be less sensitive to measurement noise, and fewer points are obtained with a non-physical solution. Moreover, an appropriate combination of interference filters can improve the reliability of the solution. The greater the difference between the wavelengths of both interference filters, the fewer points with a non-physical solution, which improves the reliability of results.This research was funded by Castilla-La Mancha Government to the project grant number ASUAV Ref. SBPLY/19/180501/000116.Corral-Gómez, L.; Armas, O.; Soriano, JA.; Pastor, JV.; García-Oliver, JM.; Micó, C. (2022). An Optical Engine Used as a Physical Model for Studies of the Combustion Process Applying a Two-Color Pyrometry Technique. Energies. 15(13):1-17. https://doi.org/10.3390/en15134717117151

Neurons along the auditory pathway exhibit a hierarchical organization of prediction error

Perception is characterized by a reciprocal exchange of predictions and prediction error signals between neural regions. However, the relationship between such sensory mismatch responses and hierarchical predictive processing has not yet been demonstrated at the neuronal level in the auditory pathway. We recorded single-neuron activity from different auditory centers in anaesthetized rats and awake mice while animals were played a sequence of sounds, designed to separate the responses due to prediction error from those due to adaptation effects. Here we report that prediction error is organized hierarchically along the central auditory pathway. These prediction error signals are detectable in subcortical regions and increase as the signals move towards auditory cortex, which in turn demonstrates a large-scale mismatch potential. Finally, the predictive activity of single auditory neurons underlies automatic deviance detection at subcortical levels of processing. These results demonstrate that prediction error is a fundamental component of singly auditory neuron responses

miR-1226 detection in GCF as potential biomarker of chronic periodontitis: a pilot study

The study and identification of new biomarkers for periodontal disease, such as microRNAs (miRNAs), may give us more information about the location and severity of the disease and will serve as a basis for treatment planning and disease-monitoring. miRNAs are a group of small RNAs which are involved in gene regulation by binding to their messenger RNA target (mRNA). In this pilot study, the procedure for purifying miRNAs from gingival crevicular fluid (GCF) was, for the first time, described. In addition, the concentration of miRNAs in GCF was analyzed and compared between patients with moderate or severe chronic periodontitis (CP) and healthy controls. GCF samples were collected from single-rooted teeth of patients with moderate or severe CP (n=9) and of healthy individuals (n=9). miRNAs were isolated from GCF using miRNeasy Serum/Plasma kit (Qiagen, CA. USA). Reverse transcription polymerase chain reaction (qRT-PCR) was used to determine the expression of a series of miRNAs candidates that are related to bone metabolism. The significance of differences in miRNA levels between both groups was determined using Mann-Whitney U test. The results from this pilot study indicate that miRNAs can be isolated from GCF. Six different miRNAs were analyzed (miR-671, miR-122, miR-1306, miR-27a, miR-223, miR-1226), but only miR-1226 showed statically significant differences between the CP group and healthy controls (p<0.05). This miRNA was downregulated in patients with CP. Within the limitations of the present study, it may be concluded that miR-1226 can be a promising biomarker for periodontal disease, adding relevant information to common clinical parameters used for diagnosis and prognosis of periodontitis

Modelling 1-month euribor interest rate by using differential equations with uncertainty

[EN] This paper deals with modelling interest rate using continuous models with uncertainty based on Itô-type stochastic differential equations. It is provided an analysis of theoretical aspects that involves the so-called Vasicek s model as well as their practical application. The latter includes model parameter fitting and measurement of goodness-of-fit of the model. The theoretical results are applied to modelling 1-month Euribor interest rate.This work has been partially supported by the Ministerio de Economía y Competitividad grant MTM2013-41765-P.Cortés, J.; Romero, J.; Sánchez Sánchez, A.; Villanueva Micó, RJ. (2015). Modelling 1-month euribor interest rate by using differential equations with uncertainty. Applied Mathematical and Computational Sciences. 7(3):37-50. http://hdl.handle.net/10251/70015S37507

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Re-ranking Permutation-Based Candidate Sets with the n-Simplex Projection

In the realm of metric search, the permutation-based approaches have shown very good performance in indexing and supporting approximate search on large databases. These methods embed the metric objects into a permutation space where candidate results to a given query can be efficiently identified. Typically, to achieve high effectiveness, the permutation-based result set is refined by directly comparing each candidate object to the query one. Therefore, one drawback of these approaches is that the original dataset needs to be stored and then accessed during the refining step. We propose a refining approach based on a metric embedding, called n-Simplex projection, that can be used on metric spaces meeting the n-point property. The n-Simplex projection provides upper- and lower-bounds of the actual distance, derived using the distances between the data objects and a finite set of pivots. We propose to reuse the distances computed for building the data permutations to derive these bounds and we show how to use them to improve the permutation-based results. Our approach is particularly advantageous for all the cases in which the traditional refining step is too costly, e.g. very large dataset or very expensive metric function