Literatura científica selecionada sobre o tema "Cycle skipping"

In this study, cycle-skipping was investigated for a natural gas engine which has single cylinder, unsupercharged with 1.16 L volume and spark ignition. Additionally, inlet manifold air was switched off during cycle-skipping to minimize pumping losses. Thus, cycle-skipping strategy was carried out, and its effects on emission and engine performance were investigated. Indicated mean effective pressure, indicated efficiency, specific emissions (CO, HC, and NOX) and combustion characteristics (in-cylinder pressure and rate of heat release) were investigated in the study. As a result of performed study, it is predicted that a significant improvement can be achieved in indicated thermal efficiency as 22.8% and 13.4% by different cycle-skipping strategies. However, there is not a continuous change in emissions for different cycle-skipping strategies. While CO and NOX emissions increased in 3N1S (three normal, one cycle-skip) condition, HC emissions decreased in accordance with normal condition. For both cycle-skipping strategies, all the emissions have an increase in accordance with normal condition. In 3N1S and 2N1S (two normal, one cycle-skip) cycle skip engine operating conditions, compared to engine operating under normal condition, CO emissions increased by 14.7 and 51.7 times, respectively. In terms of HC emissions, while emission values decreased by 27.8% under 3N1S operating conditions, they increased by 67.2% under 2N1S operating conditions. Finally, in 3N1S and 2N1S cycle skip engine operating conditions, NOx emissions increased by 3.7 and 6.9 times, respectively, compared to normal operating condition. Another significant result of this study is that peak in-cylinder pressure increased as the cycle-skipping rate increased.

Source signature estimation from seismic data is a crucial ingredient for successful application of seismic migration and full-waveform inversion (FWI). If the starting velocity deviates from the target velocity, FWI method with on-the-fly source estimation may fail due to the cycle-skipping problem. We have developed a source-based extended waveform inversion method, by introducing additional parameters in the source function, to solve the FWI problem without the source signature as a priori. Specifically, we allow the point source function to be dependent on spatial and time variables. In this way, we can easily construct an extended source function to fit the recorded data by solving a source matching subproblem; hence, it is less prone to cycle skipping. A novel source focusing annihilator, defined as the distance function from the real source position, is used for penalizing the defocused energy in the extended source function. A close data fit avoiding the cycle-skipping problem effectively makes the new method less likely to suffer from local minima, which does not require extreme low-frequency signals in the data. Numerical experiments confirm that our method can mitigate cycle skipping in FWI and is robust against random noise.

SUMMARYThis paper investigates the efficiency and properties of limit cycle walking, running, and skipping of a planar, active, telescopic-legged rimless wheel. First, we develop the robot equations of motion and design an output following control for the telescopic-legs' action. We then numerically show that a stable walking gait can be generated by asymmetrizing the impact posture. Second, we numerically show that a stable running gait can be generated by employing a simple feedback control of the control period, and compare the properties of the generated running gait with those of the walking gait. Furthermore, we find out another underlying gait called skipping that emerges as an extension of the walking gait. Through numerical analysis, we show that the generated skipping gaits are inherently stable and are less efficient than the other two gaits.

Full-waveform inversion (FWI) is a promising technique for recovering the earth models for exploration geophysics and global seismology. FWI is generally formulated as the minimization of an objective function, defined as the L2-norm of the data residuals. The nonconvex nature of this objective function is one of the main obstacles for the successful application of FWI. A key manifestation of this nonconvexity is cycle skipping, which happens if the predicted data are more than half a cycle away from the recorded data. We have developed the concept of intermediate data for tackling cycle skipping. This intermediate data set is created to sit between predicted and recorded data, and it is less than half a cycle away from the predicted data. Inverting the intermediate data rather than the cycle-skipped recorded data can then circumvent cycle skipping. We applied this concept to invert cycle-skipped first arrivals. First, we picked up the first breaks of the predicted data and the recorded data. Second, we linearly scaled down the time difference between the two first breaks of each shot into a series of time shifts, the maximum of which was less than half a cycle, for each trace in this shot. Third, we moved the predicted data with the corresponding time shifts to create the intermediate data. Finally, we inverted the intermediate data rather than the recorded data. Because the intermediate data are not cycle-skipped and contain the traveltime information of the recorded data, FWI with intermediate data updates the background velocity model in the correct direction. Thus, it produces a background velocity model accurate enough for carrying out conventional FWI to rebuild the intermediate- and short-wavelength components of the velocity model. Our numerical examples using synthetic data validate the intermediate-data concept for tackling cycle skipping and demonstrate its effectiveness for the application to first arrivals.

AbstractThe energetic costs of ovarian functions rely on the oxidizable fuels synthesized from carbohydrate, protein and fat that contribute to body’s fat storage. Energy deficient diet in association with low body fat may disrupt normal ovulatory function and lead to several menstrual irregularities. We examined the association of nutritional status with menstrual characteristics among a group of adolescent Oraon tribal population of West Bengal, India. We selected 301 adolescent girls, aged 10-19 years. Information on socio-demographic status, menstrual characteristics and assessment of the dietary intake and nutritional status were collected following standard protocol. ‘Healthy weight’ participants more likely reported irregularity in periods and skipping of menstrual cycle and shorter cycle length. Multivariate analysis revealed PBF has inverse association with PMS, duration of discharge and skipping of cycle (p<0.05); carbohydrate intake has direct association with duration of menstrual discharge (p<0.05); increased dietary fat intake has direct association with skipping of cycle, but not with BMI (p<0.05); increase in MUAC has direct association with dysmenorrhoea (p<0.05). Conclusion: Our study indicates energy deficiency does alter the menstrual characteristics of the Oraon adolescent girls.

Elastic full waveform inversion (EFWI) is essential for obtaining high-resolution multi-parameter models. However, the conventional EFWI may suffer from severe cycle skipping without the low-frequency components in elastic seismic data. To solve this problem, we propose a multistage phase correction-based elastic full waveform inversion method in the frequency-wavenumber domain, which we call PC-EFWI for short. Specifically, the seismic data are first split using 2-D sliding windows; for each window, the seismic data are then transformed into the frequency-wavenumber domain for PC-EFWI misfit. In addition, we introduced a phase correction factor in the PC-EFWI misfit. In this way, it is possible to reduce phase differences between measured and synthetic data to mitigate cycle skipping by adjusting the phase correction factor in different scales. Numerical examples with the 2-D Marmousi model demonstrate that the frequency-wavenumber domain PC-EFWI with multistage strategy is an excellent way to reduce the risk of EFWI cycle skipping and build satisfactory start models for the conventional EFWI.

In this study, a single cylinder of 1.16 L, naturally aspirated engine was converted to a spark ignition engine, which was a diesel engine operating with natural gas as fuel. By placing electronic throttle, electronic ignition module, gas fuel injectors and proximity sensors on the test engine, the engine has been turned into a positive ignition engine that can work with natural gas as fuel, thanks to the electronic control unit developed by our project team. Then, in the study performed, different cycle skipping strategies were experimentally investigated at a constant engine speed of 1565 rpm, in accordance with the generator operating conditions. Engine performance, emissions (CO, HC, and NOx), and combustion characteristics (cylinder pressure, rate of heat release, etc.) of cycle skipping strategies were experimentally investigated with natural gas as fuel in Normal, 3N1S, 2N1S, and 1N1S engine operating modes. According to the results obtained, specific fuel consumption, CO and HC values improved in all cycle skipping operating conditions, except for NOx, but the best results were obtained in 2N1S operating conditions; it was concluded that the specific fuel consumption, CO and HC values improved by 11.19%, 61.89%, and 65.60%, respectively.

Full-waveform inversion (FWI) has become the tool of choice for building high-resolution velocity models. Its success depends on producing seamless updates of the short- and long-wavelength model features while avoiding cycle skipping. Classic FWI implementations use the L2 norm to measure the data misfit in combination with a gradient computed by a crosscorrelation imaging condition of the source and residual wavefields. The algorithm risks converging to an inaccurate result if the data lack low frequencies and/or the initial model is far from the true one. Additionally, the model updates may display a reflectivity imprint before the long-wavelength features of the model are fully recovered. We propose a new solution to this fundamental challenge by combining the quadratic form of the Wasserstein distance (W2 norm) for measuring the data misfit with a robust implementation of a velocity gradient. The W2 norm reduces the risk of cycle skipping, whereas the velocity gradient effectively eliminates the reflectivity imprint and emphasizes the long-wavelength model updates. We illustrate the performance of the new solution on a field survey acquired offshore Brazil. We demonstrate how FWI successfully updates the earth model and resolves a high-velocity carbonate section missing from the initial velocity model.

In reflection seismology, full-waveform inversion (FWI) can generate high-wavenumber subsurface velocity models but often suffers from an objective function with local minima caused mainly by the absence of low frequencies in seismograms. These local minima cause cycle skipping when the low-wavenumber component in the initial velocity model for FWI is far from the true model. To avoid cycle skipping, we discovered a new wave-equation reflection traveltime inversion (WERTI) to update the low-wavenumber component of the velocity model, while using FWI to only update high-wavenumber details of the model. We implemented the low- and high-wavenumber inversions in an alternating way. In WERTI, we used dynamic image warping (DIW) to estimate the time shifts between recorded data and synthetic data. When compared with correlation-based techniques often used in traveltime estimation, DIW can avoid cycle skipping and estimate the time shifts accurately, even when shifts vary rapidly. Hence, by minimizing traveltime shifts estimated by dynamic warping, WERTI reduces errors in reflection traveltime inversion. Then, conventional FWI uses the low-wavenumber component estimated by WERTI as a new initial model and thereby refines the model with high-wavenumber details. The alternating combination of WERTI and FWI mitigates the velocity-depth ambiguity and can recover subsurface velocities using only high-frequency reflection data.

La Full waveform inversion (FWI) est devenue la méthode d'imagerie de référence en exploration géophysique. FWI utilise les formes d'ondes complètes pour imager le sous-sol avec une résolution d'un demi longueur d'onde. Etant donné la dimension de l'espace des données et des modèles, la FWI est implémentée avec des méthodes d'optimisation locale sur un espace de recherche réduit où l'équation d'onde est résolue exactement à chaque itération. Cela requiert des modèles initiaux précis pour que les données simulées prédisent les données enregistrées sans saut de phase. Pour relâcher cette condition, plusieurs variantes de la FWI ont été proposées telles que les approches sur des espaces de recherche étendus. Parmi ces approches, la 'Wavefield Reconstruction Inversion (WRI)' implémente l'équation d'onde comme une contrainte faible pour ajuster les observables avec des champs d'onde calculées avec des sources étendues. Les extensions de sources sont estimées en ajustant au sens des moindres carrés la différence entre les données enregistrées et simulées traitées comme les données diffractées enregistrées. Il en résulte que ces sources volumiques sont calculées par renversement temporel (retro-propagation) des résidus déconvolués par le Hessien dans l'espace des données. Cette approche est nommée 'extended-source' FWI (ES-FWI).Dans cette thèse, je développe un algorithme opérationnel pour la ES-FWI. Le premier problème est le calcul des sources volumiques où la déconvolution des résidus par le Hessien est coûteuse. Des études précédentes approximent ce Hessien avec une matrice diagonale ce qui peut suffire dans des contextes favorables mais sujet à des minimums secondaires dans des milieux complexes. Je propose d'approximer l'inverse du Hessien par des filtres de Wiener/Gabor. Des tests numériques sur le modèle Marmousi II démontrent les améliorations apportées par ces filtres comparativement à l'approximation diagonale. Les champs d'ondes calculés avec l'assimilation des données ont une précision qui diminue loin des points de mesure ce qui peut piéger l'inversion dans des minimums secondaires. Pour améliorer la robustesse de la méthode, j'ai implémenté des opérateurs de pondération dans l'espace des données pour injecter progressivement des donnés plus complexes dans l'inversion et reconstruire le milieu de la surface vers les niveaux profonds. Cette approche de 'layer stripping' est illustrée avec les géomodèles complexes 2004 BP Salt et GO3DOBS.La ES-FWI est une forme généralisée de la FWI où l'inverse du Hessien du problème de source est utilisé comme une matrice de pondération dans l'espace des données. Cela engendre une décomposition du Hessien en un opérateur diagonal dans le domaine des sources et un opérateur par source dans l'espace des données représentant le Hessien du problème de source évoqué ci dessus. Je montre comment ré-utiliser cette décomposition dans la FWI pour développer une approximation du Hessien Gauss-Newton qui puisse être calculée efficacement tout en accélérant la convergence de la FWI. Alternativement, l'approximation proposée peut être utilisée comme préconditioneur pour des algorithmes de quasi-Newton.Finalement, j'étends l'application de la reconstruction des champs d'onde avec assimilation des données au problème de ‘redatuming'. Cette application requiert des champs d'ondes de haute précision si bien que j'implémente la déconvolution des données diffractées avec le solveur itératif MINRES plutôt qu'avec des filtres de Gabor. L'approche consiste simplement à calculer les champs d'onde avec l'assimilation des données et à les échantillonner sur la surface d'acquisition virtuelle. Cette approche est précise lorsqu'on connaît le milieu situé entre les surfaces définies par les acquisitions réelle et virtuelle. Le ‘redatuming' des sources et des capteurs peuvent être couplés. Cette approche est illustrée avec des géomodèles marins et terrestres et avec un jeu de données réels de fond de mer
Full waveform inversion (FWI) has emerged as the baseline seismic imaging method in exploration geophysics. Given the size of the data and model spaces, FWI relies on iterative local optimization methods and reduced search space where the wave equation is strictly satisfied at each iteration. This framework requires an accurate initial model allowing for the simulated data to match the recorded data with kinematic errors less than half the period to avoid cycle skipping. To mitigate cycle skipping, several variants of FWI have been developed over the last decade such as extended-space FWI where degrees of freedom are added to the forward problem. Among them, the wavefield reconstruction inversion (WRI) implements the wave equation as a soft constraint to match the data by combining a wave-equation relaxation with data assimilation. While WRI has been initially implemented in the frequency domain where the data-assimilated wavefields can be computed with linear algebra methods, the time-domain implementation with explicit time-marching schemes has proven challenging. It was recently recognized that the source extensions generated by the wave-equation relaxation are the least-squares solutions of the scattered-data fitting problem. As such, they are computed by backward modeling of deconvolved FWI data residuals by the data-domain Hessian. This reformulation of the wavefield reconstruction as a scattering source reconstruction has led to the extended-source FWI (ES-FWI).In this thesis, I develop a practical algorithm for ES-FWI. Firstly, I focus on the efficient computation of the source extensions where the deconvolution of the data residuals by the data-domain Hessian is the main computational bottleneck. Previous studies implement the Hessian with a scaled identity matrix, which is acceptable in certain favorable scenarios but prone to failure in complex media. I propose a more accurate approximation of the inverse Hessian with various matching filters such as 1D/2D Wiener and Gabor filters. Numerical tests conducted on the Marmousi II model show the relevance of these approximations. Moreover, the data-assimilated wavefields primarily consist of the ‘migration/demigration' of the recorded data. Accordingly, their accuracy diminishes away from the receivers, which can drive the inversion towards spurious minima in particular when surface multiples are involved in the inversion. To address this issue, I design a weighting operator based on time-offset windowing in the data misfit function to inject progressively more complex data in the inversion and reconstruct the medium from the shallow parts to the deep ones. The application of the BPsalt model illustrates the relevance of this layer-stripping scheme in a very challenging context.ES-FWI can be recast as a generalized FWI, where the data misfit function is weighted by the inverse data-domain Hessian of the source extension problem. This leads to a decomposition of the Gauss-Newton (GN) Hessian into a diagonal source-side Hessian and source-dependent receiver-side data-domain Hessians. I use this decomposition to propose a computationally efficient approximation of the GN Hessian. I approximate the inverse Hessian with 2D Gabor matching filters, which can be readily used as an approximation of the GN Hessian or as a preconditioner for the quasi-Newton method. Numerical tests demonstrate the improved convergence speed of FWI provided by this Hessian.Finally, I extend the application of the data-assimilated wavefield reconstruction towards seismic redatuming, where highly-accurate wavefield reconstruction is necessary. This prompts me to use the iterative solver to perform the deconvolution of the scattered data. Using reciprocity, I can chain source and receiver redatuming. Numerical tests and application to ocean-bottom seismic data validate the effectiveness of the proposed method

AbstractThe breeding efforts of the twentieth century contributed to large increases in yield but selection may have increased vulnerability to environmental perturbations. In that context, there is a growing demand for methodology to re-introduce useful variation into cultivated germplasm. Such efforts can focus on the introduction of specific traits monitored through diagnostic molecular markers identified by QTL/association mapping or selection signature screening. A combined approach is to increase the global diversity of a crop without targeting any particular trait.A considerable portion of the genetic diversity is conserved in genebanks. However, benefits of genetic resources (GRs) in terms of favorable alleles have to be weighed against unfavorable traits being introduced along. In order to facilitate utilization of GR, core collections are being identified and progressively characterized at the phenotypic and genomic levels. High-throughput genotyping and sequencing technologies allow to build prediction models that can estimate the genetic value of an entire genotyped collection. In a pre-breeding program, predictions can accelerate recurrent selection using rapid cycles in greenhouses by skipping some phenotyping steps. In a breeding program, reduced phenotyping characterization allows to increase the number of tested parents and crosses (and global genetic variance) for a fixed budget. Finally, the whole cross design can be optimized using progeny variance predictions to maximize short-term genetic gain or long-term genetic gain by constraining a minimum level of diversity in the germplasm. There is also a potential to further increase the accuracy of genomic predictions by taking into account genotype by environment interactions, integrating additional layers of omics and environmental information.Here, we aim to review some relevant concepts in population genomics together with recent advances in quantitative genetics in order to discuss how the combination of both disciplines can facilitate the use of genetic diversity in plant (pre) breeding programs.

In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy - ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications.