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It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise. The CFD model was specifically set up to reproduce the unsteady pressure field inside the combustion chamber, thereby allowing an accurate prediction of the acoustic response of the combustion phenomena. The model was validated by simulating several steady operating conditions and comparing the numerical results against experimental data, in both temporal and frequency domains. Thereafter, a GA optimization was performed with the goal of minimizing indicated specific fuel consumption (ISFC) and combustion noise, while restricting pollutant (soot and NOₓ) emissions to their respective baseline values. Eight design variables were selected pertaining to piston bowl geometry, nozzle inclusion angle, number of injector nozzle holes, and in-cylinder swirl. An objective merit function (MF) based on the emissions, ISFC, and combustion noise was constructed to quantify the strength of the engine designs and was determined using the CFD model as the function evaluator. The in-cylinder noise level was characterized by the total resonance energy of local pressure oscillations. The optimum engine configuration thus obtained showed a significant improvement in terms of efficiency and combustion noise compared to the baseline system, along with both soot and NOₓ emissions within their respective constraints. This optimum configuration included a deeper and tighter bowl geometry with higher swirl and larger number of nozzle holes. Subsequently, a more detailed acoustics analysis based on proper orthogonal decomposition (POD) technique was carried out to further explore the combustion noise benefits achieved by the GA optimum. This computational study is a first of its kind (to the best of the authors’ knowledge), which demonstrates a comprehensive framework to incorporate combustion noise into a numerical optimization strategy for engine design.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC). The outputs (targets) of interest from these simulations included five metrics related to engine performance and emissions. Over 2000 samples generated from CFD were then used to train an ML model that could predict these five targets based on the nine input features. A robust super learner approach was employed to build the ML model, where results from a collection of different ML algorithms were pooled together. Thereafter, a stochastic global optimization genetic algorithm (GA) was used, with the ML model as the objective function, to optimize the input parameters based on a merit function so as to minimize fuel consumption while satisfying CO and NOx emissions constraints. The optimized configuration from ML-GA was found to be very close to that obtained from a sequentially performed CFD-GA approach, where a CFD simulation served as the objective function. In addition, the overall turnaround time was (at least) 75% lower with the ML-GA approach, as the training data was generated from concurrent CFD simulations and employing the ML model as the objective function significantly accelerated the GA optimization. This study demonstrates the potential of ML-GA and high-performance computing (HPC) to reduce the number of CFD simulations to be performed for optimization problems without loss in accuracy, thereby providing significant cost savings compared to traditional approaches.

Plankton blooms are complex nonlinear phenomena whose occurrence can be described by the two-timescale (fast-slow) phytoplankton-zooplankton model introduced by Truscott and Brindley (Bulletin of Mathematical Biology 56(5):981–998, 1994). In their work, they observed that a sufficiently fast rise of the water temperature causes a critical transition from a low phytoplankton concentration to a single outburst: a so-called plankton bloom. However, the dynamical mechanism responsible for the observed transition has not been identified to the present day. Using techniques from geometric singular perturbation theory, we uncover the formerly overlooked rate-sensitive quasithreshold which is given by special trajectories called canards. The transition from low to high concentrations occurs when this rate-sensitive quasithreshold moves past the current state of the plankton system at some narrow critical range of warming rates. In this way, we identify rate-induced tipping as the underlying dynamical mechanism of largely unpredictable plankton blooms such as red tides, or more general, harmful algal blooms. Our findings explain the previously reported transitions to a single plankton bloom, and allow us to predict a new type of transition to a sequence of blooms for higher rates of warming. This could provide a possible mechanism of the observed increased frequency of harmful algal blooms.

TiO 2 inks were prepared with 1 wt.% methyl cellulose as binder. The prepared inks contained 35, 50, 65, 70 and 72 wt. % solid loading of TiO 2 . The rheological characteristics were studied by rheometer. Significant effect of temperature and solid loading of TiO 2 was observed in the viscosity of the inks. The viscosity of the inks increased with increased solid loading. Also, it was observed that the onset of gelation temperature of inks decreased with increase in solid loading. The viscosity of the inks decreased with increasing shear rate showing the shear thinning behavior suitable for direct ink writing. The yield stress of methyl cellulose-TiO 2 inks was found to increase with increasing solid content of TiO 2 . Sintering of TiO 2 was caried out at 1250 °C and 1350 °C for 180 min. The microstructure showed a variation from porous structure to dense structure with increase in solid loading of TiO 2 .

Knock is a major bottleneck to achieving higher thermal efficiency in spark ignition (SI) engines. The overall tendency to knock is highly dependent on fuel anti-knock quality as well as engine operating conditions. It is, therefore, critical to gain a better understanding of fuel-engine interactions in order to develop robust knock mitigation strategies. In the present work, a numerical model based on three-dimensional (3-D) computational fluid dynamics (CFD) was developed to capture knock in a Cooperative Fuel Research (CFR) engine. For combustion modeling, a hybrid approach incorporating the G-equation model to track turbulent flame propagation, and a homogeneous reactor multi-zone model to predict end-gas auto-ignition ahead of the flame front and post-flame oxidation in the burned zone, was employed. In addition, a hybrid methodology was implemented wherein a laminar flame speed lookup table generated a priori from a chemical kinetic mechanism could be used to provide flame speed as an input to the G-equation model, instead of using conventional empirical correlations. Multi-cycle Reynolds-averaged Navier-Stokes (RANS) simulations were performed for two different spark timings (STs) corresponding to non-knocking and knocking conditions, with other operating conditions kept the same as those of a standard research octane number (RON) test. Isooctane was considered as the fuel for the numerical study. Two different reduced kinetic mechanisms were employed to describe end-gas auto-ignition chemistry and to generate the flame speed lookup table. Experimental data, including intake/exhaust boundary conditions, was provided by an isooctane ST sweep study conducted in an in-house CFR engine. Moreover, cylinder wall/valve/port surface temperatures and residual gas fraction (RGF) were estimated using a well-calibrated one-dimensional (1-D) model. On the other hand, a novel methodology was also developed to analyze experimental data for the knocking case and identify the most representative cycle. For the non-knocking case, a good agreement was found between experiment and CFD simulation, with respect to cycle-averaged values of 10% burn point (CA10), 50% burn point (CA50), and peak pressure magnitude/location. The virtual CFR engine model was also demonstrated to be capable of predicting average knock characteristics for the knocking case, such as knock point, knock intensity, and energy of resonance, with good accuracy.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels. To aid the design and optimization of a compression ignition (CI) combustion system using such fuels, a global sensitivity analysis (GSA) was conducted to understand the relative influence of various design parameters on efficiency, emissions and heat release rate. The design parameters included injection strategies, exhaust gas recirculation (EGR) fraction, temperature and pressure at intake valve closure and injector configuration. These were varied simultaneously to achieve various targets of ignition timing, combustion phasing, overall burn duration, emissions, fuel consumption, peak cylinder pressure and maximum pressure rise rate. The baseline case was a three-dimensional closed-cycle computational fluid dynamics (CFD) simulation with a sector mesh at medium load conditions. Eleven design parameters were considered and ranges of variation were prescribed to each of these. These input variables were perturbed in their respective ranges using the Monte Carlo (MC) method to generate a set of 256 CFD simulations and the targets were calculated from the simulation results. GSA was then applied as a screening tool to identify the input parameters having the most significant impact on each target. The results were further assessed by investigating the impact of individual parameter variations on the targets. Overall, it was demonstrated that GSA can be an effective tool in understanding parameters sensitive to a low temperature combustion concept with novel fuels.

The results from the measurements of some of the fundamental parameters (amplitude of sferics and transmitted signal, conductivity of lower ionosphere) of the ionospheric responses to the 22 July 2009 solar eclipse (partial: 91.7%) are shown. This study summarizes our results from sferics signals at 81 kHz and subionospheric transmitted signals at 19.8 and 40 kHz recorded at Agartala, Tripura (latitude: 23°N, longitude: 91.4°E). We observed significant absorption in amplitude of these signals during the eclipse period compared to their ambient values for the same period during the adjacent 7 days. The signal strength along their propagation paths was controlled by the eclipse associated decrease in ionization in the D-region of the ionosphere. Waveguide mode theory calculations show that the elevation of the height of lower ionosphere boundary of the Earth-ionosphere waveguide to a value where the conductivity parameter was 106 unit. The absorption in 81 kHz sferics amplitude is high compared to the absorption in the amplitude of 40 kHz signal transmitted from Japan. The simultaneous changes in the amplitudes of sferics and in the amplitude of transmitted signals assert some sort of coupling between the upper atmosphere and the Earth's near-surface atmosphere prevailing clouds during solar eclipse.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions. The second DoE campaign was conducted to optimize injector spray patterns, fuel injection strategies and in-cylinder swirl motion for the best performing piston bowl designs from the first DoE campaign. This comprehensive optimization study was performed on a supercomputer, Mira, to accelerate the development of an optimized fuel-efficiency focused design. Compared to the production combustion system in the baseline engine, the new combustion recipe from this study showed significantly improved closed-cycle fuel efficiency across key engine operating points while meeting the engine-out NOx targets. Optimized piston bowl designs and injector spray patterns were predicted to provide enhanced in-cylinder air utilization and more rapid mixing-controlled combustion, thereby leading to a fuel efficiency improvement. In addition, shifting the engine thermal boundary conditions toward leaner operation was also key to the improved fuel efficiency.

Combustion in conventional and advanced diesel engines is an intricate process that encompasses interaction among fuel injection, fuel-air mixing, combustion, heat transfer, and engine geometry. Manipulation of fuel injection strategies has been recognized as a promising approach for optimizing diesel engine combustion. Although numerous studies have investigated this topic, the underlying physics behind flame interactions from multiple fuel injections, spray-flame-wall interaction and their effects on reaction zones, and NOx/soot emissions are still not well understood. To this end, a computational fluid dynamics (CFD) study is performed to investigate the effects of pilot and post injections on in-cylinder combustion process and emissions (NOx and soot) formation in a heavy-duty (HD) diesel engine. A full-sector CFD model of the HD engine employing detailed chemistry is validated against experimental data for in-cylinder pressure, heat release rate, combustion phasing, and engine-out NOx/soot and carbon dioxide (CO2) emissions at five load points. The validated CFD model is further leveraged to gain insights into the complex pilot-main and main-post injection interactions at low load (20%) and mid load (60%) conditions, respectively. The 20% load point consists of four fuel injections (two pilots, one main and one post injection), whereas 60% load point has three injections (one pilot, one main and one post). It is observed that pilot injections significantly alter the main flame structure by shifting reaction zones contributing to heat release from combined rich premixed + non-premixed + lean premixed zones to primarily non-premixed zones. Presence of pilot injection decreases NOx concentration (while shifting the contribution of NO2 towards NOx from 50% to 14%) and increases soot concentration. The local consumption of oxygen and less time available for main fuel-air mixing due to reduction in ignition delay (ID) caused by the pilot injection are the major reasons behind increase in soot. The investigation on post injection reveals that although post injection increases soot formation, it also increases soot oxidation, with soot oxidation dominating soot formation. This results in an overall reduction in soot emissions. Hydroxyl (OH) radicals play an important role in enhancing the soot oxidation rate. Furthermore, as the post start-of-injection (SOI) timing is retarded, both soot formation and oxidation decrease, with an overall increase in net soot emissions.

We present a design of coupling to achieve targeted synchronization in two parameter mismatched chaotic discrete dynamical systems. The coupling design is of open-plus-closed-loop type for which a suitable stability criterion is derived. Numerically the proposed coupling design is illustrated using the 1D logistic map, 2D and 3D Henon maps. Experimental realization of the targeted coherent dynamics is presented using 1D logistic map.