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Renewable MCCI bio-blendstocks with advantageous properties co-optimized with engines and a ducted fuel injection could reduce engine-out emissions leading to reduced total cost of vehicle ownership and a potential to penetrate the market at scale.
To reduce the modeling burden for control of spark-ignition engines, reinforcement learning (RL) has been applied to solve the dilute combustion limit problem. Q-learning was used to identify an optimal control policy to adjust the fuel injection quantity in each combustion cycle. A physics-based model was used to determine the relevant states of the system used for training the control policy in a data-efficient manner. The cost function was chosen such that high cycle-to-cycle variability (CCV) at the dilute limit was minimized while maintaining stoichiometric combustion as much as possible. Experimental results demonstrated a reduction of CCV after the training period with slightly lean combustion, contributing to a net increase in fuel conversion efficiency of 1.33%. To ensure stoichiometric combustion for three-way catalyst compatibility, a second feedback loop based on an exhaust oxygen sensor was incorporated into the fuel quantity controller using a slow proportional-integral (PI) controller. The closed-loop experiments showed that both feedback loops can cooperate effectively, maintaining stoichiometric combustion while reducing combustion CCV and increasing fuel conversion efficiency by 1.09%. Finally, a modified cost function was proposed to ensure stoichiometric combustion with a single controller. In addition, the learning period was shortened by half to evaluate the RL algorithm performance on limited training time. Experimental results showed that the modified cost function could achieve the desired CCV targets, however, the learning time was reduced by half and the fuel conversion efficiency increased only by 0.30%.
This paper presents a three-dimensional (3-D) computational fluid dynamics (CFD) study of a motored cooperative fuel research (CFR) engine at research octane number (RON) relevant condition. The boundary conditions for 3-D simulations were generated with a one-dimensional GT-Power model. For the first time in literature, a carburetor was added to a virtual CFR engine model with 3-D CFD. Therefore, the proposed setup can simulate the fuel and thermal stratifications inside the engine cylinder with realistic detail. The transient simulations in this work were performed within the Reynolds-averaged Navier-Stokes (RANS) framework with a Realizable k-ε turbulence model. Major conclusions from the present work are: (1) The in-cylinder flow of the CFR engine is swirl-dominated due to the existence of the intake valve shroud. (2) There is a significant amount of liquid droplets entering the cylinder during the intake stroke. The maximum instantaneous amount of liquid for 50% PRF 87 (containing 87% iso-octane and 13% n-heptane (v/v)) and 50% ethanol mixture is indicated to be around 26% of total injected fuel mass. (3) The heat of vaporization (HoV) of the fuel is responsible for creating both temperature and charge stratification inside the cylinder.
The goal of the Co-Optima program was to accelerate deployment of co-optimized fuels and engines that will reduce fuel consumption, and criterion pollutants and greenhouse gas (GHG) emissions for both spark and compression-ignited engines. In this project, combustion measurements from a constant pressure flow rig (CPFR) with optical access were used to assess fundamental combustion behavior of number of different candidate fuels and fuel blends. This chamber and its peripheral systems allowed stable control of ambient air pressure and temperature and fuel injection pressure and timing for hundreds of repeat injections allowing statistical interpretation of the results. The optical diagnostics enabled capturing of liquid length, vapor length, ignition timing, lift-off length as well as localized (temporal and spatially resolved) mixture refractive index, flame structure, and soot mass. The operating conditions allowed the study of combustion conditions promoting lean lifted flame combustion (LLFC), a mixing-controlled low-temperature combustion (LTC) strategy to reduce soot production in Advanced Compression Ignition (ACI) engines.
Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition (SI) engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit. Given the complexity and chaotic nature of combustion events, the controller was enhanced with online learning in order to identify the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events. This study showed that a kernel density estimator (KDE) can be used to learn the combustion properties in real time and can be incorporated into the feedback policy in order to calculate the optimal control command. Experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. Additionally, the proposed controller can achieve a large CCV reduction with less fuel enrichment compared to previous methods, overall contributing to an increase in 0.2% indicated fuel conversion efficiency.
Partial fuel stratification (PFS) is a promising fuel injection strategy to improve the stability of lean combustion by applying a small amount of pilot injection right before spark timing. Mixed-mode combustion, which makes use of end-gas autoignition following conventional deflagration-based combustion, can be further utilized to speed up the overall combustion. In this study, PFS-assisted mixed-mode combustion in a lean-burn direct injection spark-ignition (DISI) engine is numerically investigated using multi-cycle large eddy simulation (LES). A previously developed hybrid G-equation/well-stirred reactor combustion model for the well-mixed operation is extended to the PFS-assisted operation. The experimental spray morphology is employed to derive spray model parameters for the pilot injection. The LES-based model is validated against experimental data and is further compared with the Reynolds-averaged Navier–Stokes (RANS)-based model. Overall, both RANS and LES predict the mean pressure and heat release rate traces well, while LES outperforms RANS in capturing the cycle-to-cycle variation (CCV) and the combustion phasing in the mass burned space. Liquid and vapor penetrations obtained from the simulations agree reasonably well with the experiment. Detailed flame structures predicted from the simulations reveal the transition from a sooting diffusion flame to a lean premixed flame, which is consistent with experimental findings. LES captures more wrinkled and stretched flames than RANS. Finally, the LES model is employed to investigate the impacts of fuel properties, including heat of vaporization (HoV) and laminar burning speed (SL). Combustion phasing is found more sensitive to SL than to HoV, with a larger fuel property sensitivity of the heat release rate from autoignition than that from deflagration. Moreover, the combustion phasing in the PFS-assisted operation is shown to be less sensitive to SL compared with the well-mixed operation.
Shipping is one of the most efficient transportation modes for moving freight globally. International regulations concerning decarbonization and emission reduction goals drive rapid innovations to meet the 2030 and 2050 greenhouse gas reduction targets. The internal combustion engines used for marine vessels are among the most efficient energy conversion systems. Internal combustion engines dominate the propulsion system architectures for marine shipping, and current marine engines will continue to serve for several decades. However, to meet the aggressive goals of low-carbon-intensity shipping, there is an impetus for further efficiency improvement and achieving net zero greenhouse gas emissions. These factors drive the advancements in engine technologies, low-carbon fuels and fueling infrastructure, and emissions control systems. This editorial presents a perspective on the future of ship engines and the role of low-life cycle-carbon-fuels in decarbonizing the marine shipping sector. A selection of zero-carbon, net-zero carbon, and low-lifecycle-carbon-fuels are reviewed. This work focuses on the opportunities and challenges of displacing distillate fossil fuels for decarbonizing marine shipping. In conclusion, enabling technologies such as next-generation air handling, fuel injection systems, and advanced combustion modes are discussed in the context of their role in the future of low-CO 2 intensity shipping.
As of May 2019, all RDE test hardware and test operations were completed for this program. Over 700 hot fire tests were conducted at 4 facilities with 7 RDE assemblies, with the data available for future analysis. In particular, measured static and dynamic pressure and computational fluid dynamics (CFD) provided basis for pressure gain combustion (PGC) analysis of the Engineering Scale RDE. It was demonstrated via measurements and CFD that a carefully designed diffuser can recover some total pressure of the highly oscillatory RDE exhaust. In general, the isolator/injector is largest contributor of pressure losses; however a low loss design was demonstrated that improves significantly on previous natural-gas/air RDEs. Also, a tailored injector and mixing technique was demonstrated that allowed NG-air RDE operation with overall equivalence ratio of less than 0.6. CFD models by AR, the UofM and Purdue University were extensively used throughout the program to refine diffuser design for improved pressure recovery, to simulate injection and mixing, to estimate turbine interaction, to estimate combustor pressure gain anchored with test data and to provide input to other design and analytical tasks. CFD analyses were completed for many of the configurations tested at Purdue with the 13.4” RDE including the shark fin and low area ratio isolators. Results were compared favorably to available hot fire test particle image velocimetry (PIV) measurements and wave speeds. The results of these CFD analyses suggest a modest pressure gain of 33% with a sufficiently low area ratio isolator. A thermodynamic power plant system model was set up which utilizes an RDE combustor. The performance of the RDE combustor module was scaled based on measurements from the Engineering Scale RDE tests with low loss injector and tailored fuel flow. This combined model indicates potential for significant plant efficiency improvement. The successful tailored fuel injection test result with lower than .6 equivalence ratio has implication that a power plant can use a RDE combustor configuration where all the air flows through the combustion zone thereby not requiring a bypass mixer. In this configuration, the results of the natural gas combined cycle (NGCC) plant system indicate net efficiency of 66.4%.
High-fidelity simulations of a swirl-stabilized turbulent spray flame in a laboratory-scale aero-combustor have been performed to evaluate the predictability of state-of-the-art models in capturing soot formation. The simulations employ a complex chemical mechanism developed for Jet-A with PAH chemistry, coupled with the Hybrid Method of Moments (HMOM) soot model, and a Lagrangian dilute spray model for the fuel injection. Two simulations are performed to compare the results when thermal radiation is neglected or included in the solution with a mean spectral model. Modeling closures for the soot differential diffusion effects in mixture fraction space, as well as turbulence-radiation interaction are also evaluated using the data generated by the simulations. Given the degree of complexity of the simulation, the results showed good agreement with experimental measurements of the spatial distribution of the soot volume fraction ensemble average. A closer agreement with the experiment is observed when thermal radiation is included in the solution. Thermal radiation is observed to reduce the flame temperature and increase the flame intermittency, denoted by the increase in the temperature standard deviation in mixture fraction space. The reduction in temperature also leads to a reduction in PAH production and soot volume fraction. Turbulence is observed to have different effects on radiative emission depending on the mixture fraction. Turbulent scalar fluctuations significantly enhance radiative emission in fuel lean mixtures and can also play a role for fuel rich conditions. The statistical description of the turbulence-radiation interaction, previously proposed in the literature, was observed to correctly reproduce the high-fidelity results. Model coefficients were provided for swirl-stabilized flames. The soot differential diffusion model, previously proposed in the literature, based on the residual between the exact term and its model approximation, was also evaluated. The residual correction term further improved the agreement with exact differential diffusion term evaluated with the high-fidelity simulation data in mixture fraction space. The results suggest that the effective turbulent Lewis number can be equal to unity in simulations of turbulent non-premixed recirculating flames.
The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. Furthermore, these results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.
The high $\phi$-sensitivity (η) of ignition delay time (τ IDT ) is one of the desirable fuel properties for the high-load extension of advanced compression ignition engine. Recent studies revealed the effectiveness of a high dilution rate (x D ) for enhancing η at the engine-relevant conditions. This study aims to quantify the effect of dilution on the η of isooctane using a combined experiment-simulation approach. The ignition delay of the isooctane/air mixture was measured with an Advanced Fuel Ignition Delay Analyzer (AFIDA) over the temperature range of 623 - 923 K at 10 bar pressure with global $\phi$ = 0.3 - 0.6, with and without 28.6% of additional N 2 dilution. For precise evaluation of experimental η, the facility effect of the AFIDA experiment was characterized with three-dimensional computational fluid dynamics (3-D CFD) simulation. The temperature in the combustion chamber from 3-D CFD indicated a substantial temporal dependency, varying up to ~52 K by charge-cooling of fuel injection and heat transfer from the wall. We introduced the dimensionless number θ(t) for characterizing the temporal profile of chamber temperature. Consideration of the facility effect using θ(t) resulted in better agreement between the experimental ..eta.. and zero-dimensional (0-D) kinetics simulation. The refined η were then further utilized to quantify the effectiveness of dilution to η The extent of η enhancement with dilution strategy was maximized at the low-temperature chemistry regime, increasing η by 77% with a 28.6% dilution rate. Further analysis on the dilution effect was carried out using 0-D kinetics simulation, revealing the critical dimensionless numbers relevant to the effectiveness of dilution to η enhancement. Here, this study is the first experiment-simulation combined research to quantify the effect of dilution on η, facilitating the kinetics model refinement for better reproduction of $\phi$-sensitivity.
Direct internal recycling (DIR) refers to the process of recovering pure hydrogen isotopes (D/T) from helium and other impurities in the fusion plasma exhaust and directing them back to the fuel injection system. Increasing the exhaust fraction purified through DIR significantly reduces the size and cost of the tritium plant and provides additional benefits including reduced requirements for both the tritium startup inventory and tritium breeding ratio. Metal foil pumps (MFPs) are the dominant technology for this separation, relying on the concept of superpermeation. We recently demonstrated that PdCu foils operated at low temperature provide both exceptional flux and resilience to helium absorption as the DIR fraction is increased. Herein we design and demonstrate continuous and semi-batch DIR processes using PdCu MFPs. Under continuous processing, stable performance was observed for DIR fractions up to 92 %. In addition, we demonstrate a semi-batch process capable of extending the DIR fraction to unity (> 99.8 %). Under the experimental conditions described within a PdCu MFP area of ~22 m 2 would be sufficient to process the fusion exhaust with 92 % DIR fraction at expected flowrates of 100 Pa·m 3 ·s -1 for a future fusion power plant.
Compressible mixing layer instabilities are of importance to a wide range of environmental and industrial applications. Past studies have focused on either ideal-gas or real-fluid thermodynamic regimes of single-species mixing layers. However, mixing layers of binary mixtures at supercritical conditions, commonly encountered in fuel injection systems, introduce additional complexities due to the added compositional degree-of-freedom. Moreover, the effect of strong variations in thermodynamic response functions across the Widom line on the binary mixing layer stability remains poorly understood. Thus, the objective of this study is to examine the coupling between the hydrodynamic instability and the real-fluid thermodynamics across the Widom line and its effects on the overall binary mixing layer dynamics. To this end, we develop a linear stability analysis of the full binary-species compressible transport equations coupled with the PC-SAFT equation of state. Analysis shows the existence of a novel instability mechanism that arises from juxtapositioning of the Widom-line transition and the hydrodynamic inflection point. This novel thermodynamically induced instability mechanism has the net effect of destabilizing the binary mixing layer at lowering supercritical conditions towards the critical pressure point. This is in contrast to previous stability analyses of supercritical single-species mixing layers, where increasing pressure destabilizes the flow due to its effect on reducing the density stratification. The discovered thermodynamically induced instability mechanism of binary mixing flows highlights the need for an extension of classical instability criteria to incorporate the effect of strong variations in the thermodynamic response functions across the Widom line on mixing layer instability.
If the nuclei remain polarized, spin polarized fuel can increase the fusion power produced in a reactor while using less fuel. This study assesses whether reactions between an unpolarized deuterium (D) beam and polarized deuterium nuclei can in principle experimentally ascertain the persistence of nuclear polarization in a magnetic fusion experiment. The differential cross section for D–D reactions between unpolarized and polarized nuclei depends upon polarization, so the polarization state can be inferred from measurements of the D–D fusion products. An evaluation is performed for 3 MeV proton detection in the DIII-D tokamak using 81 keV neutral beams and polarized target fuel injected as a pellet. Measurement of the escaping proton pitch v 3 ∥ v 3 is insensitive to the degree of polarization but energy-resolved measurements are sensitive. The sensitivity is comparable for different angles of beam injection. Because the D–D reaction rate is large for this scenario, uncertainties associated with counting statistics are small, making inference of the polarization feasible with current technology.
In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.
The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.
In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.
Designing an effective burner is vital for the development of coal combustion technologies. Because of high pressure, the volumetric fraction of the coal particles in the injected fuel in a pressurized oxy-combustion (POC) burner approaches or even exceeds the limitations allowed by the commercial computational fluid dynamics codes (e.g., Ansys Fluent). Consequently, for such high particle volumetric fractions, the interplay between the particles, the fluid flow, and the burner wall needs to be re-evaluated. The present computational work is a first step in a systematic analysis of the roles of various characteristics involved in the POC process, such as the method of particle release, its location, and the particle size. Specifically, pulverized coal is burned under an elevated pressure of 15 bar in an O 2 /CO 2 environment. A 100 kW, a POC combustor, is modeled with Ansys Fluent using the Reynolds-averaged Navier−Stokes approach. It is revealed that for this pilot-scale, pressurized burner, the gas phase flow velocity in the near-wall region exhibits anomalies. With the major focus on POC, this work aims to eliminate/reduce the impact of high particle loading on the gas-phase flow. To scrutinize the role of particle loading in the near-wall region and eliminate the impact of this velocity on POC downstream, the particle−gas interplay in the boundary layer is investigated by means of the computational simulations incorporating the coupling between the turbulent flow and the particles. It is found that the tuning of the particle release location makes the gas-phase flow velocity in the presence of particles consistent with the pure gas flow velocity profile. The particles size is also found to have a significant impact on the particle trajectory.