Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics

Recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network, such that the network not only conforms to the measurements, initial and boundary conditions but also satisfies the governing equations. This work first investigates the performance of PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The results elucidate the challenges of utilizing PINN in stiff ODE systems. Consequently, we employ Quasi-Steady-State-Assumptions (QSSA) to reduce the stiffness of the ODE systems, and the PINN then can be successfully applied to the converted non/mild-stiff systems. Therefore, the results suggest that stiffness could be the major reason for the failure of the regular PINN in the studied stiff chemical kinetic systems. The developed Stiff-PINN approach that utilizes QSSA to enable PINN to solve stiff chemical kinetics shall open the possibility of applying PINN to various reaction-diffusion systems involving stiff dynamics

Chemistry-transport coupling in flame dynamics and emissions

Chemical kinetics and fluid dynamics are crucial components of combustion, governing the efficiency, stability, and emissions of many practical combustion devices. Particularly, this dissertation advances the understanding of the coupling effects between chemical kinetics and transport in flame dynamics (Chapters 2 and 3) and soot emissions (Chapters 4 and 5) at engine relevant conditions. For both topics, foundational studies on chemical kinetics were first carried out in relatively simple, laminar, low-dimensional configurations with well characterized flow fields to understand low-temperature cool flame chemistry and soot chemistry. Complexities from flows were then considered, and chemistry-transport coupling was investigated at engine relevant conditions to elucidate the role of low-temperature chemistry in autoignition-affected flame dynamics and the role of hydrogen addition in soot evolution in bluff body flames, leveraging the understanding obtained in the chemical kinetics studies. The first half of this dissertation focuses on low-temperature chemistry and its role in flame dynamics. Specifically, in Chapter 2, experimental studies, supported by computations, were conducted on the coupling of low-temperature chemistry and transport in the ignition, extinction, and associated steady burning in nonpremixed DME/air counterflow flames. The presence of low-temperature chemical reactivity was detected nonintrusively, and the ignition temperature was determined subsequently. At elevated pressures, which promote low-temperature chemistry, the hysteresis in ignition and extinction behavior of nonpremixed cool flames was observed and quantified for the first time. The thermal and chemical structure of the cool flame was computationally analyzed to elucidate the dominant chemical pathways during the ignition and extinction processes. Effects of strain rate, fuel and oxygen concentration, and ambient pressure on the cool flame were investigated. Possible reasons for the discrepancies between experiments and computations were discussed to facilitate further cool flame studies and the development of low-temperature chemical models. The role of low-temperature chemistry in autoignition-affected flame dynamics was then computationally investigated in Chapter 3. Laminar nonpremixed DME/air coflow flames were investigated at elevated temperatures and pressures with various boundary temperatures and velocities. The stabilization mechanism for steady flames and the flame dynamics for the forced oscillating cases were analyzed. Besides the tribrachial structure typically observed at nonautoignitive conditions, a multibrachial thermal structure was observed due to autoignition. Consequently, a stabilization regime diagram was proposed, including frozen flow, kinetically stabilized (autoignition), autoignition-propagation-coupled stabilized, kinematically stabilized (tribrachial flame), and burner stabilized regimes. The transition of the combustion mode was elucidated through the computational investigations of sinusoidally forced oscillating cases. Transition between a multibrachial autoignition front and a tribrachial flame occurs periodically and exhibited a hysteresis. First-stage low-temperature chemistry is less affected by flow dynamics with only second-stage autoignition and flame chemistry, which accounts for the majority of the heat release, coupled with flow oscillation. The understanding of the role of low-temperature chemistry in flame dynamics under laminar autoignitive conditions lays the foundation for future studies at turbulent conditions in practical engines. The second half of this dissertation focuses on soot emissions. To understand the fuel effects on soot chemistry, in Chapter 4, the sooting limits of nonpremixed n-heptane, n-butanol, and methyl butanoate flames were determined experimentally in a liquid pool stagnation-flow configuration. In addition, complementary simulations with detailed polycyclic aromatic hydrocarbon (PAH) chemistry and a detailed soot model, based on the Hybrid Method of Moments (HMOM), were performed and compared with the experimental critical strain rates for the sooting flames. Argon dilution was used to keep the thermal environment for the three fuel cases nearly the same to elucidate the chemical effects. Both experiment and simulation showed that n-heptane and n-butanol had similar sooting characteristics, while methyl butanoate had the least sooting propensity. Further sensitivity and reaction pathway analysis demonstrated that the three fuels share similar PAH chemical pathways, and C5 and C6 ring formation from the intermediate chain species was found to be the rate-limiting step. The differences in sooting propensity were due to the role of fuel bounded oxygen and the fuel breakdown processes. The findings in this chapter provide guidance to the design of diesel/biofuel blendings to reduce soot emissions. Finally, in Chapter 5, the evolution of soot in a turbulent nonpremixed bluff body ethylene/hydrogen flame was investigated using a combination of experiments and Large Eddy Simulations and compared with a neat ethylene counterpart. With hydrogen addition, the maximum soot volume fractions in the recirculation zone and jet-like region significantly decreased. Flamelet calculations demonstrated that hydrogen addition suppressed soot formation due to the reduction of the C/H ratio, resulting in an estimated fourfold reduction in soot volume fraction due to chemical effects. Soot reduction in the downstream jet-like region of the flame was quantitatively consistent with this chemical effect. However, soot reduction in the recirculation zone was substantially larger than this analysis suggests, indicating an additional hydrodynamic effect. Large Eddy Simulation was used to further investigate soot evolution in the recirculation zone and to elucidate the role of hydrogen addition. For the same heat release rate and similar jet Reynolds number as the neat ethylene case, the addition of hydrogen required a higher jet velocity, and this led to a leaner recirculation zone that inhibited soot formation and promoted soot oxidation. The findings in this chapter further validated the comprehensive soot model for turbulent sooting flames and advanced the understanding of soot evolution in recirculating flows

Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network

Chemical reactions occur in energy, environmental, biological, and many other natural systems, and the inference of the reaction networks is essential to understand and design the chemical processes in engineering and life sciences. Yet, revealing the reaction pathways for complex systems and processes is still challenging because of the lack of knowledge of the involved species and reactions. Here, we present a neural network approach that autonomously discovers reaction pathways from the time-resolved species concentration data. The proposed chemical reaction neural network (CRNN), by design, satisfies the fundamental physics laws, including the law of mass action and the Arrhenius law. Consequently, the CRNN is physically interpretable such that the reaction pathways can be interpreted, and the kinetic parameters can be quantified simultaneously from the weights of the neural network. The inference of the chemical pathways is accomplished by training the CRNN with species concentration data via stochastic gradient descent. We demonstrate the successful implementations and the robustness of the approach in elucidating the chemical reaction pathways of several chemical engineering and biochemical systems. The autonomous inference by the CRNN approach precludes the need for expert knowledge in proposing candidate networks and addresses the curse of dimensionality in complex systems. The physical interpretability also makes the CRNN capable of not only fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems

Dependence of kinetic sensitivity direction in premixed flames

© 2020 The sensitivities of turbulent combustion simulations to chemical kinetic parameters can be analyzed to understand the controlling reactions in turbulent flames and to quantify the uncertainties in simulations. However, computing the sensitivity of turbulent combustion simulations to a large number of kinetic parameters is still challenging. A promising approach is to estimate the sensitivity from laminar flames, especially for cases where the flamelet model is applicable. Under these conditions, the underlying hypothesis is that the sensitivity direction of the flamelet profiles is independent of the strain rate and the flame coordinate, which is the progress variable for premixed flames. In the present work, this hypothesis was tested in laminar premixed counterflow flames. We first studied the sensitivity directions of two extreme cases, the near-extinction strained flames and the freely propagating unstretched flames. It was found that the sensitivity directions of the extinction strain rate and the laminar flame speed are aligned with each other for various fuels, equivalence ratios, and pressures. We then studied the dependence of the sensitivity direction of the maximum flame temperature on the strain rate as well as the dependence of the sensitivity direction of the species profiles on the progress variable. It was found that the sensitivity direction of maximum temperature was largely independent of the strain rate. Moreover, the sensitivity directions of the temperature and species profiles were independent of the progress variable, and they were all similar to the sensitivity direction of the extinction strain rate. These findings suggest that there is a universal sensitivity direction for turbulent premixed flames and the direction can be estimated by the sensitivity direction of extinction strain rate. These conclusions will enable efficient sensitivity analysis of turbulent combustion simulations when the hypothesis is valid

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification

We develop Bayesian Chemical Reaction Neural Network (B-CRNN), a method to infer chemical reaction models and provide the associated uncertainty purely from data without prior knowledge of reaction templates.</jats:p

Impacts of lubricating oil and its formulations on diesel engine particle characteristics

© 2020 Lubricating oil-related engine emission reduction is now a key path to further control the engine-out emission level and to meet the restrict regulations, especially in the manner of particulate number emission. This work experimentally studied the contribution of lubricating oil and its key constituents to the particle emission via monitoring the combustion process and analyzing the particle physic-chemical characteristics. Two sets of experiments were conducted to understand how the lubricating oil alters the regular combustion cycles and to study the effects of oil sulfur and metallic-ash constituents on the particle chemical characteristics, respectively. Details of the surface oxygenated functional groups and carbon chemical state on the particle surface were analyzed by XPS, while the FTIR was employed to characterize the possible functional groups in the bulk particle samples and the bonding patterns of the sulfur element. The effects on the particle morphology and elemental compositions were analyzed by SEM-EDS. Results show that the lubricating oil could shorten the ignition-delay phase combustion effectively, for instance, by 20% when 1wt% oil is burned along with diesel. Furthermore, more oxygenated surface functional groups and relatively more sp3 hybridization carbon shows up in the oil-derived particles. The sulfur element in the oil increases the oxygenated functional groups and lowers the aliphatic C[sbnd]H group by forming –SH radical. On the contrary, the metallic-ash fraction reduces the amount of oxygenated functional groups because the inorganic sulfates/phosphates occupy some oxygen atoms during the combustion reaction. Both the sulfur and ash tend to generate more un-substituted and meta-disubstituted benzene instead of the mono-disubstituted benzene structure, which is popular in diesel fuel-related particles mainly. Last but not the least, the sulfur and ash content significantly increase the concentration of the sulfates and phosphates of Iron, Calcium, and Zinc in the particles

Stiff neural ordinary differential equations

Neural Ordinary Differential Equations (ODE) are a promising approach to learn dynamic models from time-series data in science and engineering applications. This work aims at learning Neural ODE for stiff systems, which are usually raised from chemical kinetic modeling in chemical and biological systems. We first show the challenges of learning neural ODE in the classical stiff ODE systems of Robertson's problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems. We then present successful demonstrations in stiff systems of Robertson's problem and an air pollution problem. The demonstrations show that the usage of deep networks with rectified activations, proper scaling of the network outputs as well as loss functions, and stabilized gradient calculations are the key techniques enabling the learning of stiff neural ODE. The success of learning stiff neural ODE opens up possibilities of using neural ODEs in applications with widely varying time-scales, like chemical dynamics in energy conversion, environmental engineering, and the life sciences

Facile Thermal and Optical Ignition of Silicon Nanoparticles and Micron Particles

Silicon (Si) particles are widely utilized as high-capacity electrodes for Li-ion batteries, elements for thermoelectric devices, agents for bioimaging and therapy, and many other applications. However, Si particles can ignite and burn in air at elevated temperatures or under intense illumination. This poses potential safety hazards when handling, storing, and utilizing these particles for those applications. In order to avoid the problem of accidental ignition, it is critical to quantify the ignition properties of Si particles such as their sizes and porosities. To do so, we first used differential scanning calorimetry to experimentally determine the reaction onset temperature of Si particles under slow heating rates (∼0.33 K/s). We found that the reaction onset temperature of Si particles increased with the particle diameter from 805 °C at 20–30 nm to 935 °C at 1–5 μm. Then, we used a xenon (Xe) flash lamp to ignite Si particles under fast heating rates (∼10³ to 10⁶ K/s) and measured the minimum ignition radiant fluence (i.e., the radiant energy per unit surface area of Si particle beds required for ignition). We found that the measured minimum ignition radiant fluence decreased with decreasing Si particle size and was most sensitive to the porosity of the Si particle bed. These trends for the Xe flash ignition experiments were also confirmed by our one-dimensional unsteady simulation to model the heat transfer process. The quantitative information on Si particle ignition included in this Letter will guide the safe handling, storage, and utilization of Si particles for diverse applications and prevent unwanted fire hazards