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DOE OSTI2023

A physics-driven Σ-Y atomization model for heavy-duty engine simulations

The atomization of a liquid jet is a multi-scale and multi-physics problem of interest for many engineering applications. Particularly, it drives the fuel-oxidizer mixing and dictates the efficiency of combustion engines. High-fidelity multi-phase simulations remain challenging due to the excessive computational cost required to capture all the atomization scales. Thus, atomization models are necessary to represent sub-grid liquid structures. Here, in this work, a modified Σ-Y model in the context of Eulerian-Lagrangian Spray Atomization (ELSA) is used to transport the surface area density of the spray. The model's predictive performance is assessed under various operating conditions relevant to heavy-duty engines using the Engine Combustion Network (ECN) Spray C and Spray D research-grade injectors. The classic droplet collision formulation of the Σ-Y model alone does not replicate the response of the measured spray surface area to changes in injector, ambient pressure, and injection pressure, requiring individual tuning of the model's parameters. Instead, a transition between dense and dilute spray breakup mechanisms is proposed in terms of the average droplet spacing. The collision breakup mechanism represents the dilute spray, whereas the droplet size in the dense spray is driven by a competition between the integral scale of turbulence and the balance between the turbulent kinetic energy and the surface energy of the droplets. Such an approach minimizes the requirements for model tuning. Moreover, the role of the constants of a compressible standard k-ε RANS model is assessed in the injector's internal flow and external spray simulation framework, and an updated set is proposed. The results are validated against x-ray radiography and Ultra-Small Angle X-ray Scattering (USAXS) data and highlight the predictive capabilities of the proposed physics-driven Σ-Y model, which is compatible with engine simulation turnaround times.

42 ENGINEERING↗

DOE OSTI2021

Microwave Plasma Flash Pyrolysis for Coal-to-Product Applications

Since 2010, the US coal industry has experienced a precipitous decline, with coal production volumes dropping by as much as 35% according to EIA. This decline is hardly surprising given that 90% of domestic coal use is allocated to electricity production: While coal generation supplied about 45% of the nation's power in 2010, its share is projected to fall to only 22% in 2020. Fortunately, alternative uses for coal as a feedstock rather than as energy source are available. Coal tar is an established feedstock for chemicals and high-value carbon products including carbon fiber and battery-grade graphites. At present, growth of these uses is constrained by the low availability, purity, and yields of currently available coal liquids, and high energy- and CO2-intensity of current methods of production (i.e. coking). Previously reported work conducted in a partnership with Pacific Northwest National Laboratory demonstrated technical and economic viability of flash pyrolysis of coal in a microwave plasma environment at bench scales targeting production of fuel feedstocks (synthetic oil). H Quest’s work since then indicated promise of microwave-derived coal liquids to be uniquely suited for the lower-cost production of carbon materials, including synthetic graphite, carbon fiber, and composites. Low bulk process temperatures, subsecond residence times, and ambient pressure operation enable small-scale deployment, reduce capital and operating costs, and suppress formation of impurities, including QI material, typical in conventional coal tars. With no inherent production of CO2 or water consumption, microwave plasma flash pyrolysis presents a viable route to the sustainable production of both platform chemicals and carbon-based materials. Most recently, H Quest developed a novel, ruggedized microwave plasma reactor, which departs from the proof-of-concept approach and addresses the problems typically encountered in scaling laboratory microwave plasma apparata (e.g. eliminating fragile quartz enclosures prone to coking and fouling). This reactor has been integrated with a 15kW microwave power supply, a pulverized coal injection system, and a product recovery system. A series of screening tests across a range of coals (including hv-, lv-bituminous and subbituminous PRB), entrainment gas flows and compositions and microwave energy inputs are performed to evaluate conversion rates within the novel, scaled-up system, confirm yields of liquid products, and to perform compositional (GC/MS and elemental) analyses of the products. Results of these tests will be presented and discussed.

Skoptsov, George↗

DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Mitigating municipal solid waste fouling in biofuel conversion via screw surface modifications

Here, municipal solid waste (MSW)'s 40–60 % carbon content makes it a feedstock for biofuel production via pyrolysis. One challenge in the conversion process is MSW fouling due to thermal decomposition. The accumulated deposit on the injection screw often leads to plugging and constriction. This study examined the morphology and composition of the MSW fouling deposit and conducted a thermal simulation to understand the temperature gradience of the injection screw. Surface modifications including smoothening and anti-adhesion coating were proposed for the injection screw to address the deposit problem. For evaluating the candidate mitigations, a bench-scale fouling test was developed with the gas environment, temperature, and sliding speed relevant to the contact interface between the MSW particles and the screw. Results suggested that a smoother screw surface could reduce the grip and a non-metallic coating with a lower surface energy could decrease adhesion, consequently leading to less fouling. Specifically, reducing the roughness from 2 to 0.6 and then to 0.2 μm proportionally decreased the amount of deposit, and the diamond-like-carbon, CrN, and NiCr–CrC composite coatings effectively hindered the fouling process. This study provides fundamental insights into the MSW fouling and proof-of-concept of potential mitigations through the screw surface modification.

09 BIOMASS FUELS↗

DOE OSTI2022

Nonlinear burn control in ITER using adaptive allocation of actuators with uncertain dynamics

Abstract ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma–wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e. the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.

Physics↗

DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗