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Kwon, Joseph Sang-Il

Publications and source records attributed to Kwon, Joseph Sang-Il.

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rheological dynamics and structural characteristics of supramolecular assemblies of β-cyclodextrin and sulfonic surfactants

Cyclodextrins are highly functional compounds with a hydrophobic cavity capable of forming supramolecular inclusion complexes with various classes of molecules including surfactants. The resultant rich nanostructures and their dynamics are an interesting research problem in the area of soft condensed matter and related applications. In this paper, we report novel dynamical supramolecular assemblies based on the complexation of β-cyclodextrin with 3 different sulfonic surfactants, which are sodium hexadecylsulfate, sodium dodecylbenzenesulfonate, and myristyl sulfobetaine. It was observed that a β-cyclodextrin : surfactant/2 : 1 molar ratio was ideal for inducing axial growth and imparting large viscosities in the suspensions. Such complexation processes were accompanied by intriguing nanostructural phase behaviors and rheological properties that were very sensitive to the molecular architecture of sulfonic surfactants. The presence of an amino group in the head group of the surfactant allowed for large viscosities that reached 2.4 × 10 4 Pa s which exhibited gel-like behavior. In contrast, smaller viscosity values with a lower consistency index were observed when a bulky aromatic ring was present instead. DIC microscopy was used to visually probe the microstructure of the systems with respect to sulfonate molecular architecture. Additionally, surface tension measurements, and FTIR and NMR spectroscopies were used to gain insights into the nature of interactions that lead to the complexation and nanostructural characteristics. Finally, mechanics correlating the supramolecular morphologies to the rheological properties were proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Exploring the benefits of utilizing small modular device for sustainable and flexible shale gas water management

Growing shale gas extraction in recent years has triggered wide discussions on the associated freshwater requirement and wastewater management. Many optimization approaches have been developed for shale gas water management; however, most of the studies assumed permanent utilization of wastewater treatment facilities with fixed capacities. Considering the rapidly declining characteristics of shale gas wastewater production, these treatment facilities could remain largely underutilized after the first few months/years of production, making them less economically attractive. To maximize the capacity utilization of treatment facilities and further improve the economic performance of shale gas development, this study develops a systematic optimization framework, where the capacity strategy of conventional treatment facilities and utilization of the recent concept of modular manufacturing are both considered for flexible shale gas water management. The proposed mixed-integer linear programming (MILP) model simultaneously optimizes the design and planning of integrated shale gas and water supply chain, with a focus on capacity planning for both large-scale conventional treatment facilities and small-scale modular devices. A series of Marcellus-based case studies are performed to illustrate the applicability of the proposed model and provide general insights into the trade-offs between the multiple types of treatment facilities. The optimization results reveal that the combinatorial utilization of conventional facilities and modular devices for wastewater treatment (66% by conventional facilities and 34% by modular devices) brings 9.3% more reused water for other well development and 6.2% savings in water-related costs, compared to flexible management of only conventional facilities. Furthermore, this work suggests that taking modular device as auxiliary equipment for shale gas water management is most beneficial to increase the capacity utilization of treatment facilities and achieve a more economic and sustainable shale gas production system.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2022

Dynamic, hollow nanotubular networks with superadjustable pH-responsive and temperature resistant rheological characteristics

Recently, the interest in stimuli-responsive and adaptable materials has continuously grown in various fields and applications. For such responsive systems, different triggers, including pH, light, pressure, temperature, and electric field, have been utilized to control dynamics and assembly. Among these, pH is one of the most convenient, energy-efficient, and economic modalities. Besides, plenty of traditional materials have poor thermal and salt stability, limiting their applications. Herein, we report a new design of a pH-responsive viscoelastic supramolecular complex (VSC) based on commplexation of a new long-chain amino-amide and maleic acid. In this study, the system demonstrated a sol–gel-sol transition from pH 2 to 10, with the largest static viscosity occurring at pH 6 (~1000 Pa·s) and the smallest viscosity at pH 4 (~3.3 Pa·s), indicating ~ 300-fold control over the viscosity. For a given concentration, the static viscosity of VSC was about 15 times larger than that of CTAB/NaSal, a well-established dynamic viscoelastic system, and no pH-responsiveness was observed for the traditional system. In addition, the VSC demonstrated a superior temperature tolerance and lower temperature dependence. The potential of these intriguing dynamics viscoelastic systems was evaluated for hydraulic fracturing and enhanced oil recovery applications. Proppant settling velocity of DMAA/MA was 500 ~ 1000 times lower than that of CTAB/NaSal and common traditional polymers. Likewise, the oil recovery percentage could be significantly improved with the utilization of DMAA/MA compared to the CTAB/NaSal (86 % vs 52 %). Aside from applications in hydraulic fracturing and enhanced oil recovery, we anticipate that the intriguing rheological properties of this viscoelastic system can be beneficial for other chemical engineering applications including personal care products, cosmetics, lubricants, and biomedical gels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Nanostructural and rheological transitions of pH-responsive supramolecular systems involving a zwitterionic amphiphile and a triamine

Supramolecular aqueous complexes of a synthesized zwitterionic surfactant, 2-(dimethyl(octadecyl)ammonio)acetate (stearyl betaine) and diethylenetriamine, have been found to display pH-tunable rheological properties without the need for a co-surfactant. For this supramolecular system, the dynamically changing interplay among the constituents is responsible for the rich phase behavior including hollow nanotubules, bilayers, and vesicles. A further hypothesis is that hollow nanotubular structure is highly desirable for achieving chain entanglement geometries with reduced mesh size. The rheological properties of the suspension were measured using a rotational rheometer. An analysis using zero-shear viscosities at various temperatures as well as differential scanning calorimetry(DSC) helped shed light on thermodynamic and phase transitions taking place. The morphology of the suspension was analyzed by using a combination of small angle x-ray scattering(SAXS) and atomic force microscopy(AFM). Large steady-shear viscosities (up to ~160 Pa.s) and viscoelastic behavior was observed in acidic conditions which diminished upon increasing the pH. A novel analysis consisting of measured thermodynamic quantities such as activation energy(E a ) and enthalpy of transition(ΔH T ) further allude to the rich pH-sensitive nanoarchitecture of the system. Rather than commonly observed wormlike micelles, hollow nanotubules, as long as 1.6 µm were observed at pH 4 which disintegrated to bilayer sheets and vesicles upon increasing pH. Finally, gaining a comprehensive understanding from all the results, a transition mechanism between different suspension architectures was proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and dimensions

Recent studies have shown that solvated amphiphiles can form nanostructured self-assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the nanostructures of DBCs are directly related to their viscoelastic properties, it is important to understand how the nanostructures change under different solution conditions. However, it is challenging to obtain a three-dimensional molecular description of these nanostructures by utilizing conventional experimental characterization techniques or thermodynamic models. To this end, we combined the structural data from small angle X-ray scattering (SAXS) experiments and thermodynamic knowledge from coarse-grained Monte Carlo (CGMC) simulations to identify the detailed three-dimensional nanostructure of DBCs. Specifically, unbiased CGMC simulations are performed with SAXS-guided initial conditions, which aids us to sample accurate nanostructures in a computationally efficient fashion. As a result, an elliptical bilayer nanostructure is obtained as the most probable nanostructure of DBCs whose dimensions are validated by scanning electron microscope (SEM) images. Then, utilizing the obtained molecular model of DBCs, here we could also explain the pH tunability of the system. Overall, our results from SAXS-guided unbiased CGMC simulations highlight that using potential energy combined with SAXS data, we can distinguish otherwise degenerate nanostructures resulting from the inherent ambiguity of SAXS patterns.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep reinforcement learning control of hydraulic fracturing

Hydraulic fracturing is a technique to extract oil and gas from shale formations, and obtaining a uniform proppant concentration along the fracture is key to its productivity. Recently, various model predictive control schemes have been proposed to achieve this objective. But such controllers require an accurate and computationally efficient model which is difficult to obtain given the complexity of the process and uncertainties in the rock formation properties. In this article, we design a model-free data-based reinforcement learning controller which learns an optimal control policy through interactions with the process. Deep reinforcement learning (DRL) controller is based on the Deep Deterministic Policy Gradient algorithm that combines Deep-Q-network with actor-critic framework. In addition, we utilize dimensionality reduction and transfer learning to quicken the learning process. We show that the controller learns an optimal policy to obtain uniform proppant concentration despite the complex nature of the process while satisfying various input constraints.

42 ENGINEERING↗
DOE OSTI2021

A slip-spring framework to study relaxation dynamics of entangled wormlike micelles with kinetic Monte Carlo algorithm

Hypothesis: Wormlike micelles (WLMs) formed due to the self-assembly of amphiphiles in aqueous solution have similar viscoelastic properties as polymers. Owing to this similarity, in this work, it is postulated that kinetic Monte Carlo (kMC) sampling of slip-springs dynamics, which is able to model the rheology of polymers, can also be extended to capture the relaxation dynamics of WLMs. Theory: The proposed modeling framework considers the following relaxation mechanisms: reptation, union-scission, and constraint release. Specifically, each of these relaxation mechanisms is simulated as separate kMC events that capture the relaxation dynamics while considering the living nature of WLMs within the slip-spring framework. As a case study, the model is implemented to a system of sodium oleate and sodium chloride to predict the linear rheology and the characteristic relaxation times associated with the individual relaxation mechanisms at different pH and salt concentrations. Findings: Linear rheology predictions were found to be in good agreement with experimental data. Furthermore, the calculated relaxation times highlighted that reptation contributed to a continuous increase in viscosity while union-scission contributed to the decrease in viscosity of WLM solutions at a higher salinity and pH. Finally, this manifests the proposed model’s capability to provide insights into the key processes governing WLM’s rheology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation

This work explores the application of the recently developed Koopman operator approach for model identification and feedback control of a hydraulic fracturing process. Controlling fracture propagation and proppant transport with precision is a challenge due in large part to the difficulty of constructing approximate models that accurately capture the characteristic moving boundary and highly-coupled dynamics exhibited by the process. Koopman operator theory is particularly attractive here as it offers a way to explicitly construct linear representations for even highly nonlinear dynamics. The method is data-driven and relies on lifting the states to an infinite-dimensional space of functions called observables where the dynamics are governed by a linear Koopman operator. Here this work considers two problems: (a) fracture geometry control, and (b) proppant concentration control. In both cases, an approximate linear model of the corresponding dynamics is constructed and used to design a model predictive controller (MPC). The manuscript shows that in the case of highly nonlinear dynamics, as observed in the proppant concentration, use of canonical functions in the observable basis fails. In such cases, a priori system knowledge can be leveraged to choose the required basis. The numerical experiments demonstrate that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired target values maximizing the oil and gas productivity. Additionally, due to its linear structure, the Koopman models allow convex MPC formulations that avoid any issues associated with nonlinear optimization.

42 ENGINEERING↗
DOE OSTI2020

Multi-Size Proppant Pumping Schedule of Hydraulic Fracturing: Application to a MP-PIC Model of Unconventional Reservoir for Enhanced Gas Production

Slickwater hydraulic fracturing is becoming a prevalent approach to economically recovering shale hydrocarbon. It is very important to understand the proppant’s transport behavior during slickwater hydraulic fracturing treatment for effective creation of a desired propped fracture geometry. The currently available models are either oversimplified or have been performed at limited length scales to avoid high computational requirements. Another limitation is that the currently available hydraulic fracturing simulators are developed using only single-sized proppant particles. Motivated by this, in this work, a computationally efficient, three-dimensional, multiphase particle-in-cell (MP-PIC) model was employed to simulate the multi-size proppant transport in a field-scale geometry using the Eulerian–Lagrangian framework. Instead of tracking each particle, groups of particles (called parcels) are tracked, which allows one to simulate the proppant transport in field-scale geometries at an affordable computational cost. Then, we found from our sensitivity study that pumping schedules significantly affect propped fracture surface area and average fracture conductivity, thereby influencing shale gas production. Motivated by these results, we propose an optimization framework using the MP-PIC model to design the multi-size proppant pumping schedule that maximizes shale gas production from unconventional reservoirs for given fracturing resources.

42 ENGINEERING↗