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Process Simulation and Mechanical Analysis of High Temperature Resistance Composite Materials
Get PDFDepartment of Mechanical EngineeringHigh-temperature resistance composite materials are rapidly replacing conventional metal alloys used for thermal protection systems (TPS) of spacecraft and missiles exiting and/or reentering the atmosphere. Recently, South Korea succeeded in launching Nuri, the first domestically developed space rocket. Ongoing space programs of S. Korea include the commercialization of the Nuri technologies as well as the development of the next-generation space launch vehicles and the spaceships for exploring the moon and deep space. The Korea government is also actively developing a long-range reentry missile after the ballistic-missile range limits was abolished. These rockets and missiles are subjected to extremely high temperature and pressure when they pass through the atmosphere and thus typically designed with TPS to protect internal devices and human pilots. High-temperature resistant yet lightweight TPS materials are preferred in order to reduce a gross launch payload. Ceramic or carbon-based composite materials are much lighter than metals but also excellent thermal insulators with exceptional dimensional stability at elevated temperature.
The ceramic and carbon-based composite materials are often denoted as ceramic matrix composites (CMCs) and carbon-carbon (CC) composites, respectively. CMCs consist of ceramic fibers embedded in a ceramic matrix. The carbon matrix of CC composites is reinforced with carbon fibers. The most typical manufacturing methods of the CMCs and CC composites is a chemical vapor infiltration (CVI) process. In the CVI process, a porous fibrous preform is commonly used as the initial skeleton of a composite. The preform is placed in a CVI reactor, and the reactor is then pressurized and heated before a precursor gas is supplied. When this gas chemically reacts at the pore surfaces inside the preform, a pyrolytic carbon or ceramic layer is deposited onto the preform surfaces. The deposition process slowly changes the precursor into the matrix, filling the empty spaces of the preform.
Although the CVI process is a seemingly only viable method to produce a large-scale product, porosity in the final product is not completely avoidable because infiltrated fibers may barricade the path of the precursor gas into internal voids. The porosity is considered defects and the sources of the degradation of mechanical properties. In the present PhD study, comprehensive numerical analysis has been performed from the CVI process simulation to the micro- and meso-scale mechanical analysis of the composite materials. Firstly, the mechanisms of porosity formation are examined by developing a physico-chemical model. The effects of the porosity on the mechanical performances are investigated using a microscale and mesoscale composite models.
In the very first part, a fully three-dimensional (3D) physicochemical CVI model is developed to simulate an isothermal CVI process for fabricating bulk carbon-carbon composites using methane as a precursor gas and a multi-layered preform consisting of a non-crimp fabric and felt. The flow inside the CVI reactor was modeled using the Navier-Stokes equation, coupled with the convection-diffusion equation, to simulate the dispersive behaviors of the reactive gases inside the porous preform. The interactive molecular diffusion of methane (CH4), ethylene (C2H4), acetylene (C2H2), and benzene (C6H6) were modeled by considering the multi-step hydrocarbon reactions between the species. The hydrocarbon concentration changes, resulting from the carbon deposition on the preform surface, were computed to predict the evolution of the preform density and porosity. The current surface area of the preform was then determined based on the current porosity. The numerical results for the average preform density agreed well with the experimental data. In addition, the present model can provide detailed simulations of the temporal and spatial evolution of the preform density that cannot be experimentally observed. The effectiveness and utility of the developed model could benefit the design of CVI reactors and processes and minimize the need for test runs when processing conditions change.
In the second part, the results obtained in the micromechanical analysis were passed into a meso-scale thick 3D woven textile composite (T3DWC) model, which was a candidate of TPS for a reentry missile. Finite element analysis is performed to virtually measure homogenized thermal and mechanical properties. For the measurements over a wide range of temperature, temperature-dependent thermal and mechanical properties of constituents are considered. A two-step homogenization approach is adopted here. The first-step homogenization is carried out at a tow level using an analytical homogenization scheme as well as the micromechanical analysis in the second part. Fiber tows are homogenized and assigned with effective elastic and thermal properties. The solid tows are then implemented into a representative volume element considering the unique in-plane periodic fiber architecture of the thick composite material. Due to the unique in-plane periodicity, conventional periodic boundary conditions for thermal and mechanical loading conditions are reformulated. Anisotropic thermal conductivity of T3DWC is obtained from the second-step homogenization based on virtual thermal tests performed at ambient to elevated temperatures.
In the third part, the micromechanical behavior of the CVI-produced porous composites materials is studied. Especially, microstructural fracture behavior of a ceramic matrix composite (CMC) with nonuniformly distributed fibers is examined. A comprehensive numerical analysis package to study the effect of nonuniform fiber dimensions and locations on the microstructural fracture behavior is developed. The package starts with an optimization algorithm for generating representative volume element (RVE) models that are statistically equivalent to experimental measurements. Experimentally measured statistical data are used as constraints while the optimization algorithm is running. Virtual springs are utilized between any adjacent fibers to nonuniformly distribute the coated fibers in the RVE model. The virtual spring with the optimization algorithm can efficiently generate multiple RVEs that are statistically identical to each other. Smeared crack approach (SCA) is implemented to consider the fracture behavior of the CMC material in a mesh-objective manner. The RVEs are subjected to tension as well as the shear loading conditions. SCA is capable of predicting different fracture patterns, uniquely defined by not only the fiber arrangement but also the specific loading type. In addition, global stress-strain curves show that the microstructural fracture behavior of the RVEs is highly dependent on the fiber distributions.ope
Real-Time Risk Assessment based on Irregularly Sampled Time Series Data from Wearable Devices
No full textGraduate School of Artificial Intelligenceclos
Synthesis and Self-Organization of Charged Nanocrystals
Get PDFDepartment of Materials Science and EngineeringColloidal nanocrystals (NCs) have attracted tremendous attention because of their unique size-, shape- and composition-dependent properties which offer great potential for their use in wide application fields like electronic, optoelectronic, and energy fields. In particular, the self-organization of these functional building blocks into suprastructures can realize unique multifunctional or collective physicochemical properties that are not observed in individual NCs. Among various strategies to organize NCs, the introduction of charged surfaces to NCs grants an anisotropic driving force like electrostatic interaction, which offers an additional degree to form novel self-assembled structures. Accordingly, there have been significant research efforts to chemically develop the charged NCs by introducing of ionic polymers or long-chain ionic molecules on the NC surfaces. However, these sterically bulky organics generally result in additional collective molecular interactions, atomic dispersion forces, and entropic contributions to affect the organization of NCs. Recently, an alternative surface modification methodology was developed to replace the organic ligands with inorganic anions on the surface of NCs and enabled the synthesis of all-inorganic charged NCs with desired surface charges. The introduction of the ???inorganic ligand??? can rule out the additional intermolecular interactions between surface organics and accordingly provides the ideal model system for understanding the correlation between the building block and the self-organization behavior of the charged nanocrystals.
In this dissertation, the chemical synthesis of charged NCs and their self-organization based on the induced interparticle interactions are described. In the first part, I investigated the self-organization and the colloidal behavior of the oppositely charged all-inorganic NCs based on the electrostatic interactions. All-inorganic charged NCs were selectively synthesized with molecular inorganic ligands or by the treatment with a stripping agent, which introduced the negative or positive charges onto the NC surface, respectively, to rule out the additional interaction from organic species. Three suprastructure phases, including patchy, patchy bridged, and fully coated particles were obtained depending on the charge states of suprastructures. I focused on the correlation between the colloidal behavior of suprastructures, and the size, content ratio, and concentration of oppositely charged NCs, and the phase diagram was constructed according to the NC concentrations. Especially, I observed the behavior of NCs as surface stabilizers exhibiting unexpected colloidal stability in the fully coated phasesthus, I proposed the concept of ???nano-ligands???. This concept was applicable to a wide range of material combinations and enabled the chemical designing of the self-organized suprastructures.
The second part of the dissertation describes the synthesis of matchstick-shaped Janus nano-surfactants and the programmability of the self-assembly with controlled surface amphiphilicity. Molecular amphiphiles are known as promising building blocks for organizing ordered structures through specific and local interactions. Inorganic Janus NCs can impart functionalities in addition to the structural ordering, however, the geometry of Janus NCs was limited to the sphere or dumbbell shape. I synthesized the matchstick-shaped Janus nano-surfactants which mimic the structure of the organic molecular surfactant. The amphiphilic structure was introduced by the selective ligand exchange process based on the hard-soft acid-base theory. Diverse ordered structures such as lamellar, curved, wrinkled, cylindrical, and micellar structures were exhibited depending on the controlled surface amphiphilicity. I aimed to study the correlation between the phase selectivity of suprastructures and the properties of matchstick-shaped nano-surfactants.ope
First sign changes of modular forms for general level N
No full textLet f(z) = En>0 a(n)qn be a cusp form of weight k on Gamma 0(N) with real Fourier coefficients a(n). When N is squarefree, Choie and Kohnen gave a bound that the first sign change of a(n) occurs, and recently it was improved by He and Zhao. In this paper, we compute a bound for the first sign change problem for arbitrary N.(c) 2023 Elsevier Inc. All rights reserved
Remote sensing of sea surface salinity: challenges and research directions
No full textSalinity is a key parameter that affects the surface, deep circulations, and heat transport of oceans. Sea surface salinity (SSS) represents the salinity at the ocean surface and impacts atmosphere - ocean interactions and vertical ocean circulation. To monitor SSS, three passive microwave radiometers with an L-band (1.4 GHz) have been launched since 2009. The scientific need for SSS retrieval and estimation has grown in recent years; however, the operational retrieval of SSS via satellite remote sensing still faces significant challenges. This study provides a review of satellite-based SSS retrieval methods and guidelines to encourage future research. This paper introduces satellite-derived SSS research trends and summarizes the representative SSS satellite sensors and their retrieval methods. The limitations and challenges of satellite-derived SSS are then discussed. The errors from the retrieval algorithms, discrepancies in the spatio-temporal scales of in situ and remote sensing, and limitations of the satellite-derived SSS are then detailed. Finally, our paper provides suggestions for the future directions of SSS remote sensing in five ways: mitigation of measurement errors, improvement of currently available SSS products, enhancement of the usage of in situ data, reconstruction of three-dimensional salinity information, and synergetic uses of multi-satellite missions
Indirect measurement of cutting forces during robotic milling using multiple sensors and a machine learning-based system identifier
No full textRobotic machining has attracted great interest in the fields of aerospace and automotive manufacturing because of its advantages over conventional computer numerical controlled (CNC) machine tools; robotic machining is flexible and can fabricate components with complex (sculpted) geometries and of large dimensions. The principal challenge, however, is the poor structural rigidity of robots, leading to high-level vibration and chatter during machining. The cutting force must be monitored to detect and suppress chatter as well as optimize machining. A practical method is required to measure the cutting force during machining without impeding robot movement. This study presents an indirect method that accurately measures the milling force using an accelerometer located on the spindle and a flange-mounted capacitive force sensor. A Kalman filter was used to compensate for the dynamics between the robot tool center point (TCP) and the location of the accelerometer. In the context of pose -dependent modeling of the dynamics of the robot, a novel machine-learning (ML)-based identifier is presented that rapidly identifies the relevant dynamics (i.e., the modal parameters) without any initial human guesswork. Data from dynamic simulations and experiments were used to train and validate the ML-based identifier. The validation results showed that the identifier rapidly predicted the behaviour of physical systems with an average accuracy of 98.49 % compared to the modal analysis method. Next, the cutting forces were indirectly measured using the acceleration signals and a Kalman filter, developed based on the modal parameters. The forces measured by the proposed method were in good agreement with the reference forces, measured using a table dynamometer; the average peak-to-peak accuracy was 90.17 % for the AC component of the cutting force, and the average time-domain accuracy was 95.27 % when the DC component was also considered
Extremely Shallow Valence Band in Lanthanum Trihydride
No full textHydride ions (H-) in solvents are chemically active anions with strong electron-donating ability and are used as reducing agents in organic chemistry. Here, we evaluate the energy level of 1s-electrons in H- accommodated in solid lanthanum hydrides, LaHx (2 <= x <= 3), by photoemission (ultraviolet photoelectron and photoelectron yield spectros-copies) measurements and density functional theory calculations. We show that a very shallow valance band maximum with an ionization potential of 3.8 eV is attained in LaH3 and that the primary cause is attributed to the small electronegativity of hydrogen and the significant bonding-antibonding interaction between neighboring H(-)s with a close separation originating from the H-stuffed fluorite-related structure. These results encourage the challenge for p-type conduction in hydride semiconductors and provide a clue to the chemical understanding of polyhydride superconductors
Oxidation of Aldehydes into Carboxylic Acids by a Mononuclear Manganese(III) Iodosylbenzene Complex through Electrophilic C-H Bond Activation
No full textThe oxidation of aldehyde is one of the fundamental reactions in the biological system. Various synthetic procedures and catalysts have been developed to convert aldehydes into corresponding carboxylic acids efficiently under ambient conditions. In this work, we report the oxidation of aldehydes by a mononuclear manganese(III) iodosylbenzene complex, [Mn-III(TBDAP)(OIPh)-(OH)](2+) (1), with kinetic and mechanistic studies in detail. The reaction of 1 with aldehydes resulted in the formation of corresponding carboxylic acids via a pre-equilibrium state. Hammett plot and reaction rates of 1 with 1 degrees-, 2 degrees-, and 3 degrees- aldehydes revealed the electrophilicity of 1 in the aldehyde oxidation. A kinetic isotope effect experiment and reactivity of 1 toward cyclohexanecarboxaldehyde (CCA) analogues indicate that the reaction of 1 with aldehyde occurs through the rate-determining C-H bond activation at the formyl group. The reaction rate of 1 with CCA is correlated to the bond dissociation energy of the formyl group plotting a linear correlation with other aliphatic C-H bonds. Density functional theory calculations found that 1 electrostatically interacts with CCA at the pre-equilibrium state in which the C-H bond activation of the formyl group is performed as the most feasible pathway. Surprisingly, the rate-determining step is characterized as hydride transfer from CCA to 1, affording an (oxo)methylium intermediate. At the fundamental level, it is revealed that the hydride transfer is composed of H atom abstraction followed by a fast electron transfer. Catalytic reactions of aldehydes by 1 are also presented with a broad substrate scope. This novel mechanistic study gives better insights into the metal oxygen chemistry and would be prominently valuable for development of transition metal catalysts
Synthesis of Thermally Stable and Highly Luminescent Cs5Cu3Cl6I2 Nanocrystals with Nonlinear Optical Response
No full textLow-dimensional Cu(I)-based metal halide materials are gaining attention due to their low toxicity, high stability and unique luminescence mechanism, which is mediated by self-trapped excitons (STEs). Among them, Cs5Cu3Cl6I2, which emits blue light, is a promising candidate for applications as a next-generation blue-emitting material. In this article, an optimized colloidal process to synthesize uniform Cs5Cu3Cl6I2 nanocrystals (NCs) with a superior quantum yield (QY) is proposed. In addition, precise control of the synthesis parameters, enabling anisotropic growth and emission wavelength shifting is demonstrated. The synthesized Cs5Cu3Cl6I2 NCs have an excellent photoluminescence (PL) retention rate, even at high temperature, and exhibit high stability over multiple heating???cooling cycles under ambient conditions. Moreover, under 850-nm femtosecond laser irradiation, the NCs exhibit three-photon absorption (3PA)-induced PL, highlighting the possibility of utilizing their nonlinear optical properties. Such thermally stable and highly luminescent Cs5Cu3Cl6I2 NCs with nonlinear optical properties overcome the limitations of conventional blue-emitting nanomaterials. These findings provide insights into the mechanism of the colloidal synthesis of Cs5Cu3Cl6I2 NCs and a foundation for further research
Comparison of different machine learning algorithms to estimate liquid level for bioreactor management
Get PDFEstimating the liquid level in an anaerobic digester can be disturbed by its closedness, bubbles and scum formation, and the inhomogeneity of the digestate. In our previous study, a soft-sensor approach using seven pressure meters has been proposed as an alternative for real-time liquid level estimation. Here, machine learning techniques were used to improve the estimation accuracy and optimize the number of sensors required in this approach. Four algorithms, multiple linear regression (MLR), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) with radial basis function kernel were compared for this purpose. All models outperformed the cubic model developed in the previous study, among which the ANN and RF models performed the best. Variable importance analysis suggested that the pressure readings from the top (in the headspace) were the most significant, while the other pressure meters showed varying significance levels depending on the model type. The sensor that experienced both headspace and liquid phases depending on the level variation incurred a higher error than other sensors. The results showed that the ML techniques can provide an effective tool to estimate digester liquid levels by optimizing the number of sensors and reducing the error rate