The work presented in this paper estimates the spectral radiance emitted from plasma induced by the interaction of hypervelocity moving structural/material systems with the atmosphere. The motivation for this effort originates from the need to compute the radiative heat fluxes imparted to hypersonic vehicles to facilitate their design, control, and maintenance. In response to this need, a computational framework was established to predict the fluid dynamics fields around a hypervelocity vehicle that in turn is coupled with the plasma physics that enables the calculation of the plasma fields and species dynamics. This framework implements a one-way coupling between fluid dynamics and plasma physics models. The framework solves the fluid dynamics partial differential equations representing the conservation of mass, momentum, and energy. The computed pressure, velocity, and temperature fields are subsequently utilized to drive the plasma physics PDEs describing the mass transport and energetics of all the nitrogen-oxygen 11 species present according to the socalled Dunn plasma model. An application of this framework for a spherical body for a wide range of velocities is presented as a verification of the framework's functionality. Typical distributions of the fluid and plasma dynamics are presented. Finally, the plasma radiance spectra are produced by employing statistical mechanics principles.

The continuous progress of additive manufacturing techniques has enabled engineers and designers to build complex geometries at various length scales with minimal setup time, reduced need for skilled labor, and minimal material waste. One important subset of structures endowed with complex geometrical features is formed by periodic and non-periodic metamaterials that enable engineering applications where certain combinations of performance features are desirable. For example, these structures could be used in naval engineering applications where light-weight, large surface area, energy absorption, heat dissipation, and acoustic bandgaps are critical. Nevertheless, to deploy these complex geometry structures, their multiphysics response must be well understood and characterized. The current effort aims to describe an initial approach for designing and deploying graded triply periodic minimal surface (TPMS) architectures. The work focuses on three TPMS systems: (a) Neovius, (b) Schoen's gyroid, and (c) Schoen's F-RD made of Ti-6Al-4V alloy. These three geometries are enhanced using variable wall thickness to obtain the so-called graded TPMS. Three problems are explored: (1) evaluation of the elastic equivalent material properties of various unit cells with graded thickness; (2) investigation of acoustics dispersion properties of three cells and (3) a thermo-structural response of a complex geometry made of twelve graded representative volume elements (RVE). Finite element results of the relevant thermo-structural partial differential equations for the case of a gyroid triply periodic cylindrical sandwich structure are presented.

The use of topology optimization algorithms for engineering design has become widespread. Motivated by the objective of producing lightweight, high-performance components, a wide variety of approaches have emerged, and many alternatives now exist for every stage of the associated computing pipeline. The present work introduces a generalized topology optimization framework for use in the area of additive manufacturing (AM), with a focus on naval applications. The development of this framework is outlined, with particular attention paid to the development of an anisotropic experimental data-driven material constitutive model. Additionally, methods for handling arbitrary numbers of objectives, design constraints imposed by additive manufacturing process physics, and boundary conditions particular to naval applications are explored. Steps for post-processing and refining the optimizer output in order to produce manufacturing-ready files are also discussed. The approach is demonstrated on a pair of test cases. The first is the design of an optimized "conformal" pressure vessel, which departs from the typical sphere / cylinder forms and allows for increased storage capacity by occupying a non-convex envelope space. The second application problem is the design of an optimized propulsor intended to be produced using large-scale AM. This propulsor is optimized for inertial and hydrodynamic loads, and a parametric study of the effect of outer shell thickness is performed.

Materials science requires the collection and analysis of great quantities of data. These data almost invariably require various post-acquisition computation to remove noise, classify observations, fit parametric models, or perform other operations. Recently developed machine-learning (ML) algorithms have demonstrated great capability for performing many of these operations, and often produce higher quality output than traditional methods. However, it has been widely observed that such algorithms often suffer from issues such as limited generalizability and the tendency to "over fit" to the input data. In order to address such issues, this work introduces a metacomputing framework capable of systematically selecting, tuning, and training the best available machine-learning model in order to process an input dataset. In addition, a unique "cross-training" methodology is used to incorporate underlying physics or multiphysics relationships into the structure of the resultant ML model. This metacomputing approach is demonstrated on four example problems: repairing "gaps" in a multiphysics dataset, improving the output of electron back-scatter detection crystallographic measurements, removing spurious artifacts from X-ray microtomography data, and identifying material constitutive relationships from tensile test data. The performance of the metacomputing framework on these disparate problems is discussed, as are future plans for further deploying metacomputing technologies in the context of materials science and mechanical engineering.

Volumetric material manufacturing involves forming a solid shape within a precursor powder volume as a whole instead of the traditional layer by layer technique used for most additive manufacturing methodologies. This work presents an inverse technique for designing masking parameters in order to generate a predetermined shape within the precursor powder volume. The model utilizes a microwave cavity for inducing heat in BaTiO3 precursor powder via Joule heating. Material sintering and microwave propagation is modeled by coupling electromagnetics and heat conduction using finite element discretization. This interaction is complicated by the nonlinear thermal dependence of dielectric properties of BaTiO3 (i.e. density, specific heat, dielectric constant and loss tangent), which are considered here. Heat can be localized within the powder volume using one or multiple conductive masks that modulate incident energy. Utilizing both 3D and 2D models, it is demonstrated that an arbitrary nonuniform mask can create nonuniform heating in the powder volume. An experimental framework is then established to conduct a forward experiment parameterized with five masking and cavity design variables causing different powder heating patterns. Then an inverse problem is solved by specifying a desired shape and outputting a mask and cavity design capable of generating said shape. It is demonstrated that mask parameters can be optimized in order to produce a desired shape by this inverse method.

The advent of additive manufacturing (AM) has enabled the prototyping of periodic and non-periodic metamaterials (a.k.a. lattice or cellular structures) that could be deployed in a variety of engineering applications where certain combinations of performance features are desirable. For example, these structures could be used in a variety of naval engineering applications where light-weight, large surface area, energy absorption, heat dissipation, and acoustic bandgaps are critical. Furthermore, combining the multifunctional design optimization of these structures with progressive degradation due to cyclic fatigue would create attritable systems with tailorable performances not yet in reach by current conventional systems. Nevertheless, in order to deploy these complex geometry structures their multiphysics response has to be well understood and characterized. The objective of the current effort is to describe an initial approach for designing a uniaxial fatigue specimen as the first step toward the design of a multiaxial fatigue test coupon. In order to compare bending- and stretching-dominated structures, two strut-based lattices made of Ti-6Al-4V alloy consisting of the octet and tetrakaidecahedron (or Kelvin) cells are examined. The specimens are designed to fail in the central gauge area where edge effects are minimized. Finite element results of the relevant structural mechanics are used to compare the performance of the four geometries and to evaluate the effect of relative density on fatigue life.

The presence of gaps and spurious non-physical artifacts in datasets is a nearly ubiquitous problem in many scientific and engineering domains. In the context of multiphysics numerical models, data gaps may arise from lack of coordination between modeling elements and limitations of the discretization and solver schemes employed. In the case of data derived from physical experiments, the limitations of sensing and data acquisition technologies, as well as myriad sources of experimental noise, may result in the generation of data gaps and artifacts. In the present work we develop and demonstrate a machine learning (ML) meta-framework for repairing such gaps in multiphysics datasets. A unique “cross-training” methodology is used to ensure that the ML models capture the underlying multiphysics of the input datasets, without requiring training on datasets free of gaps/artifacts. The general utility of this approach is demonstrated by the repair of gaps in a multiphysics dataset taken from hypervelocity impact simulations. Subsequently, we examine the problem of removing scan artifacts from X-ray computed micro-tomographic (XCMT) datasets. A unique experimental methodology for acquiring XCMT data, wherein articles are scanned multiple times under different conditions, enables the ready identification of artifacts, their removal from the datasets, and the filling of the resulting gaps using the ML framework. This work concludes with observations regarding the unique features of the developed methodology, and a discussion of potential future developments and applications for this technology.

This work is motivated by the need to modulate microwave beam propagation, phase, shape, and direction using an array of plasma elements. Tailoring microwave beams in this fashion will enable new material processing capabilities such as induced localized heating. An initial process example consists of a cylindrical plasma element inserted into a waveguide between a microwave radiation source and a material of interest. In order to establish the feasibility of the proposed process, an accurate model of an argon mercury plasma including plasma-microwave coupling was developed and described herein. Both microwave plasma heating and magnetic plasma couplings are considered. The required computational framework was implemented within the COMSOL Multiphysics finite element solver. The model is first used to investigate a 2D geometry, before being extended to a 3D geometry. The obtained solutions of the relevant partial differential equations and the associated predictions increased the understanding of the interplay between plasma and electromagnetic properties under consideration in the model. Model implementation confirms that a plasma element can be used to modulate incident microwave radiation, thereby shaping the transmitted beam. The framework also enables analysis of beam shaping techniques under consideration for material processing of ceramics. For ceramic processing, beam shaping techniques are being used to direct microwave radiation to predictable, localized areas in order to sinter the dielectric powders under consideration.

The process of sintering occurs when enough heating energy is applied on the particles of precursor powders to coalesce together and form a solid material without melting. Solidification takes place through cross mass diffusion along common interfaces and this technique has been used extensively by the materials processing community for ceramic part manufacturing. However, in most cases, furnaces are being used to elevate the temperature of material powder precursors globally throughout the entire volume of the intended parts. Instead of this approach, the present work explores the feasibility of using localized heating induced by coherent microwave radiation. Microwave-based material processing involves coupling between thermal and electromagnetic physics where the microwave radiation heats the sample locally via volumetrically tailored heat fluxes. However, changes in temperature change the dielectric properties of the sample, which then in turn affect microwave propagation. The nonlinearity introduced by the temperature dependence of the material properties into the relevant partial differential equations of this coupled system is further complicated by poorly defined dielectric, thermal, and thermo-electric properties of the dielectric precursor powders at temperatures required for sintering. This work focuses on analyzing a TE106 2.45 GHz microwave cavity used for processing BaTiO3, or BTO, precursor powder. Both a physical and a virtual experiment were carried out in tandem to understand the microwave propagation and dielectric property evolution with respect to temperature. It was demonstrated that appropriate tuning of the material properties (i.e., density, specific heat, heat conductivity, dielectric permittivity and loss tangent) relative to temperature enabled localized heating predicted by our model to match that of the physical experiment.

X-ray tomography (XCT) and microtomography (uCT) are powerful experimental techniques for determining the internal structure of materials and objects. However, the physics governing these systems, particularly the myriad of complex interactions between X-rays and materials, lead to the frequent generation of spurious data “artifacts.” When these techniques are used to determine the quantitatively precise dimensions and morphology of defects and other features present in the objects under study, the presence of these artifacts is highly deleterious. A computational framework for simulating and studying tomographic processes, and the physical origins of such artifacts, may increase the overall utility of these techniques. This work presents the introduction, development, and demonstration of such a framework based on a ray-marching approach. A number of physics-driven and computationally-driven considerations guiding the development of this framework are discussed. A demonstration problem taken from prior literature is examined, and it is shown that even a basic implementation of this framework offers meaningful insight which can be used to improve quantitative measurements made using XCT. We conclude with remarks regarding the usage of this technique in a broader scope, and the work required to approach such tasks.

The microwave sintering of ceramics and other materials has emerged as an attractive method of manufacturing solid objects though volumetric approaches. The accurate modeling of such processes requires the knowledge of the dielectric constant, and particularly the real and imaginary parts of the permittivity, of these materials as they vary with temperature. This particular measurement becomes very challenging as the temperature rises. In this work, an experimental apparatus and an inverse approach are proposed, based on the coupling of the thermo-mechano-electromagnetic physics that can be used to measure the real and imaginary parts of the dielectric constant at high temperatures.

Technologies for material defect detection/metrology are often based on measuring the interactions between defects and waves. These interactions frequently create artifacts that skew the quantitative character of the relevant measurements. Since defects can have a significant impact on the functional behavior of the materials and structures they are embedded in, accurate knowledge of their geometric shape and size is necessary. Responding to this need, the present work introduces preliminary efforts toward a multiscale modeling and simulation framework for capturing the interactions of waves with materials bearing defect ensembles. It is first shown that conventional approaches such as ray tracing result in excessive geometric errors. Instead, a more robust method employing solutions to the wave equation (calculated using the Finite Element Method) is developed. Although the use of solutions to the general wave equation permits application of the method to many wave-based defect detection technologies, this work focuses exclusively on the application to X-ray computed tomography (XCT). A general parameterization of defect geometries based on superquadratic functions is also introduced, and the interactions of defects modeled in this fashion with X-rays are investigated. A synthetic two-dimensional demonstration problem is presented. It is shown that the combination of parameterization and modeling techniques allows the recovery of an accurate, artifact-free defect geometry utilizing classical inverse methods. The path forward to a more complete realization of this technology, including extensions to other wave-based technologies, three-dimensional problem domains, and data derived from physical experiments is outlined.

Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions.

Ceramic powders are commonly used as precursors for several ceramic part manufacturing processes. Their dielectric characterization is necessary for all the cases where electromagnetic radiation is used to induce heating. In support of such activities at the U.S. Naval Research Laboratory, the present work introduces a technique for measuring the complex dielectric constants of ceramic powders at microwave frequencies. The data produced by this technique is critical for the proper modeling of ceramic powder microwave absorption and will assist in ongoing research into volumetric microwave sintering. This technique involves a transmission line measurement using a network analyzer and waveguide setup. Dielectric parameters are then extracted from these measurements using two established methods. Complex relative permittivity measurements are presented for ceramic powders ofyttria stabilized zirconia (YSZ), barium titanate (BaTiO3), zinc oxide (ZnO), and titanium dioxide (TiO2) as well as a method for containing the sample shape during measurement. Experiments were carried out between 25 and 40GHz; in the Ka microwave band. Experimental results suggest that the dielectric constant (ε′) of these powders are similar to those of bulk. Results are compared to lower frequency reference data showing reasonable agreement.

Contemporary material testing applications such as high throughput material testing under realistic conditions, emulation of in-service loading conditions for the qualification of additively manufactured parts, material failure and damage propagation modeling validation and material constitutive characterization, are all underscoring the demand for an automated multiaxial testing capability. In order to address these needs, the present work introduces the initial progress of the design and prototyping of a 6 degrees offreedom (6-DoF) robotic system to be used as such a testing infrastructure. This system is designed to be capable of enforcing 6-DoF kinematic orforce controlled boundary conditions on deformable material specimens, while at the same time measuring both the imposed kinematics and the corresponding reaction forces in a fully automated manner. Furthermore, as an extension to our previously prototyped systems, the system proposed here is designed to apply both quasi -static loading but also cyclic loading for enabling multiaxial fatigue studies. In addition to the architecture, the design and current status of its implementation for the most critical sub -systems is presented.

The development of advanced additive manufacturing (AM) and material processing techniques is currently a topic of great interest to broad communities of scientists and engineers. In particular, there is a need for AM processes capable of producing functional and high-quality components at a faster rate than is currently achievable. In response to this demand, the present work introduces the initial steps of a novel spatially-resolved and selective approach for processing volumetric regions of ceramic materials. The proposed method utilizes microwave radiation to heat material at desired locations within a domain filled with ceramic powder. Using this principle of operation, a number of methods for implementation of this process are proposed. As a first step, a multiphysics computational methodology and an associated model that allows for the analysis and design of relevant processing systems is introduced. Additionally, a number of simulations demonstrating the feasibility of the proposed methodology are presented. Based on these preliminary results, we conclude with a discussion of ongoing and future efforts to fully realize this technology.

Fast detection and identification of trace gases in ambient conditions demand a high signal-to-ratio (SNR) and superior resolution from a single measurement. We performed time-domain terahertz (THz) spectroscopy on gas (or vapor) phase samples of carbon monoxide, methanol, water, and acetonitrile at concentrations less than 10 ppm and demonstrated 50–60 dB SNR on a single measurement. Using data measured at different sample concentrations and terahertz probe beam powers, we investigated the interplay between the SNR, the probe beam power, the sample concentration, and the electric dipole moment of molecules, for which we extended an absorbance theory to THz frequencies. When comparing our data with references and a theoretical model, we found some discrepancy in certain spectral line intensities, suggesting that certain rotational resonance quantum state may have higher populations or transition probabilities than our model predicts at atmospheric conditions. We could achieve the above results largely due to our high power THz source capable of generating up to 3 mW, an order of magnitude greater than those available commercially.