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At least 235 records · Page 13

Adaptively coupled phase retrieval in multi-peak Bragg coherent diffraction imaging

Recent advances in Bragg coherent diffraction imaging (BCDI) experimental techniques permit routine measurement of multiple Bragg peaks from a single crystalline grain. The resulting images contain the full lattice distortion vector field which can be differentiated to provide lattice strain and rotation. With the advent of fourth-generation synchrotron light sources, such multi-peak datasets are produced at high rates, facilitating the need for rapid phase retrieval of the multiple peaks and subsequent image analysis. Here we describe and demonstrate a new implementation of a coupled phase retrieval technique for multi-peak BCDI which simultaneously treats each Bragg peak of the dataset and produces a three-dimensional image of the crystal's morphology and lattice distortion field. In addition, this method uses the redundant information contained in the various Bragg diffraction patterns to detect and suppress spurious signal appearing on the detector in a subset of the measurements. Compared with manual data editing, adaptive coupling produces a more consistent phase profile in reciprocal space and sharper surfaces in direct space, with no significant difference in computational cost. These improvements reduce the need for manual preprocessing and enable robust high-throughput analysis of multi-peak BCDI data, supporting near-real-time strain microscopy at modern synchrotron facilities.

36 MATERIALS SCIENCE↗

Passive Energy-Saving Solutions for Clothes Dryers: A Modeling and Experimental Study for Improved Efficiency and Affordability

Dryers are integral appliances in modern households, yet their significant power consumption remains a critical challenge for reducing energy bills and upgrades. This work presents the outcomes of a comprehensive investigation aimed at lowering the operating energy costs of the dryer through modeling and experimental approaches. Specifically, the objectives of this research are twofold: (1) to reduce energy consumption without negatively impacting drying performance or time; and (2) to ensure affordability by developing retrofittable solutions characterized by low cost and a quick payback period. These strategies promise universal applicability to all dryer categories, encompassing both gas and electric models, by tackling core issues such as unnecessary heat loss and excess energy supply, both prevalent across dryer designs. Our methodology combines robust modeling frameworks and experimental validation to target energy efficiency improvements through three primary pathways: (1) effective heat loss management using ultralow-cost insulation materials tailored for dryer systems; (2) heat management across the drying cycle, facilitated by passive heat transfer mechanisms, and (3) optimization of heat supply to the load to ensure precise energy delivery, minimizing waste. These innovations are designed to seamlessly integrate with existing dryer configurations, providing a scalable and retrofittable solution that ensures affordability without requiring substantial redesigns or expensive components. Using these methods, the study demonstrates improvement opportunities in dryer energy consumption while maintaining the desired performance. The modeling component utilizes computational simulations to evaluate the thermal and energy performance of these innovations under varied operational conditions, providing foundational insights for experimental design. By addressing critical areas such as heat loss, energy recovery, and supply optimization, this work proposes impactful solutions for lowering the energy costs in domestic clothes dryers. The ultralow-cost, retrofittable nature of the proposed strategies ensures widespread adoption potential across diverse dryer categories, making significant strides toward affordable household energy practices.

Cheekatamarla, Praveen [ORNL] (ORCID:0000000248827↗

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗

Open Circuit Resonant (SansEC) Sensor for Composite Damage Detection and Diagnosis in Aircraft Lightning Environments

Composite materials are increasingly used in modern aircraft for reducing weight, improving fuel efficiency, and enhancing the overall design, performance, and manufacturability of airborne vehicles. Materials such as fiberglass reinforced composites (FRC) and carbon-fiber-reinforced polymers (CFRP) are being used to great advantage in airframes, wings, engine nacelles, turbine blades, fairings, fuselage and empennage structures, control surfaces and coverings. However, the potential damage from the direct and indirect effects of lightning strikes is of increased concern to aircraft designers and operators. When a lightning strike occurs, the points of attachment and detachment on the aircraft surface must be found by visual inspection, and then assessed for damage by maintenance personnel to ensure continued safe flight operations. In this paper, a new method and system for aircraft in-situ damage detection and diagnosis are presented. The method and system are based on open circuit (SansEC) sensor technology developed at NASA Langley Research Center. SansEC (Sans Electric Connection) sensor technology is a new technical framework for designing, powering, and interrogating sensors to detect damage in composite materials. Damage in composite material is generally associated with a localized change in material permittivity and/or conductivity. These changes are sensed using SansEC. Unique electrical signatures are used for damage detection and diagnosis. NASA LaRC has both experimentally and theoretically demonstrated that SansEC sensors can be effectively used for in-situ composite damage detection.

Wang, Chuantong↗

On the Role of Friction and Particle Size Distribution in Granular Packings

Packing of particles in a disordered arrangement has tremendous significance in both condensed matter physics and engineering applications. The last three decades have seen remarkable progress in our understanding of the physics of granular packings that has been largely facilitated by a rapid growth in the power of modern computers. Although granular packings are ubiquitous in diverse natural settings, from clogging of powders in hoppers to the crowding of living cells, a significant motivation for modeling granular packings has emerged from a proposal that the transition from a fluid-like state to a solid-like state of a granular material upon increasing volume fraction, called jamming, is intimately related to the origins of glass transition in thermal systems. As such, a majority of modeling efforts have focused on the jamming behavior of an idealized granular material: frictionless, monodisperse sphere. While such studies have illuminated the rich physics of jamming, granular materials in nature and engineering practice are rarely frictionless or monodisperse. The analogous research on the packing of these ‘real-world’ granular materials is still not fully developed. Besides requiring the exploration of a huge parameter space, three key computational considerations have inhibited their modeling: (i) traditional computational methods are not adept at simulating mechanically-stable packings of frictional particles near the jamming transition; (ii) standard algorithms of contact detection in discrete element methods are impractical to simulate granular packings with a wide distribution of particle sizes; (iii) a lack of well-established contact mechanics models of friction that can accurately reproduce experimental data. This chapter will review the latest computational advances to simulate the jamming of size-dispersed frictional particles, and describe the rich microstructural diversity that emerges in their packings.

granular↗

Numerical Simulation of a Simplified High-Lift CRM Configuration Embedded with Fluidic Actuators

Numerical simulations have been performed for a simplified high-lift configuration that is representative of a modern transport airplane. This configuration includes a leading-edge slat, fuselage, wing, nacelle-pylon and a simple hinged flap. The suction surface of the flap is embedded with multiple rows of fluidic actuators to reduce the extent of reversed flow regions and improve the aerodynamic performance of the configuration with flap in a deployed state. In the current paper, a Lattice Boltzmann Method based high-fidelity computational fluid dynamics (CFD) code, known as PowerFLOW® is used to simulate the entire flow field associated with this configuration, including the flow inside the actuators. A fully compressible version of the PowerFLOW® code that has been validated for high speed flows is used for the present simulations to accurately represent the transonic flow regimes that are encountered in the flow field generated by the actuators operating at higher mass flow (momentum) rates required to mitigate reverse flow regions on the suction surfaces of the main wing and the flap. The numerical solutions predict the expected trends in aerodynamic forces as the actuation levels are increased. More efficient active flow control (AFC) systems and actuator arrangement for lift augmentation are emerging based on the parametric studies conducted here prior to wind tunnel tests. These numerical solutions will be compared with experimental data, once such data becomes available.

Vatsa, Veer N.↗

Development of a Beam Halo Monitor

Muons, Inc. proposed improved Residual-Gas luminescent Beam Profile Monitors for Intense Beams by adding novel features including a mirascope, a light collecting device devised in the 16th century by an inventor of the telescope. A modern mirascope consists of 2 paraboloids. Light generated by the beam is collected by the paraboloids and creates a beam image. By placing a lens near the image it is possible to collect almost 100% of the emitted light, up to ten times better than existing fluorescence profilometers. Intense (multi-MW) proton beams pose a challenge for beam profile measurements. Solid-based beam intercepting instrumentation produces unallowable levels of radiation at these high powers. An alternative is to use a zero-or-low mass device such as a residual-gas profile monitor, either through collection of ionization or fluorescence. Fluorescence has an intrinsic advantage over the usual collection of ionized electrons in that it is not affected by the strong and variable electric and magnetic fields created by the beam that can distort profiles and make calibration difficult. For this reason, we proposed the fluorescence technique using the mirascope and other methods to enhance light collection and versatility by modifying the residual gas and its spatial distribution. In Phase I, we designed a mirascope and its electronics for commissioning and beam tests in a proposed Phase II. The design was based on optical simulations done in Phase I, and a physics run plan was developed for optimizing and testing. The Phase II experimental site and program have been planned and our goal is to produce a universal low cost Mirascope Residual-Gas Luminescent Beam Profile Monitor that can replace all other profile monitors currently used in accelerators.

43 PARTICLE ACCELERATORS↗

Self-Consistent Relativistic Electron Scattering using the Sherlock Scattering Model for X-ray Diagnostics

We present on a new, self-consistent, arbitrary-temperature Romberg integration scheme for modeling electron scattering in materials in a LANL Lagrangian Shock Hydro (LSH) code. Electron beam-target interactions are fundamental to a wide range of scientific and technological applications. When high-energy electron beams hit their target, they may scatter, deposit energy, or ionize the source. These processes govern the behavior and outcomes in nanotechnology manufacturing, electron microscopy, and modern X-ray diagnostics. Simulating these interactions is essential for interpreting experimental results, predicting material responses, and designing efficient tools and experiments. At Los Alamos, this is done using a LSH code, which is a multi-dimension, multi-material, massively parallel, multi-physics code used to simulate applications from asteroid impacts to electron beam interactions. By effectively and efficiently modeling the way that electrons scatter from the beam we can bolster these simulations and more accurately predict experimental outcomes. The model currently implemented in the LSH of interest is based on work by Papp and does not self-consistently preserve momentum in the slightly relativistic regime; here we adopt a model proposed by Braams and Karney and implement a Romberg integration scheme to compute the diffusion tensor. In this paper we will provide background on the Braams-Karney diffusion tensor as well as the Romberg integration scheme we employed to numerically solve for it. We will show that our integration scheme is accurate in solving for the set of scalar potentials used to re-express the diffusion tensor in differential form, and in solving for the diffusion coefficients in the larger LSH code. By using this diffusion tensor rather than the existing Papp one, and numerically integrating it with a Romberg method, we produce much more accurate, self-consistent results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Second-harmonic generation tensors from high-throughput density-functional perturbation theory

Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density-functional perturbation theory for a set of 579 inorganic semiconductors. The computational methodology is discussed and the provided database is described with respect to both its data distribution and its format. Several comparisons with both experimental and ab initio results from literature allow to confirm the reliability of our data. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗

Integrated Computational-Experimental Development of Lithium-Air Batteries for Electric Aircraft

The primary obstacle to enable NASA's vision of Green Aviation is the extraordinary energy storage requirements for electric aircraft. Significant advances in high energy, rechargeable, safe batteries are required to enable electric aviation. Boeing's SUGAR and NASA studies have identified 400 Wh/kg as the threshold energy density for general aviation and 750 Wh/kg for commercial regional air service. State of the Art Lithium Ion Battery (LIB) technology currently has a density of 200 Wh/kg and is expected to plateau at 300 Wh/kg due to fundamental chemistry limitations making it unsuitable for future electric aircraft. Additional demanding requirements include high power, rechargeability, and high safety. Such battery technology does not currently exist. The recent considerable activity in battery research (DOE, Tesla Gigafactory, etc) overwhelmingly has been geared towards reducing cost and improving safety of LIB technology in order to promote the adoption of electric automobiles; and thus it is expected to have little impact on electric aviation development. New battery materials will be needed for the "Beyond Li Ion" (BLI) technologies required for high energy, safe electric aviation. Li-Air batteries have the highest known theoretical energy density (3400 Wh/kg) and therefore and if realized promises to transform the global transportation system. These high energy batteries have the potential to meet the energy storage challenges of current and future NASA aeronautics and space missions in addition to many terrestrial transportation applications as well. However, this technology requires significant components development and integration, as it is currently unable to achieve aircraft requirements. The objective of this project is to leverage modern computational materials methods combined with battery multiphysics tools to develop radically advanced compatible cathode and electrolyte materials, build several Li-Air cells, and flight-demonstrate the corresponding Li-Air battery packs. A significant problem for current Lithium-Air batteries is large scale decomposition of the battery electrolyte during operation leading to battery failure after a handful of charge/discharge cycles. Therefore, development of large scale, ultra-high energy, rechargeable, and safe Lithium-Air batteries require highly stable electrolytes that are resistant to decomposition under operating conditions. A NASA-based cross-organizational "dream team" of high-powered experts combined integrated supercomputer modeling, fundamental chemistry analysis, advanced material science, and battery cell development to tackle this very challenging, multidisciplinary problem. The ultimate goal for the team is to develop an integrated experimental/computational infrastructure to produce a reliable predictive capability for the selection of optimal components, their fabrication parameters, and "design rules" of novel cell components for advanced ultra-high energy batteries that can meet energy storage challenges of NASA missions and many terrestrial transportation applications.

Li-air battery↗

Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor

Thermoacoustic instability (TAI) is a critical issue in modern lean-burn gas-turbine combustors, which is induced by a strong coupling between the resonant combustor acoustics and fluctuations in the heat release rate. This instability may lead to high-amplitude pressure waves that generate undesirable noise levels as well as fatigue stresses in mechanical structures of the combustor. The intense pressure fluctuations due to TAI may also cause large flow perturbations and possibly flow reversal that may lead to flame oscillations, flame liftoff, and even flame blow-out. Hence, there is a strong need for exercising control actions in a timely fashion to mitigate the TAI phenomena. Anomaly detection is an essential prerequisite to the design of a good controller and such a detector must be able to reliably predict a forthcoming TAI. To detect and predict the onset of a TAI from an ensemble of pressure time series, this paper investigates three data-driven methods: Fast Fourier transform (FFT), symbolic time series analysis (STSA), and hidden Markov modeling (HMM). The main focus of the paper is to make a comparative evaluation of these three anomaly detection methods for classification of the current regime of operation into stable and unstable categories as well as for real-time identification of precursors to impending instabilities with short-length time series of measured variables (e.g., pressure oscillations). Finally, the results, generated on experimental data from a multi-nozzle combustor apparatus, have been compared to evaluate the performance of FFT, STSA, and HMM methods for TAI analysis.

42 ENGINEERING↗

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗

Precise Frequency Measurements Using a Superconducting Cavity Stabilized Oscillator

Many physics experiments call on improved resolution to better define the experimental results, thus improving tests of theories. Modern microwave technology combined with high-Q resonators can achieve frequency readout and control with resolutions up to a part in 10(exp 18). When the physical quantity in question in the experiment can be converted to a frequency or a change in frequency, a high-stability microwave oscillator can be applied to obtain state-of-the-art precision. In this work we describe the overall physical concepts and the required experimental procedures for optimizing a high-resolution frequency measurement system that employs a high-Q superconducting microwave cavity and a low-noise frequency synthesizer. The basic approach is to resolve the resonant frequencies of a high-Q (Q > 10(exp 10)) cavity to extremely high precision (one part in 10(exp 17)- 10(exp 18)). Techniques for locking the synthesizer frequency to a resonant frequency of the superconducting cavity to form an ultra-stable oscillator are described. We have recently set up an ultra-high-vacuum high-temperature annealing system to process superconducting niobium cavities, and have been able to consistently achieve Q > 10(exp 9). We have integrated high-Q superconducting cavities with a low-noise microwave synthesizer in a phase-locked-loop to verify the frequency stability of the system. Effects that disturb the cavity resonant frequency (such as the temperature fluctuations and mechanical vibrations) and methods to mitigate those effects are also considered. Applicability of these techniques to experiments will be discussed, and our latest experimental progress in achieving high-resolution frequency measurements using the superconducting-cavity-stabilized-oscillator will be presented.

Strayer, D. M.↗

A substandard candle: the low-$$\nu $$ method at few-GeV neutrino energies

Abstract As accelerator-based neutrino oscillation experiments improve oscillation parameter constraints with more data, control over systematic uncertainties on the incoming neutrino flux and interaction models is increasingly important. The intense beams offered by modern experiments permit a variety of options to constrain the flux using in situ “standard candle” measurements. These standard candles must use very well understood interaction processes to avoid introducing additional interaction model dependence. One option often discussed in this context is the “low- $$\nu $$ ν ” method, which is designed to isolate neutrino interactions where there is low energy-transfer to the nucleus, such that the interaction cross section is expected to be approximately constant as a function of neutrino energy. The shape of the low-energy transfer event sample can then be used to extract the flux shape. Applications of the method at high neutrino energies (many tens of GeV) are well understood. However, the applicability of the method at the lower energies of current and future few-GeV accelerator neutrino experiments remains unclear due to the presence of nuclear and form-factor effects inherent in the interaction models.In this analysis we examine the prospects for improving constraints on the accelerator neutrino fluxes in situ with the low- $$\nu $$ ν method in an experiment-independent way, using (anti)neutrino interactions on argon and hydrocarbon targets from the GENIE, NEUT, NuWro and GiBUU event generators. We begin by investigating the extent to which deviations from the constant cross-section assumption are dependent on poorly understood aspects of the neutrino interaction model. We then assess whether a low energy-transfer event sample can be confidently identified using experimentally accessible observables. We finally consider how the practicalities of reconstructing the energy spectrum of interacting neutrinos in realistic detectors might further limit the utility of low- $$\nu $$ ν flux constraints. The results show that flux constraints from the low- $$\nu $$ ν method would be severely dependent on the interaction model assumptions used in an analysis of neutrinos with energies below 5 GeV, and anti-neutrinos below at least 15 GeV. The spread of model predictions show that a low- $$\nu $$ ν analysis is unlikely to offer much improvement on typical neutrino flux uncertainties, even with a perfect detector. Notably—running counter to the assumption inherent to the low- $$\nu $$ ν method—the model-dependence increases with decreasing energy transfer for experiments in the few-GeV region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A reaction–diffusion model for grayscale digital light processing 3D printing

We report that digital light processing (DLP) 3D printing is an additive manufacturing process that utilizes light patterns to photopolymerize a liquid resin into a solid. Due to the accuracy of modern digital micromirror devices (DMD) and recent advances in resin chemistry, it is now possible to create functionally graded structures using different light intensity values, also known as grayscale DLP (g-DLP). Different intensities of light lead to differences in the polymer crosslinking density after curing, which ultimately produces a part with gradients of material properties. However, g-DLP is a complicated process. First, the DLP printing is a highly coupled chemical and physical process that involves light propagation, chemical reactions, species diffusion, heat transfer, volume shrinkage, and changes in mechanical behaviors of the curing resin. Second, in g-DLP, light gradients create strong in plane gradients of chemical species concentrations in the curing liquid resin due to the strong dependence of light intensity on the rate of monomer crosslinking. Furthermore, light gradients through the depth create concentration gradients due to the degree of cure dependent light absorption and the use of photoabsorbers. These complex physical features of the printing process must be understood in order to properly control printing parameters such as light exposure time, printing speed, and grayscale variations to achieve accurate mechanical properties. In this paper, a photopolymerization reaction–diffusion model is developed and used in conjunction with experiments to investigate the coupled effects of light propagation, chemical reaction rates, and species diffusion during g-DLP 3D printing. The model is implemented numerically utilizing the finite difference method and simulation results are compared to experimental findings of simple printed structures. The agreement between experimental and model predictions of simple quantities of interest, such as geometric feature sizes, shows that the model can capture the overcure due to free-radical and other species diffusion during printing when grayscale patterns are employed. This model lays the groundwork for future extensions that can incorporate more complex coupled physics such as heat transfer, volume shrinkage, and material property evolution, which are critically important in utilizing g-DLP 3D printing for the fabrication of high-performance parts which excellent geometric and material property tolerances.

36 MATERIALS SCIENCE↗

A Hybrid Numerical Method for Turbulent Mixing Layers

A hybrid method has been developed for simulations of compressible turbulent mixing layers. Such mixing layers dominate the flows in exhaust systems of modern day aircraft and also those of hypersonic vehicles currently under development. The method configurations in which a dominant structural feature provides an unsteady mechanism to drive the turbulent development in the mixing layer. The hybrid method uses a Reynolds-averaged Navier-Stokes (RANS) procedure to calculate wall bounded regions entering a mixing section, and a Large Eddy Simulation (LES) procedure to calculate the mixing dominated regions. A numerical technique was developed to enable the use of the hybrid RANS-LES method on stretched, non-Cartesian grids. Closure for the RANS equations was obtained using the Cebeci-Smith algebraic turbulence model in conjunction with the wall-function approach of Ota and Goldberg. The wall-function approach enabled a continuous computational grid from the RANS regions to the LES region. The LES equations were closed using the Smagorinsky subgrid scale model. The hybrid RANS-LES method is applied to a benchmark compressible mixing layer experiment. Preliminary two dimensional calculations are used to investigate the effects of axial grid density and boundary conditions. Vortex shedding from the base region of a splitter plate separating the upstream flows was observed to eventually transition to turbulence. The location of the transition, however, was much further downstream than indicated by experiments. Actual LES calculations, performed in three spatial directions, also indicated vortex shedding, but the transition to turbulence was found to occur much closer to the beginning of the mixing section. which is in agreement with experimental observations. These calculations demonstrated that LES simulations must be performed in three dimensions. Comparisons of time-averaged axial velocities and turbulence intensities indicated reasonable agreement with experimental data.

Georgiadis, Nicholas J.↗

Progressive Damage Failure Analysis of Post-Buckled Composite Single-Stringer Panel with Teflon Inserts

The high strength-to-weight and stiffness-to-weight ratio materials, such as laminated composites, are advantageous for modern aircraft. Laminated composites with initial flaws are susceptible to delamination under buckling loads. PDA tools help enhance the industry’s understanding of the mechanisms for damage initiation and growth in composite structures while assisting in the design, analysis, and sustainment methods of these composite structures. The global-local modeling approach for the single-stringer post-buckled panel was evaluated through this effort, using Teflon inserts to simulate the defect of damage during manufacturing. This understanding is essential for designing the post-buckled structure, reducing weight while predicting damage initiation location, and addressing a potential design review for future aircraft repairs. In this work, the initial damage was captured with Teflon inserts as the starting configuration; and any reference to the damage initiation refers to any damage beyond the “initial unbonded region.” The effort aims to develop, evaluate, and enhance methods to predict damage initiation and progression and the failure of post-buckled hat-stiffened panels using multiple Abaqus FEA Virtual Crack Closure Technique (VCCT) definitions. Validation of the PDA using the VCCT material model was performed on a large single-stringer panel subjected to compressive loading. The compressive loading of the panel caused the skin to buckle before any damage began to occur locally. In addition, comparisons are made for critical aspects of the damage morphology, such as a growth pattern that included delamination from the skin-stiffener interface to the skin and ply interfaces. When compared against the experimental data produced through the NASA Advanced Composites Project (ACP), the present model captured damage migration from one surface to another, and model validations were within 4% of the experimental data.

PDA↗