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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Turbulence Modeling for Unsteady Transonic Flows

Conditionally sampled, ensemble-averaged velocity measurements, made with a laser velocimeter, were taken in the flowfield over the rear half of an 18% thick circular arc airfoil at zero incidence tested at M = 0.76 and of a Reynolds number based on chord of 11 x 10(exp 6). Data for one cycle of periodic unsteady flow having a reduced frequency bar-f of 0.49 are analyzed. A series of compression waves, which develop in the early stages of the cycle, strengthen and coalesce into a strong shock wave that moves toward the airfoil leading edge. A thick shear layer forms downstream of the shock wave. The kinetic energy and shear stresses increase dramatically, reach a maximum when dissipation and diffusion of the turbulence exceed production, and then decrease substantially. The response time of the turbulence to the changes brought about by the shock-wave passage upstream depends on the shock-wave strength and position in the boundary layer. The cycle completes itself when the shock wave passes the midchord, weakens, and the shear layer collapses. Remarkably good comparisons are found with computations that employ the time-dependent Reynolds averaged form of the Navier-Stokes equations using an algebraic eddy viscosity model, developed for steady flows.

Marvin, J. G.↗
NASA NTRSDate not supplied

P52D-1583 - Further Improvements to Constraints on Titan’s Atmospheric n-Butane, via Ground-Based Observations

We have acquired high spectral resolution ground-based observations of Titan in the mid-infrared from NASA Infrared Telescope Facility’s Texas Echelon Cross Echelle Spectrograph instrument , designed to constrain the abundances of new molecules within Titan’s atmosphere. The target molecule for this work, n-butane (n-C H ), though predicted by photochemical models of Titan’s atmosphere , has never been detected. Using the NEMESIS radiative transfer code , we retrieved a disk-averaged temperature profile for Titan by modeling CH emission in the 1248 cm region. Using this temperature profile, we then retrieved disk-averaged profiles of ethene (C H ), propane (C H ), and propene (C H ) in the 956 and 965 cm regions, which are also home to the strong and vibrational features of n-butane. We used an n-butane pseudoline list from JPL in order to systematically introduce a range of n-butane mixing ratios into the model, to constrain the molecule’s abundance based on the model’s changing fit to the data. We derive 3-confidence upper limits at an altitude of 150 km of 48 and 31 ppb for n-butane, via the and bands in the 956 and 965 cm regions, respectively. These are significantly tighter upper limits when compared to the ~300 ppb upper limit found in our recent work which used the same methodology but on much lower resolution limb data collected by the Cassini spacecraft. Upper limits on gas abundances are important for continued improvement of photochemical models of Titan’s atmosphere, which is understood to be an important analog for the prebiotic Earth atmosphere . They are also important for understanding the composition of Titan’s lakes and seas, which are likely to contain these larger hydrocarbons . Finally, as larger molecules like C ’s are thought to be able to serve as formation sites for building haze particles, constraining their abundances is important for understanding the process of haze formation at Titan and at Earth Ref: [1] J. H. Lacy, et al., PASP, 2002. [2] Y.L. Yung, et al., ApJS, 1984. [3] M. Dobrijevic, et al., Icarus, 2021. [4] P. G. J. Irwin et al., JQSRT, 2008. [5] K. Sung, et al., JQSRT, 2020. [6] B. L. Steffens, et al., PSJ, 2022. [7] Clarke & Ferris, Origins Life Evol. Biosphere, 1997. [8] D. Cordier, et al., ApJ, 2008. [9] D. B. Curtis, et al., JPCA, 2005.

Brendan Steffens↗
DOE OSTI2020

Numerical study of the effect of propped surface area and fracture conductivity on shale gas production: Application for multi-size proppant pumping schedule design

Hydraulic fracturing is a technique extensively used in the oil and gas industry, where water, proppant (sand) and additives are injected into unconventional reservoirs to enhance the recovery of shale hydrocarbon. Although some previous studies have developed pumping schedules that maximize gas production for a single-size proppant, there are very few studies that consider the effect of varying proppant diameters across pumping stages on shale gas production. Motivated by this, we carried out an extensive sensitivity analysis to determine the effect of different proppant diameters on the average fracture conductivity (FC), average propped surface area (PSA) and cumulative shale gas production volume. We found out that the cumulative shale gas production volume depends on both the average PSA and average FC. We also found out that small-diameter proppant resulted in higher average PSA and lower average FC, whereas large-diameter proppant resulted in lower average PSA and higher average FC. Hence, we designed a multi-size proppant pumping schedule considering both of these parameters into account for simultaneously propagating multiple fractures to maximize shale gas production from unconventional reservoirs. Since the size of injected proppant particles determines the average PSA and average FC for the propped hydraulic fractures, we developed a novel framework called Sequentially Interlinked Modeling Structure (SIMS) to predict the average PSA, average FC and cumulative shale gas production volume at the end of 10 years for a given pumping schedule. Then, we used this SIMS framework to obtain a multi-size proppant pumping schedule that maximizes shale gas production. In closing, we demonstrated that the obtained pumping schedule gives a cumulative shale gas production volume greater than the values obtained from the existing pumping schedules.

03 NATURAL GAS↗
NASA NTRS2012

Development of Non-Optimum Factors for Launch Vehicle Propellant Tank Bulkhead Weight Estimation

Non-optimum factors are used during aerospace conceptual and preliminary design to account for the increased weights of as-built structures due to future manufacturing and design details. Use of higher-fidelity non-optimum factors in these early stages of vehicle design can result in more accurate predictions of a concept s actual weights and performance. To help achieve this objective, non-optimum factors are calculated for the aluminum-alloy gores that compose the ogive and ellipsoidal bulkheads of the Space Shuttle Super-Lightweight Tank propellant tanks. Minimum values for actual gore skin thicknesses and weld land dimensions are extracted from selected production drawings, and are used to predict reference gore weights. These actual skin thicknesses are also compared to skin thicknesses predicted using classical structural mechanics and tank proof-test pressures. Both coarse and refined weights models are developed for the gores. The coarse model is based on the proof pressure-sized skin thicknesses, and the refined model uses the actual gore skin thicknesses and design detail dimensions. To determine the gore non-optimum factors, these reference weights are then compared to flight hardware weights reported in a mass properties database. When manufacturing tolerance weight estimates are taken into account, the gore non-optimum factors computed using the coarse weights model range from 1.28 to 2.76, with an average non-optimum factor of 1.90. Application of the refined weights model yields non-optimum factors between 1.00 and 1.50, with an average non-optimum factor of 1.14. To demonstrate their use, these calculated non-optimum factors are used to predict heavier, more realistic gore weights for a proposed heavy-lift launch vehicle s propellant tank bulkheads. These results indicate that relatively simple models can be developed to better estimate the actual weights of large structures for future launch vehicles.

Wu, K. Chauncey↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Revisiting the validity of eddy viscosity models for predicting airflow over water waves

In this study, we revisit the validity of eddy viscosity models for predicting wave-induced airflow disturbances over ocean surface waves. We first derive a turbulence curvilinear model for the phase-averaged Navier–Stokes equations, extending the work of Cao, Deng & Shen (2020 J. Fluid Mech. 901, A27), by incorporating turbulence stress terms previously neglected in the linearised viscous curvilinear model. To verify our formulation, we perform a priori tests by numerically solving the model using mean wind and turbulence stress profiles from large-eddy simulations (LES) of airflow over waves across various wave ages. Results show that including turbulence stress terms improves wave-induced airflow predictions compared with the previous viscous curvilinear model. We further show that using a standard mixing-length eddy viscosity yields inaccurate predictions at certain wave ages, as it fails to capture wave-induced turbulence, which fundamentally differs from mean shear-driven turbulence. The LES data show that accurate representations of wave-induced stresses require a complex-valued eddy viscosity. The maximum magnitude of this eddy viscosity scales as ∼𝑢 𝜏 ⁢𝜁 𝑖𝑛𝑛𝑒𝑟 , where 𝑢 𝜏 is the friction velocity and 𝜁 𝑖𝑛𝑛𝑒𝑟 is the inner-layer thickness, the height at which the eddy-turnover time matches the wave advection time scale. This scaling aligns with the prediction by Belcher & Hunt (1993 J. Fluid Mech. 251, 109–148). Overall, the findings demonstrate that traditional eddy viscosity models are inadequate for capturing wave-induced turbulence. More sophisticated turbulence models are essential for the accurate prediction of airflow disturbances and form drag in wind–wave interaction models.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Gas Generation Models for Thermal Runaway in Lithium Ion Batteries

A numerical model is implemented to study cell venting, internal pressure, and gas-phase dynamics behavior of Li-ion cells undergoing thermal runaway. A k-ɛ Reynolds-Averaged Navier-Stokes (RANS) model is adopted to describe the turbulent flow out of the cells, while the fluid dynamics inside the cells is described by Darcy-Forchheimer’s equation. Thermal abuse reactions and gas generation kinetics are described by a single-step lumped reaction model. A series of computational fluid dynamics (CFD) simulations are conducted on a single cell at various states-of-charge (100%, 50%, 25%) to demonstrate detailed flow and thermal behavior as a function of quantity of gas generated during cell venting. Venting events are categorized into two stages: i) breaching of the cell container and ii) thermal runaway reactions. It is shown that the cell response is dominated by the second stage since most of the gases are generated during thermal runaway. Also, the propensity for propagation is highly affected by state-of-charge (SOC). Cells at higher SOCs produce more heat and gas during the venting event, owing to higher mass and concentrations of reacting gases, and consequently reach higher internal cell pressures which increase the risk of side-wall breaching.

Kim, Jinyong↗
DOE OSTI2022

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Astronomy & Astrophysics↗
NASA NTRS1987

Modeling of turbulent transport as a volume process

An alternative type of modeling was proposed for the turbulent transport terms in Reynolds-averaged equations. One particular implementation of the model was considered, based on the two-point velocity correlations. The model was found to reproduce the trends but not the magnitude of the nonisotropic behavior of the turbulent transport. Some interesting insights were developed concerning the shape of the contracted two-point correlation volume. This volume is strongly deformed by mean shear from the spherical shape found in unstrained flows. Of particular interest is the finding that the shape is sharply waisted, indicating preferential lines of communication, which should have a direct effect on turbulent transfer and on other processes.

Jennings, Mark J.↗
NASA NTRS1990

Gaussian quadrature exponential sum modeling of near infrared methane laboratory spectra obtained at temperatures from 106 to 297 K

Transmission measurements made on near-infrared laboratory methane spectra have previously been fit using a Malkmus band model. The laboratory spectra were obtained in three groups at temperatures averaging 112, 188, and 295 K; band model fitting was done separately for each temperature group. These band model parameters cannot be used directly in scattering atmosphere model computations, so an exponential sum model is being developed which includes pressure and temperature fitting parameters. The goal is to obtain model parameters by least square fits at 10/cm intervals from 3800 to 9100/cm. These results will be useful in the interpretation of current planetary spectra and also NIMS spectra of Jupiter anticipated from the Galileo mission.

Giver, Lawrence P.↗
NASA NTRS1993

Computational fluid mechanics

Two papers are included in this progress report. In the first, the compressible Navier-Stokes equations have been used to compute leading edge receptivity of boundary layers over parabolic cylinders. Natural receptivity at the leading edge was simulated and Tollmien-Schlichting waves were observed to develop in response to an acoustic disturbance, applied through the farfield boundary conditions. To facilitate comparison with previous work, all computations were carried out at a free stream Mach number of 0.3. The spatial and temporal behavior of the flowfields are calculated through the use of finite volume algorithms and Runge-Kutta integration. The results are dominated by strong decay of the Tollmien-Schlichting wave due to the presence of the mean flow favorable pressure gradient. The effects of numerical dissipation, forcing frequency, and nose radius are studied. The Strouhal number is shown to have the greatest effect on the unsteady results. In the second paper, a transition model for low-speed flows, previously developed by Young et al., which incorporates first-mode (Tollmien-Schlichting) disturbance information from linear stability theory has been extended to high-speed flow by incorporating the effects of second mode disturbances. The transition model is incorporated into a Reynolds-averaged Navier-Stokes solver with a one-equation turbulence model. Results using a variable turbulent Prandtl number approach demonstrate that the current model accurately reproduces available experimental data for first and second-mode dominated transitional flows. The performance of the present model shows significant improvement over previous transition modeling attempts.

Hassan, H. A.↗
NASA NTRSDate not supplied

Aeroservoelastic Control Law Development for the Integrated Adaptive Wing Technology Maturation Wind-Tunnel Test

In this paper, a linear quadratic Gaussian (LQG) regulator was applied to a computational aeroservoelastic (ASE) model of the Integrated Adaptive Wing Technology Maturation (IAWTM) wind-tunnel model to demonstrate its effectiveness for active flutter suppression. The aerodynamics within this computational ASE model were solved using unsteady Reynolds-averaged Navier-Stokes (RANS) equations; the structural dynamics of the model were solved using modal analysis. This RANS-based ASE model was used as the plant for the LQG regulator. The Kalman filter and controller used within the LQG regulator were derived from a reduced-order model (ROM) of the RANS-based ASE model. This ROM-based ASE model was generated using system identification techniques. It was shown that the Kalman filter adequately predicted important states of the plant, and that LQG regulator successfully stabilized an aeroelastically unstable plant without exceeding reasonable control surface limitations. All of this was demonstrated using simulation data, and the methods presented here appear feasible for use in the upcoming IAWTM wind-tunnel tests.

Josiah M. Waite↗
NASA NTRS1980

Cross-field transport of less than 1 MeV protons in energetic particle events

A systematic analysis of hourly averaged low-energy anisotropy data (0.3-0.5 MeV) has been carried out with the JHU/APL detectors onboard the IMP-7 and 8 spacecraft from 1972-1975. The energetic particle events were divided into two major groups: flare-associated or nonimpulsive. Resolving the anistropy vectors into components parallel and perpendicular to the measured magnetic field leads directly to the following model-independent conclusions: (1) the average perpendicular anisotropy component is entirely accounted for in terms of the ExB drift, and hence transverse diffusion is negligible at these energies; application of the diffusion model to the nonimpulsive data set implies a ratio of the diffusion mean free paths parallel and perpendicular to the magnetic field much less than 0.051; (2) the parallel anisotropy component averages nearly to zero in the nonimpulsive data set, indicating virtually no net streaming along the field at 1 AU, averaged over all events.

Zwickl, R. D.↗
DOE OSTI2020

A hybrid porous model for full reactor core scale CFD investigation of a prismatic HTGR

Three-dimensional (3-D) Computational Fluid Dynamics (CFD) analysis of a whole nuclear reactor core is a tremendous challenge due to the large geometric volume and complex structures. Here, this research presents a hybrid porous (HP) model to simplify a prismatic High Temperature Gas-cooled Reactor (HTGR) core, so 3-D CFD investigation can be performed on a full reactor core scale. In the HP model, the prototypic small coolant channels in the nuclear fuel blocks are lumped together to form multiple equivalent large coolant channels, and then the porous medium flow model is applied to each of them. Therefore, heat transfer in fuel blocks is computed by a hybrid combination of solid energy and porous flow energy equations. The similarity between the HP model and prototypic model is achieved by deriving the porous flow permeability, inertial resistance factor, and artificial thermophysical properties. Compared with the widely used whole porous (WP) flow model, the HP model preserves more realistic geometric structures, and therefore more accurate physical processes. The General Atomics' Modular High Temperature Gas-cooled Reactor (MHTGR) design was chosen as a prototype to demonstrate the methodology. Simulations were performed using the prototypic CFD model and HP model at steady-state forced circulation, steady-state natural circulation, and transient conditions that correspond to normal operation, extended period of pressurized cool down, and short-term transients after reactor shutdown, respectively. The comparison shows good agreement between the HP model and prototypic model in the maximum fuel temperature, average solid temperature, and helium flow rate, which demonstrates the potential applicability of the HP model for a full reactor core scale simulation in the future. As a benefit, the HP model reduces the mesh quantity by a factor of 50 from a prototypic model. Correspondingly, the computation time was reduced by a factor of at least 30.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Numerical modeling of hydrogen mixing in a direct-injection engine fueled with gaseous hydrogen

Hydrogen is considered as one of the most promising options to achieve effective decarbonization of the energy and transportation sectors. As such, it has recently been receiving increasing attention because of its promising potential as an energy carrier for advanced energy and propulsion systems. With a focus on internal combustion engines, direct injection (DI) of gaseous hydrogen during the compression stroke offers great potential for high engine efficiency and specific power while reducing the risk of backfiring and pre-ignition issues. Therefore, many experimental and numerical efforts have recently been dedicated to understanding the physical and chemical behaviors of hydrogen in engine during mixing and combustion. This study focuses on computational fluid dynamics (CFD) modeling of the hydrogen DI process in a hydrogen optical research engine. Under the conditions studied, gaseous hydrogen is injected into the combustion chamber via a centrally located single-hole injector at a pressure of 100 bar. Two configurations, namely low-and high-tumble, are investigated to understand the impact of different in-cylinder flow patterns on the fuel-air mixture preparation. Simulations are carried out using the commercial CFD software CONVERGE. Here, the in-cylinder turbulence is modeled with an unsteady Reynolds-averaged Navier-Stokes (URANS) formulation closed by the renormalization group (RNG) k-ε model. Several numerical methods and model constants, including but not limited to turbulent Schmidt number, are evaluated. The numerical results are systematically compared against experimental measurements of velocity and hydrogen concentration fields on the vertical center plane to assess the performance of the CFD model, unveil the physics of hydrogen mixing, and establish best practices for modeling hydrogen DI under relatively high injection pressure conditions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Modeling cell venting and gas-phase reactions in 18650 lithium ion batteries during thermal runaway

A numerical model is developed to study cell venting, internal pressure, and gas-phase dynamics behavior of 18650 Li-ion cells undergoing thermal runaway. A k-e Reynolds-Averaged Navier-Stokes (RANS) model is adopted to describe the turbulent flow out of the cells, while the fluid dynamics inside the cells is described by Darcy-Forchheimer's equation. Thermal abuse reactions and gas generation kinetics are described by a single-step lumped reaction model. Then, a series of computational fluid dynamics (CFD) simulations are conducted on a single 18650 cell at various states-of-charge (100%, 50%, 25%) to study detailed flow and thermal behavior as a function of quantity of gas generated during cell venting. Venting events are categorized into two stages: i) breaching of the cell container and ii) thermal runaway reactions. It is found that the cell response is dominated by the second stage since most of the gases are generated during thermal runaway. Also, the propensity for propagation is highly affected by state-of-charge (SOC). Cells at higher SOCs produce more heat and gas during the venting event, owing to higher mass and concentrations of reacting gases, and consequently reach higher internal cell pressures which increase the risk of side-wall breaching.

25 ENERGY STORAGE↗
DOE OSTI2023

Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

Recent progress in data-driven turbulence modeling has shown its potential to enhance or replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence models. Here, this work utilizes invariant neural network (NN) architectures to model Reynolds stresses and turbulent heat fluxes in forced convection flows (when the models can be decoupled). As the considered flow is statistically one dimensional, the invariant NN architecture for the Reynolds stress model reduces to the linear eddy viscosity model. To develop the data-driven models, direct numerical and RANS simulations in vertical planar channel geometry mimicking a part of the reactor downcomer are performed. Different conditions and fluids relevant to advanced reactors (sodium, lead, unitary-Prandtl-number fluid, and molten salt) constitute the training database. The models enabled accurate predictions of velocity and temperature, and compared to the baseline k–τ turbulence model with the simple gradient diffusion hypothesis, do not require tuning of the turbulent Prandtl number. The data-driven framework is implemented in the open-source graphics processing unit–accelerated spectral element solver nekRS and has shown the potential for future developments and consideration of more complex mixed convection flows.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Finite Element Modeling for Compression Strength Prediction of Honeycomb Cores with Geometric Imperfections Measured using X-ray CT Imaging

Aluminum honeycomb cores have been used extensively in composite sandwich panels due to having high bending rigidity while maintaining low density. During manufacturing of the aluminum honeycomb, the thin metal walls are susceptible to imperfections that deviate from an ideal honeycomb shape. Computational tools to quantify imperfections and investigate their effects on quasi-static compression and impact response can inform design criteria to construct safer and lighter launch vehicle structures. This paper presents the results of a study on finite element modeling of metallic honeycomb cores (HCC) with geometric imperfections measured using X-ray Computer Tomography (CT), to predict the compression response. Finite element (FE) models are constructed with measured imperfections with appropriate boundary conditions. It is shown that the average behavior of larger 10x11 cell models can be predicted by sampling single cell models from the larger domain. Amplification of imperfections for cells at the center of sampled specimens are mode switched from the larger out-of-plane imperfection to the mode shape of the adjacent cells resulting in strengthening of the center specimens.

aluminum honeycomb↗