High-fidelity Finite Element Analysis Research Articles

Analytical prediction and numerical verification of stress concentration profile around an in-situ tow break in resin-impregnated filament-wound composites

A new empirical analytical approach is developed for predicting the stress concentration profile around an in-situ tow break in filament-wound composites. A shear-lag analysis is firstly performed to solve for the perturbational axial displacement of the broken tow and resultant debonding lengths. Solution of stress field caused by tangential load on the surface of an elastic half space is then utilized in combination with superposition concepts to obtain the overload magnitudes in the neighboring tows. Subsequently, high-fidelity finite element analysis on a representative uni-directional laminate model under different stress states is performed, and excellent overall agreement is observed between analytical and numerical predictions. The proposed method takes into account essential aspects such as transversely isotropic material properties, in-situ stress states and their effect on the interfacial frictional forces in the debonded interfaces, and thus provides a convenient way to evaluate the stress concentration factors in damaged filament-wound composites. In addition, this approach can be applied to yield auxiliary failure evaluation criteria for statistical strength prediction or finite element modeling of filament-wound composites or similar structures.

Assembly of Large Flip Chip Die in Ceramic Packages

Hermetic packaging is required to ensure the reliability of processors and application specific integrated circuit (ASIC) chips that are used in strategic systems, whose operating lifetimes typically exceed thirty years. Performance requirements for emerging system applications are dictating the need for one thousand or more I/O, which precludes the use of wire bonded devices. These packaging requirements can be met by flip chip bonding die onto multilayer, high temperature cofired ceramic substrates. The hermetic environment is provided by a Kovar metal ring that is brazed onto the ceramic and sealed in the final assembly step, by welding on a metal cover. Several process and design requirements must be met to achieve high reliability flip chip assemblies. Reflowed devices must be free of flux residues because they are corrosive, provide a path for ion diffusion, are a source of moisture and degrade the adhesion of underfill to chip and substrate. Removal of flux residues by cleaning is problematic because the gap between chip and substrate is very small relative to chip area. A better approach is to bond chips without flux, as we do, by reflowing them in a formic acid environment. An important reliability requirement for strategic hardware is the ability to sustain many cycles over extended temperature ranges. Temperature cycles induce plastic deformation in the solder connections as a result of stresses generated by the mismatch in thermal expansion coefficients of the chip and package. These stresses can be reduced significantly by underfilling the bonded chip with a filled adhesive. Choice of material as well as process execution impact the effectiveness of underfill enhancement of reliability. Optimization of both the physical design and composition of the solder connections is essential to producing reliable flip chip assemblies. We make extensive use of finite element analyses (FEA) in which we track the accumulation of plastic strain energy in the solder connections as they are subjected to temperature cycle induced stresses. Good correlations between the energy accumulated per cycle and the number of cycles to failure can be achieved if accurate state models of the solder are used. We have a catalog of Anand models for the solder compositions that we use in our FEA simulations. One of the more challenging aspects of designing high performance flip chip packages is matching the size and pitch of the bond pads on the ceramic with those on the chip. Current devices use 90 micron diameter pads on 200 micron pitch, but newer designs are migrating towards 80 micron diameter pads on 180 micron pitch. It challenging to maintain tight tolerances on pad geometries in these higher density packages and to route all the additional signals. Thermal management is also an important component of the package design. Thermal vias and thermally conductive underfills are used to remove heat from the front side of the chip. On the backside of the chip, the gap between chip and cover is minimized and filled with a high thermal conductivity interface material. High fidelity FEA modeling is necessary to ensure that the chip can be adequately cooled in its hermetic environment. This paper discusses the impact of these design and assembly factors on the reliability of high performance, hermetically packaged flip chip assemblies.

Innovative Insights on the Thin Square Plate Large Deflection Problem.

Thin plates subjected to transverse load and undergoing large deflections have been widely studied and published in the literature. However, there is still a lack of information and understanding about the membrane stresses created under large deflections and their associated Airy stress function, as displayed in the well-known von Kármán equations set. The present study aims at providing explicit expressions for the membrane stresses, the deflections, and the Airy stress function for a general square plate area vertically uniformly loaded to reach large deflection state. This was obtained by using the results of a high-fidelity finite element analysis applied on a lateral loaded simply supported thin square plate, which are then casted to yield approximate Fourier series expressions for the membrane stresses, deflections, and the Airy stress function. The stress map figures provide a good understanding of the critical points on the plate, while the explicit mathematical expressions enabled the calculation of deflections and stresses for the entire plate area. Among other interesting findings, the presence of relatively high tensile and compressive membrane stresses existing near the plate edges was revealed, which might lead to potential failure hazards. The derivatives of the deflections and the Airy stress function enabled the validation of the large deflections von Kármán equations set for the investigated case, and it turned out that the generated expressions for the stresses and the lateral deflection based on a high-fidelity finite element model satisfy the second equation with a good accuracy, while the first one remains to further be investigated. Moreover, using the generated explicit equations, the load influence on the deflections and stresses was also analyzed to yield general novel expressions for the medium and very large deflections states. To generalize the investigated case, the stresses and the deflections were non-dimensionalized so they can be used for any material and plate dimensions.

Multiphysics-informed DeepONet of a lithium-ion battery to predict thermal runaway

This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) to predict the thermal runaway of lithium-ion batteries (LIBs) under a variety of thermal operational and abuse conditions. Specifically, this study aims to address the functional mapping from a heating curve to predict the evolution of the temperature of a LIB and dimensionless concentration of dominant components of the LIB including an anode, cathode, electrolyte, and solid electrolyte interphase. The proposed method has two key characteristics. First, the MPI-DeepONet is supervised by using ordinary and partial differential equations, which govern highly complex and nonlinear phenomena of thermal runaway of a LIB, including the chemical reaction degradation of the dominant four components and thermodynamics. This feature enables to train of the proposed neural network with a small amount of data available, suggesting that the proposed neural network is accurate and robust even though the proposed method is trained even with limited data. Second, the proposed neural network is trained with the data that is generated from high-fidelity finite element analysis under a variety of thermal operational and abuse conditions because measurements for the thermal runaway of a LIB are limitedly available. Hence, the MPIDeepONet does not require actual measurements, which is extremely difficult in field experiments. Finally, the accuracy and robustness of the proposed architecture are verified through actual measurements and other scenarios, which are different from the data trained. The analysis of results reveals that the MPI-DeepONet secures higher accuracy and robustness than purely data-driven DeepONet. The proposed surrogate model, which is faster than existing surrogate models, suggesting that this model contributes to developing a digital twin model of a LIB, which can be deployed on a battery thermal management system and provides sufficient information for effective power and energy management.

Multi-fidelity machine learning based uncertainty quantification of progressive damage in composite laminates through optimal data fusion

Recently machine learning (ML) based approaches have gained significant attention in dealing with computationally intensive analyses such as uncertainty quantification of composite laminates. However, high-fidelity ML model construction is computationally demanding for such high-dimensional problems due to the required large amount of high-fidelity training data. We propose to address this issue effectively through multi-fidelity ML based surrogates which can use a training dataset consisting of optimally distributed high- and low-fidelity simulations. For forming multi-fidelity surrogates of progressive damage in composite laminates, we combine low-fidelity finite element analysis data obtained using Matzenmiller damage model with Hasin failure criteria and high-fidelity finite element analysis data obtained using three-dimensional continuum damage mechanics based model with P Linde’s failure criteria. It is shown that there is a significant computational advantage to using the multi-fidelity surrogate approach as compared to conventional single-fidelity surrogates. Such computational advantage through optimal data fusion without compromising accuracy becomes crucial for the subsequent data-driven uncertainty quantification and sensitivity analysis of composites involving thousands of realizations. Ply orientations come out to be the most sensitive parameters to matrix damage, fibre damage and reaction force in composite laminates. The degree of uncertainty in the output quantities depend on the input-level stochastic variations. For example, a combined stochastic variation of ±10% in material properties and ±10° in ply orientations lead to 1.85%, 16.98% and 11.24% coefficient of variation in the matrix damage, fibre damage and reaction force respectively. In general, the numerical results obtained based on the efficient data-driven approach strongly suggest that source-uncertainty of composites significantly influences the progressive damage evolution and global mechanical behaviour, leading to the realization of the importance of adopting an inclusive analysis framework considering such inevitable random variabilities.

Influence of steel reinforcement on the performance of an RC structure subjected to a high-velocity large-caliber projectile

The reinforcement setting in reinforced concrete (RC) protective structure should be optimized to resist the impact of high-velocity large-caliber projectiles (LCPs), which is yet to be clearly understood. This study adopts a high-fidelity finite element analysis (FEA) approach to examine the influence of steel reinforcement setting, including the diameter/spacing/strength of reinforcement, the number/spacing/distribution depth of the mesh layer, and the reinforcement ratio, on the performance of RC structure against the large-caliber projectile impact. The FEA approach utilizes an improved dynamic constitutive model of concrete recently proposed by the authors to capture the responses of concrete, i.e., the penetration resistance, cratering, scabbing, and cracking failure, and the *CONSTRAINED_BEAM_IN_SOLID coupling method to consider the bond-slip relation between steel reinforcement and concrete. By simulating the 64 mm-diameter projectile impact tests on the plain concrete (PC) and RC targets, it validates the established FEA approach in contrast to that using the existing concrete models in LS-DYNA, i.e., the Holmquist-Johnson-Cook, Riedel-Thoma-Hiermaier, and Karagozian & Case concrete models, and the full coupling method, i.e., the *CONSTRAINED_LAGRANGE_IN_SOLID. The performance of the RC target struck by a 154 mm-diameter LCP is further numerically studied using the validated FEA approach. Based on more than fifty simulation cases, it derives the optimal reinforcement setting for the engineering design: (i) increasing the mesh layer number should be given priority to increasing the reinforcement ratio; (ii) the optimal rebar diameter and spacing are 1/15 and 1/2 of projectile diameter, respectively; (iii) uniform distribution of reinforcement layer along target thickness is suggested; (iv) normal strength reinforcement is preferred; (v) the practical highest reinforcement ratio is 4% to reduce structural thickness.

Cable force optimization of cable-stayed bridges: A surrogate model-assisted differential evolution method combined with B-Spline interpolation curves

Cable-stayed bridges have been widely constructed to cross large obstacles in transportation networks and metropolitan cities. As a highly redundant structure, the structural performance of a cable-stayed bridge can be significantly influenced by the cable prestressing forces. Therefore, the determination of optimal cable forces is necessary at the design stage. As computational mechanics matures, numerical computational methods such as high-fidelity finite element analyses facilitate accurate assessment of the bridge state, and evolutionary algorithms have been applied to enhance the global search ability. However, the combination of evolutionary algorithms and finite element model-based evaluations can substantially increase the computational burden, rendering the efficiency of optimization significantly time-consuming for practical engineering projects. To overcome this limitation, this work proposes a new surrogate model-assisted differential evolution method to facilitate the determination of optimal cable forces. Different from the existing methods, the proposed method develops a new individual selection strategy together with an additional archive to manage the diversity of population and further enhance the exploration ability. A new criterion is also proposed for the refinement of the surrogate model in the local search stage to avoid redundant information. Furthermore, B-Spline interpolation curve is integrated within the framework as a high-dimensional model representation technique for the design variables of cable forces. Five classic numerical examples and a practical engineering application validate the performance and advantages of the proposed method.

Generating Clear Vibrotactile Cues with a Magnet Embedded in a Soft Finger Sheath.

Haptic displays act on the user's body to stimulate the sense of touch and enrich applications from gaming and computer-aided design to rehabilitation and remote surgery. However, when crafted from typical rigid robotic components, they tend to be heavy, bulky, and expensive, while sleeker designs often struggle to create clear haptic cues. This article introduces a lightweight wearable silicone finger sheath that can deliver salient and rich vibrotactile cues using electromagnetic actuation. We fabricate the sheath on a ferromagnetic mandrel with a process based on dip molding, a robust fabrication method that is rarely used in soft robotics but is suitable for commercial production. A miniature rare-earth magnet embedded within the silicone layers at the center of the finger pad is driven to vibrate by the application of alternating current to a nearby air-coil. Experiments are conducted to determine the amplitude of the magnetic force and the frequency response function for the displacement amplitude of the magnet perpendicular to the skin. In addition, high-fidelity finite element analyses of the finger wearing the device are performed to investigate the trends observed in the measurements. The experimental and simulated results show consistent dynamic behavior from 10 to 1000 Hz, with the displacement decreasing after about 300 Hz. These results match the detection threshold profile obtained in a psychophysical study performed by 17 users, where more current was needed only at the highest frequency. A cue identification experiment and a demonstration in virtual reality validate the feasibility of this approach to fingertip haptics.

Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis.

This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios.

Experimental and numerical investigation on large deformation reconstruction of thin laminated composite structures using inverse finite element method

The inverse finite element method (iFEM) is one of the best candidates to perform displacement monitoring (shape sensing) of structures using a set of on-board/embedded strain sensors. This study demonstrates the high efficiency, robustness, and accuracy of the iFEM approach to reconstruct geometrically non-linear deformations of thin laminated plates and shells by performing experimental measurements and numerical analyses. The iFEM formulation is derived based on the first-order shear deformation theory of plates. A weighted least-squares variational principle is utilized with incremental non-linear strains while performing the geometrical update of the model using predicted incremental deformations. Moreover, a quadrilateral inverse-shell element (iQS4) is employed to discretize the whole domain of the laminated panels and solve the numerical/experimental shape-sensing problems. Further, a polynomial strain pre-extrapolation technique is incorporated with the iQS4 formulation to smoothen the discrete strain data obtained from a few strain rosettes placed along the entire length of the structures. For each case study, a high-fidelity finite element analysis is performed to establish a reference displacement solution. Finally, the qualitative and quantitative comparison of reconstructed displacement results with reference solutions confirms the superior potential of the iFEM-iQS4 approach for full-field shape sensing of thin laminates undergoing non-linear deformations.

Novel Correlations Between Process Forces and Void Morphology for Effective Detection and Minimization of Voids During Friction Stir Welding

Abstract Sub-surface voids and material heterogeneities resulting from the friction stir welding (FSW) process often necessitate post-weld inspection to ensure the quality of weld obtained from this solid-state welding process. In this context, in-process void detection techniques can potentially help in optimizing the process conditions and thereby reduce expensive and time-consuming post-process inspection of welds. Current in-process void detection techniques rely on approaches that try to directly correlate the part-scale welding quality to void formation, without a fundamental understanding of the underlying mechanics and materials physics that modulate void evolution. In this work, we demonstrate an effective in-process numerical technique that uses process force signals to detect volumetric void formation and connect the variations in the force signals to interactions between the tool probe and the underlying material voids. Our approach relies on a high-fidelity finite element analysis simulation of the FSW process and on correlation of numerically obtained process force signals with the corresponding void structures. This correlation is obtained in the phase-space relating in-plane reaction forces on the tool to the tool rotation angle. We focus on the interactions of the tool geometry and tool motion with the surrounding material undergoing plastic deformation and deduce novel insights into various correlations of tool motion and void formation. Through this approach, we can identify tool-related process conditions that can be optimized to minimize void formation and demonstrate a potential in situ force-based void monitoring method that links to the underlying plastic flow and void structures during the FSW process.

Systematic characterization of wing mechanosensors that monitor airflow and wing deformations

SummaryAnimal wings deform during flight in ways that can enhance lift, facilitate flight control, and mitigate damage. Monitoring the structural and aerodynamic state of the wing is challenging because deformations are passive, and the flow fields are unsteady; it requires distributed mechanosensors that respond to local airflow and strain on the wing. Without a complete map of the sensor arrays, it is impossible to model control strategies underpinned by them. Here, we present the first systematic characterization of mechanosensors on the dragonfly’s wings: morphology, distribution, and wiring. By combining a cross-species survey of sensor distribution with quantitative neuroanatomy and a high-fidelity finite element analysis, we show that the mechanosensors are well placed to perceive features of the wing dynamics relevant to flight. This work describes the wing sensory apparatus in its entirety and advances our understanding of the sensorimotor loop that facilitates exquisite flight control in animals with highly deformable wings.

Fragility analysis of a prestressed concrete containment vessel subjected to internal pressure via the probability density evolution method

The prestressed concrete containment vessel (PCCV) is the final barrier to prevent radioactive material leakage from a nuclear power plant to the outside. Therefore, the fragility evaluation is very important for the design and analysis of the PCCV. In this paper, the fragility analysis of the PCCV (tested at Sandia National Laboratories (SNL)) subjected to internal pressure is conducted using the probability density evolution method (PDEM). First, a high-fidelity finite element analysis procedure is introduced to perform the deterministic analysis of the PCCV. In the following stochastic analysis and fragility evaluation, the material properties are selected as the uncertain resources. By applying the PDEM, the probability surface and probability density function (PDF) of the stochastic responses are obtained. Furthermore, the limit state of the PCCV is evaluated by the global average strain, which is defined as the ratio of radial displacement to the radius of the PCCV, and the PCCV reaches its functional and structural failure states when the global average strain reaches 0.4% and 1.5%, respectively. To consider more than one failure mode in the fragility evaluation, an equivalent extreme-value event combined with the PDEM is adopted to obtain the failure probability and the fragility curve of the PCCV to evaluate its fragility behavior. The results show that the fragility of the PCCV is still zero until the internal pressure increases to 2.890 times and 3.155 times the design pressure and then increases rapidly and reaches 1 at 3.560 times and 3.955 times the design pressure of the functional and structural failure criteria, respectively.

A unified theory of adhesive-bonded tubular joints with dissimilar adherends subjected to a multiplicity of axisymmetric loads

In the field of stress analysis of adhesive-bonded joints, many mathematical models have been developed to analyze joints composed of a single-layered isotropic material or a homogenized layered composite material as adherends that are subjected to only one type of loading. Despite being useful in the design process, these models are unquestionably too limited for real-world applications because joints are inevitably exposed to various types of loads in changing environments. Therefore, this study aims to formulate a new unified theory of adhesive-bonded cylindrical coupler joints under a wide variety of axisymmetric loading conditions. The loading scenarios include torsion, tension, internal and external pressure, and changes in temperature and moisture. The joints can consist of isotropic, orthotropic, or laminated composite adherends bonded together by a thin adhesive layer. The governing equations of the joint are simplified to be second order ordinary differential equations, which can be analytically solved in closed forms for isotropic adherends or numerically solved for anisotropic adherends. The effects of the fiber orientation on symmetric-balanced laminated composite adherends for each load are comprehensively reported. The present models are rigorously validated through comparison with the finite element method performed in ABAQUS™. Comparable to high-fidelity finite element analysis, the present theory can accurately predict three-dimensional stress fields in tubular joints made of any practical elastic materials and geometric dimensions.

Modelling the quasi-static flexural behaviour of composite sandwich structures with uniform- and graded-density foam cores

In-service, composite sandwich structures, which consist of fibre-composite skins (also termed face-sheets) adhesively bonded to a polymeric foam core, can encounter extreme quasi-static flexural loading that may cause serious damage to the sandwich structure. The ability to model the flexural behaviour of such structures can lead to improved designs and more efficient maintenance procedures. In the present research, a three-dimensional finite-element analysis (FEA) model is developed to predict the flexural behaviour of such sandwich structures using a commercial software package (i.e. Abaqus/Explicit). The high-fidelity FEA simulation combines an elastic–plastic (E-P) damage model of the composite skins together with a crushable foam-core damage model. The E-P damage model is implemented with a user subroutine to capture the damage, such as plastic deformation of the matrix and matrix cracking, fibre fracture and delamination cracking of the composite skins. The crushable foam model is used to predict (a) the mechanical response of the crushed foam core, (b) the induced damage from ductile fracture due to growth, coalescence and fracture of the cells and (c) the induced damage from shear fracture of the foam due to plastic shear-band localisation. Results from the modelling studies, such as the loading response and the damage mechanisms, are discussed and compared with the experimental results obtained from the sandwich structures manufactured with both uniform- and graded-density foam cores but which all have the same average core density. Good agreement is achieved between the experimental results and the predictions from the numerical modelling simulations.

A Study on Using Image-Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations

AbstractIn design for forming, it is becoming increasingly significant to develop surrogate models of high-fidelity finite element analysis (FEA) simulations of forming processes to achieve effective component feasibility assessment as well as process and component optimizations. However, surrogate models using traditional scalar-based machine learning methods (SBMLMs) fall short on accuracy and generalizability. This is because SBMLMs fail to harness the location information available from the simulations. To overcome this shortcoming, the theoretical feasibility and practical advantages of innovatively applying image-based machine learning methods (IBMLMs) in developing surrogate models of sheet stamp forming simulations are explored in this study. To demonstrate the advantages of IBMLMs, the effect of the location information on both design variables and simulated physical fields is first proposed and analyzed. Based on a sheet steel stamping case study, a Res-SE-U-Net IBMLM surrogate model of stamping simulations is then developed and compared with a baseline multilayer perceptron (MLP) SBMLM surrogate model. The results show that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness. This article presents a promising methodology in leveraging IBMLMs as surrogate models to make maximum use of information from stamp forming FEA results. Future prospective studies that are inspired by this article are also discussed.

Effect of inter-module connections on progressive collapse behaviour of MiC structures

Modular integrated Construction (MiC) is a game changing construction approach which could significantly increase construction efficiency, quality, and sustainability. For steel MiC structures, modules are manufactured in the factories and assembled at the construction sites through inter-module connections. The concern of the vulnerability of these connections under abnormal hazards leading to disproportionate progressive collapse will impede industrial applications of MiC structures in the construction community. This study investigates the structural robustness of corner-supported modular steel buildings with the focus on different inter-module connections. A sub-structure extracted from a five-storey modular building is analysed by high fidelity finite element analysis under a corner column removal scenario. The load redistribution mechanism and failure modes are investigated thoroughly, based on which the simplified macro model is proposed where connections are modelled with rotational springs. Good consistence is found for the simplified model and the detailed FE analysis in terms of the pushdown curve. It is also found that the inter-module connection types will affect the beam-column joint properties which dominate the progressive collapse resistance of MiC structures. Therefore, attention should be paid when selecting different inter-module connections. The current rigid beam-column joint connection may lead to nonconservative evaluation.

NP-ODE: Neural process aided ordinary differential equations for uncertainty quantification of finite element analysis

Finite Element Analysis (FEA) has been widely used to generate simulations of complex nonlinear systems. Despite its strength and accuracy, FEA usually has two limitations: (i) running high-fidelity FEA often requires high computational cost and consumes a large amount of time; (ii) FEA is a deterministic method that is insufficient for uncertainty quantification when modeling complex systems with various types of uncertainties. In this article, a physics-informed data-driven surrogate model, named Neural Process Aided Ordinary Differential Equation (NP-ODE), is proposed to model the FEA simulations and capture both input and output uncertainties. To validate the advantages of the proposed NP-ODE, we conduct experiments on both the simulation data generated from a given ordinary differential equation and the data collected from a real FEA platform for tribocorrosion. The results show that the proposed NP-ODE outperforms benchmark methods. The NP-ODE method realizes the smallest predictive error as well as generating the most reasonable confidence intervals with the best coverage on testing data points. Appendices, code, and data are available in the supplementary files.

Transient Dynamic System Behavior of Pressure Actuated Cellular Structures in a Morphing Wing

High aspect ratio aircraft have a significantly reduced induced drag, but have only limited installation space for control surfaces near the wingtip. This paper describes a multidisciplinary design methodology for a morphing aileron that is based on pressure-actuated cellular structures (PACS). The focus of this work is on the transient dynamic system behavior of the multi-functional aileron. Decisive design aspects are the actuation speed, the resistance against external loads, and constraints preparing for a future wind tunnel test. The structural stiffness under varying aerodynamic loads is examined while using a reduced-order truss model and a high-fidelity finite element analysis. The simulations of the internal flow investigate the transient pressurization process that limits the dynamic actuator response. The authors present a reduced-order model based on the Pseudo Bond Graph methodology enabling time-efficient flow simulation and compare the results to computational fluid dynamic simulations. The findings of this work demonstrate high structural resistance against external forces and the feasibility of high actuation speeds over the entire operating envelope. Future research will incorporate the fluid–structure interaction and the assessment of load alleviation capability.

High-Fidelity Finite Element Modeling and Analysis of Adaptive Gas Turbine Stator-Rotor Flow Interaction at Off-Design Conditions

ABSTRACTThe objective of this work is to computationally investigate the impact of an incidence-tolerant rotor blade concept on gas turbine engine performance under off-design conditions. When a gas turbine operates at an off-design condition such as hover flight or takeoff, large-scale flow separation can occur around turbine blades, which causes performance degradation, excessive noise, and critical loss of operability. To alleviate this shortcoming, a novel concept which articulates the rotating turbine blades simultaneous with the stator vanes is explored. We use a finite-element-based moving-domain computational fluid dynamics (CFD) framework to model a single high-pressure turbine stage. The rotor speeds investigated range from 100% down to 50% of the designed condition of 44,700 rpm. This study explores the limits of rotor blade articulation angles and determines the maximal performance benefits in terms of turbine output power and adiabatic efficiency. The results show articulating rotor blades can achieve an efficiency gain of 10% at off-design conditions thereby providing critical leap-ahead design capabilities for the U.S. Army Future Vertical Lift (FVL) program.