Search

This work presents a decision-tree-based Fuzzy Inference System (FIS) for making optimal choices in the development of reduced-order finite element (FE) models, in our case, shell-solid multifidelity models. FE analysis is widely used to simulate the real-world response of complex engineering structures and requires a high level of expertise for making a priori modeling decisions. Many times, these decisions are quite subjective in nature and lead to significant analyst-to-analyst variability, which in turn leads to considerable differences in engineering solutions. An expert system that recommends optimal modeling choices would notably reduce such variability. Expert systems use a knowledge base, developed by a subject matter expert, which is not always easy for complex structures. This work assesses the potential of interpretable machine learning (decision trees) to create data-driven rules that could be used by a FIS to make modeling choices for a multifidelity T-joint model. Specifically, the FIS takes the structural geometry and desired accuracy as inputs and infers the optimal two-dimensional/three-dimensional topology distribution. Once developed, the FIS is able to provide real-time optimal choices along with interpretability that fosters analysts’ confidence. Potential improvements to the presented framework that can enable its application to complex and nonlinear problems are discussed.

This research develops a highly effective deep-learning-based surrogate model that can provide the optimum topologies of 2D and 3D structures. In general, structural topology optimization requires plenty of computations because of a large number of finite element analyses to obtain optimal structural layouts by reducing the weight and satisfying the constraints. Therefore, many researchers have developed a deep-learning-based model using the initial static analysis results to predict the optimum designs. However, these studies still considered relatively simple example problems, such as a cantilever plate and MBB beam, even though they required a large number of data to achieve an accurate surrogate model for the simple application. To overcome these limitations, we propose a new framework, which 1) efficiently uses limited data by normalizing it and 2) employs a surrogate model capable of handling more practical cases, such as a 2D panel and 3D stiffened panel subjected to a distributed load, and can be used for local–global structural optimization. In all cases, the developed surrogate models can predict the optimum layouts with structural performance levels equal to those of the structures considered to be ground truths. Also, when the optimal structures were obtained using the proposed method, the total calculation time was reduced by 98% as compared to conventional topology optimization once the convolutional neural network had been trained.

This paper presents the use of Fredholm–Volterra integro-differential equation approach for determining the critical buckling load and natural frequencies of periodically supported nonprismatic Eul...

This paper presents a novel non-intrusive sensitivity-based nonlinear finite element model updating method in which the local nonlinearity of structure is considered. The sensitivity analysis is conducted to determine the sensitivity of a dynamic response with respect to different nonlinear parameters using the real and imaginary perturbation method in one single calculation. The use of the imaginary perturbation is capable of second-order accuracy in imaginary perturbation sensitivity analysis of the nonlinear finite element model updating procedure. The nonlinear parameters in the finite element model are estimated by using the improved sensitivity-based optimization algorithm. A nonlinear multi-degree-of-freedom oscillator and a cantilever beam with multiple nonlinear supports are studied to verify the accuracy of the proposed method. The effects of measurement noise and initial parameters on the performance of the presented approach are further investigated. Then, the proposed approach is verified by an experimental test of a cantilever beam with a steel slice. The updated nonlinear finite element model is further evaluated by the structure subjected to new excitations. Results show that the proposed method can effectively update the nonlinear finite element model even in the presence of contaminated measurement data and different initial parameters in the finite element model.

This research proposes a new framework to develop an accurate machine-learning-based surrogate model to predict the optimum topological structures using an advanced encoder-decoder network, Unet, and Unet++. The trained surrogate model predicts the optimum structural layout as output by inputting the results from the initial static analysis without any iterative optimization calculations. Input and output data are generated using the commercial finite element analysis package, Abaqus/Standard, and an optimization package, Abaqus/Tosca. We applied the data augmentation technique to increase the amount of data without actual calculations. Primarily, this research focused on overcoming the weaknesses of previous studies that the trained network is only applicable to limited geometry variations and requires an organized grid rectangular mesh. Therefore, this study suggests a mapping process to convert the analysis data on any type of mesh element to a tensor form, which enables training and employing the network. Also, to increase the prediction accuracy, we trained the network with the labeled optimum material data using a binary segmented output, representing the structure and void regions in the domain. Finally, the trained networks are evaluated using the intersection over union (IoU) scores representing the classification accuracy. The best-performing network provides highly accurate results, and this model provided the IoU scores for average, maximum, and standard deviation as 90.0%, 99.8%, and 7.1%, respectively. Also, we apply it to solve local-global structural optimization problems, and the overall calculation time is reduced by 98%. College of Engineering (COE) at Virginia Tech Published version The authors acknowledge financial support from the College of Engineering (COE) at Virginia Tech within Fellowship for Graduate Student First-Author Papers for open-access publication. We also thank Advanced Research Computing at Virginia Tech for providing computational resources and technical support that have contributed to the results reported in this paper. URL: https://arc.vt.edu/

Flight and ground test applications for in-flow and near-wall flow temperature sensing demand robust and accurate sensing, making thermocouple sensors attractive. Even for these extremely well-deve...

The stress-intensity factors of box beams under torsion and crack propagation under torsion or/and bending moment are discussed here. This study is motivated by a previous work [1] that derived a c...

Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous sub-mesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here, we fabricated extracellular-matrix mimicking suspended fiber networks: crosshatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying inter-fiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease, while force-disease biphasic relationship exhibited f-actin stress-fiber network dependence. However, unique to suspended fibers, coiling occurring at tips of protrusions and not the length or breadth of protrusions displayed strongest correlation with metastatic potential. To confirm that our findings were more broadly applicable beyond the mouse model, we repeated our studies in human ovarian cancer cell lines and found that the biophysical trends were consistent with our mouse model results. Altogether, we report complementary high throughput and high content biophysical metrics capable of identifying ovarian cancer metastatic potential on time scale of hours. Published version

Optimization of complex structures using standard evolutionary algorithms, like genetic algorithm (GA), is known to be computationally expensive, because a very large number of finite element analy...

Honeycomb sandwich panels are used in aerospace structures for their high stiffness-to-mass ratio. Reducing structural mass is critical in aircraft and spacecraft development due to its direct impa...

Free vibrations of thick and skew quadrilateral laminates have been analyzed by using the Ritz method and a third-order shear and normal deformable plate theory (TSNDT) that does require a shear-co...

Three flutter-suppression designs for a flexible flying-wing research drone are discussed, along with the modeling and flight-test results. The drone, with wing span of 10 feet, aspect ratio of alm...

Because of their higher aerodynamic efficiency and lighter structural weight, thin composite wings with large aspect ratios are increasingly being used for civil transport aircraft wing design. How...

The coupled equations of motion of an unrestrained flexible body when derived using multibody dynamics or mean axes formulation consist of three mass coupling integrals, namely, the first-order and...

We delineate effects of 1) face-to-core stiffness ratio (FCSR) and mass density ratio (FCDR) in square laminates and sandwich plates and 2) the fiber and transverse direction elastic modulus ratio ...

The main objective of the presented work is to understand the aeroelastic characteristics of an inflatable kite. Experimental modal analyses of two test articles are conducted using system identification and signal processing. To understand the flutter behavior of inflatable wings, flutter test articles made from Nylon and Dyneema fabrics are tested first for their modal properties and then for flutter in a wind tunnel. Various experimental techniques are implemented for understanding the modal responses of these two test articles. Instruments such as a 3D-photogrammetry camera system and accelerometers are used to measure the dynamic and static responses of these test articles. Using MATLAB's System Identification Toolbox and Signal Processing Toolbox, the modal parameters are identified from measured responses, such as out-ofplane displacement and accelerations. The experimental and operational modal parameters are then used to estimate the modal responses. The Zimmerman- Weissenburger flutter margin parameter is used to predict the onset of the flutter modes from the identified modal parameters. The verified system identification technologies are leveraged to understand the aeroelastic dynamic instability of a tethered inflatable wing during a wind tunnel test.

Variable angle tow laminates (VAT) and stiffeners are known to redistribute the in-plane load distribution and tailor the buckling mode shapes, respectively, for improving structural performance. To leverage the benefits of using VAT laminates in the practical applications, in the present paper, we discuss buckling load maximization conducted for a stiffened VAT laminated plate with a central cutout considering VAT laminate manufacturing constraints. Three representative boundary conditions as seen in the aerospace structures are considered: in-plane axial displacement, in-plane pure shear, and in-plane pure bending displacements. Two common manufacturing constraints, the one on the automatic fiber placement (AFP) manufacturing head turning radius and the other on the tow gap/overlap, while fabricating VAT laminates are considered in the laminate design. These two manufacturing constraints are modeled by controlling the fiber path radius of curvature and tape parallelism in optimizing the fiber path orientations for the VAT laminates. Stiffener layout and fiber path angle for the VAT laminated plates are both considered in the buckling load maximization study. To avoid using a fine mesh in modeling the stiffened VAT laminates with a cutout when employing the finite element analysis during the optimization, the VAT laminated plate and the stiffeners are modeled independently. The displacement compatibility is enforced at the stiffener–plate interfaces to ensure that the stiffeners move with the plate. Particle swarm optimization is used as the optimization algorithm for the buckling load maximization study. Optimization results show that, without considering AFP manufacturing constraints, the VAT laminates can increase the buckling loads by 21.2% and 12.4%, respectively, comparing to the commonly used quasi-isotropic laminates and traditionally straight fiber path laminates for the structure under the in-plane axial displacement case, 19.7% and 12.5%, respectively, for the in-plane shear displacement case, and 62.1% and 26.6%, respectively, for the in-plane bending displacement case. The AFP manufacturing constraints are found to have different impacts on the buckling responses for the VAT laminates, which cause the maximum buckling load to be 9.3–10.1%, 3.0–3.2%, and 23.2–29.8% less than those obtained without considering AFP manufacturing constraints, respectively, for the present studied model under in-plane axial, shear, and bending displacements. Published version

The paper describes a fully automated process to generate a shell-based finite element model of a large hybrid truck chassis to perform mass optimization considering multiple load cases and multiple constraints. A truck chassis consists of different parts that could be optimized using shape and size optimization. The cross members are represented by beams, and other components of the truck (batteries, engine, fuel tanks, etc.) are represented by appropriate point masses and are attached to the rail using multiple point constraints to create a mathematical model. Medium-fidelity finite element models are developed for front and rear suspensions and they are attached to the chassis using multiple point constraints, hence creating the finite element model of the complete truck. In the optimization problem, a set of five load conditions, each of which corresponds to a road event, is considered, and constraints are imposed on maximum allowable von Mises stress and the first vertical bending frequency. The structure is optimized by implementing the particle swarm optimization algorithm using parallel processing. A mass reduction of about 13.25% with respect to the baseline model is achieved. Published version

During mitosis, cells round up and generate outward forces to create space and orient the mitotic spindles. Here, using suspended ECM-mimicking nanofiber networks, we recapitulate in vivo adhesion organization and confinement to interrogate mitotic outcomes for various interphase cell shapes. Elongated cells attached to single fibers through two focal adhesion clusters (FACs) at their extremities result in perfect spherical mitotic cell bodies that undergo large 3D displacement while being held by retraction fibers. Increasing the number of parallel fibers increases cellular extremity FACs and retraction fiber-driven stability, leading to reduced 3D cell-body movement, metaphase plate rotations, and significantly faster division times. Interestingly, interphase kite shapes on a crosshatch pattern of four fibers undergo mitosis resembling single-fiber outcomes due to rounded bodies being primarily held in position by retraction fibers from two perpendicular suspended fibers. We develop a cortex-astral microtubule analytical friction and force model to capture retraction-fiber-driven stability of the metaphase plate rotations. We report that reduced orientational stability results in increased monopolar mitotic defects. In the case of cells attached to two parallel fibers, rounded mitotic cells can get confined between the suspended fibers, allowing estimation of the mitotic forces through measurement of the outward deflection of the fibers. Interestingly, confinement causes rotated mitotic spindles similar to those reported in dense tissues. Overall, we establish dynamics of mitosis in fibrous environments governed by fiber arrangement and architecture-driven differences in interphase cell shapes, adhesion geometries, and varying levels of mechanical confinement.

Under certain loading conditions, curvilinearly stiffened panels are seen to have better structural performance while being lighter in mass when compared to panels with straight stiffeners. A curvi...

This article conducts a multiobjective optimization study for minimizing both the actuator energy and the total drag of a NASA N + 2 aircraft, the Blended-Wing-Body (BWB), during cruising, by using...

This paper presents results from thermal buckling analysis and optimization of stiffened composite panels with variable angle tow (VAT) laminates and curvilinear stiffeners. Considering the meshing...

Compared to straight fiber path laminates, variable-angle tow (VAT) laminates are known to redistribute the in-plane stress resultants for improving structural buckling response. The VAT laminates ...

This paper studies weight minimization of a composite flying-wing aircraft using bioinspired arbitrarily shaped spars and ribs, known as SpaRibs, for the internal structural layout design. A genera...

With the advances being made in additive manufacturing, it is becoming increasingly possible to fabricate a broad class of complex-shaped designs for practical applications. This manufacturing capa...