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Valvular heart disease has recently become an increasing public health concern due to the high prevalence of valve degeneration in aging populations. For patients with severely impacted aortic valves that require replacement, catheter-based bioprosthetic valve deployment offers a minimally invasive treatment option that eliminates many of the risks associated with surgical valve replacement. Although recent percutaneous device advancements have incorporated thinner, more flexible biological tissues to streamline safer deployment through catheters, the impact of such tissues in the complex, mechanically demanding, and highly dynamic valvular system remains poorly understood. The present work utilized a validated computational fluid–structure interaction approach to isolate the behavior of thinner, more compliant aortic valve tissues in a physiologically realistic system. This computational study identified and quantified significant leaflet flutter induced by the use of thinner tissues that initiated blood flow disturbances and oscillatory leaflet strains. The aortic flow and valvular dynamics associated with these thinner valvular tissues have not been previously identified and provide essential information that can significantly advance fundamental knowledge about the cardiac system and support future medical device innovation. Considering the risks associated with such observed flutter phenomena, including blood damage and accelerated leaflet deterioration, this study demonstrates the potentially serious impact of introducing thinner, more flexible tissues into the cardiac system.

This work couples high-fidelity moving-domain finite element compressible flow modeling with a Surrogate Management Framework (SMF) for optimization to effectively design a variable speed gas turbine stage. The superior accuracy of high-fidelity modeling, however, comes with relatively high computational costs, which are further amplified in the iterative design process that relies on parametric sweeps. An innovative approach is developed to reduce the number of iterations needed for optimal design, leading to a significant reduction in the computational cost without sacrificing the high fidelity of the analysis. The proposed design optimization approach is applied to a novel incidence-tolerant turbomachinery blade technology that articulates the stator- and rotor-blade positions of an annular single-stage high pressure turbine to achieve peak performance. This work also extends our understanding of rotor–stator interactions by simulating complex internal flows occurring during multi-speed turbine operation. Potential variable-speed gas turbine stage designs and the proposed optimization approach are presented to provide valuable insight into this new turbomachinery technology that can positively impact future propulsion systems.

The 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.

Heart valves play a critical role in maintaining proper cardiovascular function in the human heart; however, valve diseases can lead to improper valvular function and reduced cardiovascular performance. Depending on the extent and severity of the valvular disease, replacement operations are often required to ensure that the heart continues to operate properly in the cardiac system. Transcatheter aortic valve replacement (TAVR) procedures have recently emerged as a promising alternative to surgical replacement approaches because the percutaneous methods used in these implant operations are significantly less invasive than open heart surgery. Despite the advantages of transcatheter devices, the precise deployment, proper valve sizing, and stable anchoring required to securely place these valves in the aorta remain challenging even in successful TAVR procedures. This work proposes a parametric modeling approach for transcatheter heart valves (THVs) that enables flexible valvular development and sizing to effectively generate existing and novel valve designs. This study showcases two THV configurations that are analyzed using an immersogeometric fluid–structure interaction (IMGA FSI) framework to demonstrate the influence of geometric changes on THV performance. The proposed modeling framework illustrates the impact of these features on THV behavior and indicates the effectiveness of parametric modeling approaches for enhancing THV performance and efficacy in the future.

In this work, we propose and validate a new stabilized compressible flow finite element framework for the simulation of aerospace applications. The framework is comprised of the streamline upwind/Petrov–Galerkin (SUPG)-based Navier–Stokes equations for compressible flows, the weakly enforced essential boundary conditions that act as a wall function, and the entropy-based discontinuity-capturing equation that acts as a shock-capturing operator. The accuracy and robustness of the framework is tested for various Mach numbers ranging from low-subsonic to transonic flow regimes. The aerodynamic simulations are carried out for 2D and 3D validation cases of flow around the NACA 0012 airfoil, RAE 2822 airfoil, ONERA M6 wing, and NASA Common Research Model (CRM) aircraft. The pressure coefficients obtained from the simulations of all cases are compared with experimental data. The computational results show good agreement with the experimental findings and demonstrate the accuracy and effectiveness of the finite element framework presented in this work for the simulation of aircraft aerodynamics.

Many aerospace applications involve complex multiphysics in compressible flow regimes that are challenging to model and analyze. Fluid–structure interaction (FSI) simulations offer a promising approach to effectively examine these complex systems. In this work, a fully coupled FSI formulation for compressible flows is summarized. The formulation is developed based on an augmented Lagrangian approach and is capable of handling problems that involve nonmatching fluid–structure interface discretizations. The fluid is modeled with a stabilized finite element method for the Navier–Stokes equations of compressible flows and is coupled to the structure formulated using isogeometric Kirchhoff–Love shells. To solve the fully coupled system, a block-iterative approach is used. To demonstrate the framework’s effectiveness for modeling industrial-scale applications, the FSI methodology is applied to the NASA Common Research Model (CRM) aircraft to study buffeting phenomena by performing an aircraft pitching simulation based on a prescribed time-dependent angle of attack.

Aircraft horizontal stabilizers are prone to fatigue damage induced by the flow separation from aircraft wings and the subsequent impingement on the stabilizer structure in its wake, which is known as a buffet event. In this work, the previously developed isogeometric blended shell approach is reformulated in a dynamic analysis setting for the simulation of aircraft takeoff using varying pitch angles. The proposed Kirchhoff–Love (KL) and continuum shell blending allows the critical structural components of the aircraft horizontal stabilizer to be modeled using continuum shells to obtain high-fidelity 3D stresses, whereas the less critical components are modeled using computationally efficient KL thin shells. The imposed aerodynamic loads are generated from a hybrid immersogeometric and boundary-fitted computational fluid dynamics (CFD) analysis to accurately record the dynamic excitation on the stabilizer external surface. Specifically, the entire aircraft except for the wings and stabilizers is immersed into a non-boundary-fitted fluid domain based on the immersogeometric analysis (IMGA) concept for computational savings, whereas the mesh surrounding the aircraft wing and stabilizers is boundary-fitted to accurately compute the aerodynamic loads on the stabilizer. The obtained time histories of the loads are then applied to dynamic blended shell analysis of the horizontal stabilizer, and the high-fidelity stress response is evaluated for subsequent fatigue assessment. A simple frequency-domain fatigue analysis is then carried out to evaluate the buffet-induced fatigue damage of the stabilizer. The results from both the steady-state and dynamic nonlinear blended shell analyses of a representative horizontal stabilizer demonstrate the numerical accuracy and computational efficiency of the proposed approach.

Immersogeometric analysis (IMGA) is a geometrically flexible method that enables one to perform multiphysics analysis directly using complex computer-aided design (CAD) models. In this paper, we develop a novel IMGA approach for simulating incompressible and compressible flows around complex geometries represented by point clouds. The point cloud object's geometry is represented using a set of unstructured points in the Euclidean space with (possible) orientation information in the form of surface normals. Due to the absence of topological information in the point cloud model, there are no guarantees for the geometric representation to be watertight or 2-manifold or to have consistent normals. To perform IMGA directly using point cloud geometries, we first develop a method for estimating the inside-outside information and the surface normals directly from the point cloud. We also propose a method to compute the Jacobian determinant for the surface integration (over the point cloud) necessary for the weak enforcement of Dirichlet boundary conditions. We validate these geometric estimation methods by comparing the geometric quantities computed from the point cloud with those obtained from analytical geometry and tessellated CAD models. In this work, we also develop thermal IMGA to simulate heat transfer in the presence of flow over complex geometries. The proposed framework is tested for a wide range of Reynolds and Mach numbers on benchmark problems of geometries represented by point clouds, showing the robustness and accuracy of the method. Finally, we demonstrate the applicability of our approach by performing IMGA on large industrial-scale construction machinery represented using a point cloud of more than 12 million points.

A digital twin (DT), with the components of a physics-based model, a data-driven model, and a machine learning (ML) enabled efficient surrogate, behaves as a virtual twin of the real-world physical process. In terms of Laser Powder Bed Fusion (L-PBF) based additive manufacturing (AM), a DT can predict the current and future states of the melt pool and the resulting defects corresponding to the input laser parameters, evolve itself by assimilating in-situ sensor data, and optimize the laser parameters to mitigate defect formation. In this paper, we present a deep neural operator enabled computational framework of the DT for closed-loop feedback control of the L-PBF process. This is accomplished by building a high-fidelity computational model to accurately represent the melt pool states, an efficient surrogate model to approximate the melt pool solution field, followed by an physics-based procedure to extract information from the computed melt pool simulation that can further be correlated to the defect quantities of interest (e.g., surface roughness). In particular, we leverage the data generated from the high-fidelity physics-based model and train a series of Fourier neural operator (FNO) based ML models to effectively learn the relation between the input laser parameters and the corresponding full temperature field of the melt pool. Subsequently, a set of physics-informed variables such as the melt pool dimensions and the peak temperature can be extracted to compute the resulting defects. An optimization algorithm is then exercised to control laser input and minimize defects. On the other hand, the constructed DT can also evolve with the physical twin via offline finetuning and online material calibration. Finally, a probabilistic framework is adopted for uncertainty quantification. The developed DT is envisioned to guide the AM process and facilitate high-quality manufacturing.

Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. Therefore, a 2-year field study was undertaken with precision nutrient and irrigation management under system of crop intensification (SCI) to understand the applicability of sensor-based decision tools in improving the physiological performance, water productivity, and seed yield of soybean crop. The experiment consisted of three irrigation regimes [I 1 : standard flood irrigation at 50% depletion of available soil moisture (DASM) (FI), I 2 : sprinkler irrigation at 80% ET C (crop evapo-transpiration) (Spr 80% ET C ), and I 3 : sprinkler irrigation at 60% ET C (Spr 60% ET C )] assigned in main plots, with five precision nutrient management (PNM) practicesPNM 1 -[SCI protocol], PNM 2 -[RDF, recommended dose of fertilizer: basal dose incorporated (50% N, full dose of P and K)], PNM 3 -[RDF: basal dose point placement (BDP) (50% N, full dose of P and K)], PNM 4 -[75% RDF: BDP (50% N, full dose of P and K)] and PNM 5 -[50% RDF: BDP (50% N, full P and K)] assigned in sub-plots using a split-plot design with three replications. The remaining 50% N was top-dressed through SPAD assistance for all the PNM practices. Results showed that the adoption of Spr 80% ET C resulted in an increment of 25.6%, 17.6%, 35.4%, and 17.5% in net-photosynthetic rate (P n ), transpiration rate (T r ), stomatal conductance (G s ), and intercellular CO 2 concentration (C i ), respectively, over FI. Among PNM plots, adoption of PNM 3 resulted in a significant ( p =0.05) improvement in photosynthetic characters like P n (15.69 µ mol CO 2 m −2 s −1 ), T r (7.03 m mol H 2 O m −2 s− 1 ), G s (0.175 µmol CO 2 mol −1 year −1 ), and C i (271.7 mol H 2 O m 2 s −1 ). Enhancement in SPAD (27% and 30%) and normalized difference vegetation index (NDVI) (42% and 52%) values were observed with nitrogen (N) top dressing through SPAD-guided nutrient management, helped enhance crop growth indices, coupled with better dry matter partitioning and interception of sunlight. Canopy temperature depression (CTD) in soybean reduced by 3.09–4.66°C due to adoption of sprinkler irrigation. Likewise, Spr 60% ETc recorded highest irrigation water productivity (1.08 kg ha −1 m −3 ). However, economic water productivity (27.5 INR ha −1 m −3 ) and water-use efficiency (7.6 kg ha −1 mm −1 day −1 ) of soybean got enhanced under Spr 80% ETc over conventional cultivation. Multiple correlation and PCA showed a positive correlation between physiological, growth, and yield parameters of soybean. Concurrently, the adoption of Spr 80% ET C with PNM 3 recorded significantly higher grain yield (2.63 t ha −1 ) and biological yield (8.37 t ha −1 ) over other combinations. Thus, the performance of SCI protocols under sprinkler irrigation was found to be superior over conventional practices. Hence, integrating SCI with sensor-based precision nutrient and irrigation management could be a viable option for enhancing the crop productivity and enhance the resource-use efficiency in soybean under similar agro-ecological regions.