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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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An Aerospace Engineer in Defense Policy's Court: Nuclear Deterrence Policy and the Nuclear Weapons Council.
Abstract not provided.
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil
Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.
The Fluid Dynamics Uncertainty Quantification Challenge Problem: XFOIL vs. MFOIL
Uncertainty quantification (UQ) has become more critical in aerospace engineering due to the growing dependence on computational tools for design optimization and performance analyses of aerospace vehicles. Even though the significance of UQ in assessing the credibility of computational analyses is well recognized, its costs and complexity impede its integration into standard practices, particularly in computational fluid dynamics (CFD) and other fluid analyses. This paper presents a UQ study for low-fidelity computational aerodynamics analyses with XFOIL and mfoil (i.e., the MATLAB version of XFOIL with several implementation modifications); these tools are utilized widely in both research and education. The main contributions of this paper are as follows: 1) improved precision in quantifying the uncertainty of the baseline Monte Carlo results used to benchmark surrogate modeling techniques for UQ, 2) quantification of the effect of the implementation differences between XFOIL and mfoil on solution quantities of interest (QoIs), such as lift and pitching moment coefficients, and 3) development of an open-source UQ library for use with XFOIL and mfoil, which has educational values and helps promote UQ for fluid analyses with aerospace applications. Results and discussions revolve around cases 1-4 of the challenge problem posed by the AIAA Fluid Dynamics Technical Committee’s Uncertainty Quantification Discussion Group (UQDG). In case 3, this work employs CFDverify, an open-source solution verification software, to quantify the discretization error and evaluate the extrapolated QoIs based on the grid convergence index (GCI). This UQ study differentiates itself from previous studies in the rigor of handling baseline Monte Carlo uncertainty and in including mfoil, which is a more accessible alternative to XFOIL. Finally, despite the growing computing power, low-fidelity computational tools remain valuable, such as for aerodynamic shape optimization at Mach numbers below 0.65 and low-to-mid Reynolds numbers.
Transition to High-Hydrogen Fueled Advanced Gas Turbine Engines for Land-Based Power Generation and Industrial Applications
Invited lecture presented as part of the 2023 University of Central Florida - Mechanical and Aerospace Engineering Seminar Series. These slides discuss the role of multi-sourced hydrogen for electrical power generation. Particular focus is given to the combustion of hydrogen for the purpose of chemical to thermal energy. Pressure gain combustion is given as an example technology that could leverage hydrogen as a fuel source ultimately leading to higher rates of energy conversion (chemical to electrical) resulting in greater energy security and reduced cost of electricity.
Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks
Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.
Thermophysical Properties of High-Frequency Induction Heat Sintered Graphene Nanoplatelets/Alumina Ceramic Functional Nanocomposites
This paper concerns the thermophysical properties of high-frequency induction heat (HFIH) sintered alumina ceramic nanocomposites containing various graphene nanoplatelets (GNP) concentrations. The GNP/alumina nanocomposites demonstrated high densities, fine-grained microstructures, highest fracture toughness and hardness values of 5.7 MPa m{sup 1/2} and 18.4 GPa, which found 72 and 8%, superior to the benchmarked monolithic alumina, respectively. We determine the role of GNP in tuning the microstructure and inducing toughening mechanisms in the nanocomposites. The sintered monolithic alumina exhibited thermal conductivity value of 24.8 W/mK; however, steady drops of 2, 15 and 19% were recorded after adding respective GNP contents of 0.25, 0.5 and 1.0 wt.% in the nanocomposites. In addition, a dwindling trend in thermal conductions with increasing temperatures was recorded for all sintered samples. Simulation of experimental results with proven theoretical thermal models showed the dominant role of GNP dispersions, microstructural porosity, elastic modulus and grain size in controlling the thermal transport properties of the GNP/alumina nanocomposites. Thermogravimetric analysis showed that the nanocomposite with up to 0.5 mass% of GNP is thermally stable at the temperatures greater than 875 °C. The GNP/alumina nanocomposites owning a distinctive combination of mechanical and thermal properties are promising contenders for the specific components of the aerospace engine and electronic devices having contact with elevated temperatures.
Hybrid CFRP/SWCNT Composites with Enhanced Electrical Conductivity and Mechanical Properties
The results of the investigation dealing with enhancing the mechanical and functional properties of carbon fiber-reinforced polymers are presented in the paper. The scientific issue is relevant enough to the aerospace engineering where strength-to-density ratio is one of the most important properties providing sufficient strength and stiffness along with minimal weight, while electrical and heat conductivity are required for lightning strike protection and better efficiency of thermal deicing systems, respectively. In the present research, insufficient electric conductivity of CFRP is improved by adding single-wall carbon nanotubes. Measurement of electrical resistivity of modified composites as well as CNT-filled epoxy is taken. The results of mechanical testing demonstrate unchanged tensile strength, while flexural strength increases, which is attributed to higher interfacial shear strength of hybrid CFRP/CNT composites.
A Quick Screening Approach for Design of Multi-Principle Element Alloy with Solid Solution Phase
Multi-principle element alloys (MPEAs) provide an innovative way to design materials with potential applications in extreme conditions, such as high-dose irradiation in nuclear reactors, high temperatures in aeronautic and aerospace engineering, etc. However, the composition design and optimization of MPEAs are challenging due to the vast options and complexity which make traditional theory and methods insufficient or even invalid. To effectively design MPEAs, a two-parameter approach is developed based on newly proposed enthalpy and entropy-enthalpy competition parameters. This approach can not only be used to classify conventional steels, high-entropy steels (HESs), high-entropy alloys (HEAs) and amorphous alloys, but also to predict the optimal composition of MPEAs with stable solid solution (SS) phases. The optimal compositions of three sets of Fe-Mn-Cr-Co-Ni alloys are quickly screened from millions of options to form HESs or HEAs with stable SS phases. This quick screening approach can also be applied to design other alloys and facilitate the research and development of MPEAs possessing the targeted properties and economic acceptability.
Superelasticity and cryogenic linear shape memory effects of CaFe 2 As 2
Shape memory materials have the ability to recover their original shape after a significant amount of deformation when they are subjected to certain stimuli, for instance, heat or magnetic fields. But, their performance is often limited by the energetics and geometry of the martensitic-austenitic phase transformation. We report a unique shape memory behavior in CaFe 2 As 2 , which exhibits superelasticity with over 13% recoverable strain, over 3 GPa yield strength, repeatable stress–strain response even at the micrometer scale, and cryogenic linear shape memory effects near 50 K. These properties are acheived through a reversible uni-axial phase transformation mechanism, the tetragonal/orthorhombic-to-collapsed-tetragonal phase transformation. These results offer the possibility of developing cryogenic linear actuation technologies with a high precision and high actuation power per unit volume for deep space exploration, and more broadly, suggest a mechanistic path to a class of shape memory materials, ThCr 2 Si 2 -structured intermetallic compounds.
Protein structural changes on a CubeSat under rocket acceleration profile
Catalyzing life-sustaining reactions, proteins are composed by 20 different amino acids that fold into a compact yet flexible three-dimensional architecture, which dictates what their function(s) might be. Determining the spatial arrangement of the atoms, the protein’s 3D structure, enables key advances in fundamental and applied research. Protein crystallization is a powerful technique to achieve this. Unlike Earth’s crystallization experiments, biomolecular crystallization in space in the absence of gravitational force is actively sought to improve crystal growth techniques. However, the effects of changing gravitational vectors on a protein solution reaching supersaturation remain largely unknown. Here, we have developed a low-cost crystallization cell within a CubeSat payload module to exploit the unique experimental conditions set aboard a sounding rocket. We designed a biaxial gimbal to house the crystallization experiments and take measurements on the protein solution in-flight with a spectrophotometry system. After flight, we used X-ray diffraction analysis to determine that flown protein has a structural rearrangement marked by loss of the protein’s water and sodium in a manner that differs from crystals grown on the ground. We finally show that our gimbal payload module design is a portable experimental setup to take laboratory research investigations into exploratory space flights.
A reusable neural network pipeline for unidirectional fiber segmentation
Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.
Airfoil leading edge blowing to control bow shock waves
Abstract This manuscript presents a detailed characterization of active control of bow shock waves via leading edge injection, including subsonic coolant ejection and the appearance of Coanda effects. The flow phenomena occurring at steady and pulsating flow injection regimes were analyzed using steady and unsteady two-dimensional Reynolds-Averaged Navier Stokes, leading to a precise evaluation of the thermal load and drag reductions. Steady supersonic injection yields the largest abatement in thermal load and aerodynamic drag, while subsonic or fluctuating ones can also provide significant improvements at reduced cooling mass flow rates. Furthermore, a Coanda effect, causing a non-symmetric flow topology, was observed and analyzed for reduced injection port size. This Coanda effect is due to the sudden expansion happening from the injection port to the main flow and it causes the flow topology at the leading edge to become non-symmetric despite the complete symmetry of the problem. This is the first time in the literature such a phenomenon is documented for a supersonic airfoil leading edge injection. Furthermore, it enables the design of novel flow control strategies for the leading edge shock topology and flow structures in supersonic flows.
Multi-fidelity information fusion with concatenated neural networks
Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.
Shock wave formation from head-on collision of two subsonic vortex rings
Vortex ring collisions have attracted intense interest in both water and air studies (Baird in Proc R Soc Lond Ser Math Phys Sci 409:59–65, 1987, Poudel et al. in Phys Fluids 33:096105, 2021, Lim and Nickels in Nature 357:225, 1992, New et al. in Exp Fluids 57:109, 2016, Suzuki et al. in Geophys Res Lett 34, 2007, Yan et al. in J Fluids Eng 140:054502, 2018, New et al. in J Fluid Mech 899, 2020, Cheng et al. in Phys Fluids 31:067107, 2019, Hernández and Reyes in 29:103604, 2017, Mishra et al. in Phys Rev Fluids, 2021, Zednikova et al. in Chem Eng Technol 42:843–850, 2019, Kwon et al. in Nature 600:64–69, 2021). These toroidal structures spin around a central axis and travel in the original direction of impulse while spinning around the core until inertial forces become predominant causing the vortex flow to spontaneously decay to turbulence (Vortex Rings, https://projects.iq.harvard.edu/smrlab/vortex-rings). Previous studies have shown the collision of subsonic vortex rings resulting in reconnected vortex rings, but the production of a shock wave from the collision has not been demonstrated visibly (Lim and Nickels in Nature 357:225, 1992, Cheng et al. in Phys Fluids 31:067107, 2019). Here we present the formation of a shock wave due to the collision of explosively formed subsonic vortex rings. As the vortex rings travel at Mach 0.66 toward the collision point, they begin to trap high pressure air between them. Upon collision, high pressure air was imploded and released radially away from the axis of the collision, generating a visible shock wave traveling through and away from the colliding vortices at Mach 1.22. Our results demonstrate a pressure gradient with high pressure release creating a shock wave. We anticipate our study to be a starting point for more explosively formed vortex collisions. For example, explosives with different velocities of detonation could be tested to produce vortex rings of varying velocities.