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ESAero’s software, which has been known by the names HAPSS, DOETech, TOGW Framework, and PANTHER over the last decade. The general capabilities and archi- tecture of these tools have been published [67], but the software itself is proprietary. The PANTHER tool has at least been used for analysis and to generate tradespace con- tour plots, but it is unclear whether an airplane-level multidisciplinary design analysis and optimization (MDAO) capability has been implemented. Numerical optimization has been used to design a Meredith effect radiator [67]. Another known, unified framework is the Georgia Tech GT-HEAT framework [154]. This is a propulsion-focused model: All components, even electrical models, are im- plemented in NPSS. This modeling framework has good fidelity in propulsion and electronics but lacks important capabilities in aerodynamics, structure, and cost mod- eling. Optimization was conducted using a design of experiments (DOE) and surrogate modeling methodology [155]. NASA has been developing modeling, simulation, and optimization capabilities for the X-57, N-3X, STARC-ABL, and PEGASUS studies, mostly at conceptual levels of fidelity. Capristan and Welstead introduced the LEAPS software package, designed as a replacement for NASA’s FLOPS mission analysis software but supporting distinc- tive features of aircraft EP (including mixed electric and fuel-burning propulsion and independent throttling). LEAPS uses energy-based methods to provide low-fidelity, low-cost estimates of somewhat optimized mission trajectories [156, 157]. Basic MDAO capabilities were implemented to guide NASA studies [51, 141]). More recently, Hwang and Ning [158] developed a medium-fidelity optimization tool for ve- hicles similar to the X-57, incorporating blade element momentum theory propulsion modeling, vortex lattice aerodynamic analysis, finite element structural sizing, auto- matic derivatives using the adjoint method (to handle hundreds of design variables), and full mission analysis; however, thermal modeling was not included. Falck et al. [141] demonstrated the need for modeling thermal constraints when trajectory optimization is considered. The tool was implemented in OpenMDAO to facilitate gradient-based optimization and computation of derivatives [159]. Despite significant progress on particular studies, there is no single NASA optimization en- vironment for electric propulsion trade studies, and high-fidelity analysis has not yet been incorporated. Gray et al. [160] conducted high-fidelity aeropropulsive shape optimization of the STARC-ABL’s aft tailcone propulsor. A Reynolds-averaged Navier-Stokes (RANS) CFD solution was fully coupled to a 1-D thermodynamic cycle model using the Open- MDAO framework. This combination enables direct physical modeling of boundary- layer ingestion. The flow solver and thermodynamic cycle model both provide efficient derivatives, enabling the use of a coupled adjoint method to compute total derivatives and perform optimization with respect to hundreds of geometric design variables. This is the first published shape optimization of a hybrid electric aircraft using high-fidelity CFD, but the scope of the MDAO problem was limited to the region of the aft tailcone propulsor; a full-airplane optimization has not yet been conducted. The fully coupled aeropropulsive optimization found that the fuel burn reduction contribution from BLI was attributable to both aerodynamic effects and propulsive ef- fects (8 to 12 counts from aerodynamic drag; 16 effective counts from propulsion). The 33PDF Image | Electric, hybrid, and turboelectric fixed-wing aircraft
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