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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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59 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Inverse Design of Low-Boom Supersonic Concepts Using Reversed Equivalent-Area Targets

A promising path for developing a low-boom configuration is a multifidelity approach that (1) starts from a low-fidelity low-boom design, (2) refines the low-fidelity design with computational fluid dynamics (CFD) equivalent-area (Ae) analysis, and (3) improves the design with sonic-boom analysis by using CFD off-body pressure distributions. The focus of this paper is on the third step of this approach, in which the design is improved with sonic-boom analysis through the use of CFD calculations. A new inverse design process for off-body pressure tailoring is formulated and demonstrated with a low-boom supersonic configuration that was developed by using the mixed-fidelity design method with CFD Ae analysis. The new inverse design process uses the reverse propagation of the pressure distribution (dp/p) from a mid-field location to a near-field location, converts the near-field dp/p into an equivalent-area distribution, generates a low-boom target for the reversed equivalent area (Ae,r) of the configuration, and modifies the configuration to minimize the differences between the configuration s Ae,r and the low-boom target. The new inverse design process is used to modify a supersonic demonstrator concept for a cruise Mach number of 1.6 and a cruise weight of 30,000 lb. The modified configuration has a fully shaped ground signature that has a perceived loudness (PLdB) value of 78.5, while the original configuration has a partially shaped aft signature with a PLdB of 82.3.

Li, Wu↗
NASA NTRS2017

Building a Practical Natural Laminar Flow Design Capability

A preliminary natural laminar flow (NLF) design method that has been developed and applied to supersonic and transonic wings with moderate-to-high leading-edge sweeps at flight Reynolds numbers is further extended and evaluated in this paper. The modular design approach uses a knowledge-based design module linked with different flow solvers and boundary layer stability analysis methods to provide a multifidelity capability for NLF analysis and design. An assessment of the effects of different options for stability analysis is included using pressures and geometry from an NLF wing designed for the Common Research Model (CRM). Several extensions to the design module are described, including multiple new approaches to design for controlling attachment line contamination and transition. Finally, a modification to the NLF design algorithm that allows independent control of Tollmien-Schlichting (TS) and cross flow (CF) modes is proposed. A preliminary evaluation of the TS-only option applied to the design of an NLF nacelle for the CRM is performed that includes the use of a low-fidelity stability analysis directly in the design module.

Campbell, Richard L.↗
NASA NTRSDate not supplied

Robust Design Under Uncertainty of Hypersonic Inlets

The objective of this work is to optimize scramjet inlet designs under uncertainty at multiple fidelity levels. Optimizations are performed for both deterministic and stochastic conditions. For stochastic conditions, robust design is used to minimize the variance in system performance in addition to optimizing mean system performance. Uncertainty quantification is performed using non-intrusive polynomial chaos. Comparisons are made between low-fidelity analysis and high-fidelity analysis, as well as deterministic optimization and robust optimization. Results indicate that the general design trend of previous inlet optimization techniques is recovered for the external compression portion of an inlet. For the internal compression portion of an inlet, when viscous effects become more significant and boundary layer separation becomes more likely, the optimal trend departs from previous results. Multifidelity uncertainty quantification methods are found to substantially reduce the computation time of robust design while obtaining results that nearly match those of high-fidelity analysis. Robust optimization is found to decrease the standard deviation of throat Mach number by up to 40% compared to deterministic optimization. Changes in the design variables and total turning that lead to robust scramjet inlets are identified and linked back to fundamental physical principles through the theta-beta-Mach function.

Hypersonic↗
NASA NTRSDate not supplied

Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning

Multi-model Monte Carlo methods have been demonstrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation amongst the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Preliminary results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC. The challenges and potential benefits to exploring a fully iterative and comprehensive optimization strategy in future work are highlighted.

uncertainty quantification↗