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Ben D. Phillips

Publications and source records attributed to Ben D. Phillips.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Uncertainty Quantification of CFD Model Assumptions Against Sonic Boom Noise Prediction of a Commercial Supersonic Transport

This paper presents the results of uncertainty modeling of sonic boom noise generation from commercial supersonic transport considering the Spalart-Allmaras (SA) turbulence modeling parameters as well as Mach number, angle of attack and altitude. Sample generation and analysis for this uncertainty model was performed by UQPCE, which is a software package developed at the NASA Langley Research Center. To build the uncertainty model, 42 cases of sonic boom noise calculation were performed. Computation of the ground noise can be briefly summarized in two steps. First, the near field pressure waveforms are sampled from CFD calculation using the NASA Langley’s FUN3D solver. Second, this information is passed to an atmospheric propagation code, sBOOM, which solves an augmented Burger’s equation and simulates how the near field waveforms will change while passing through the atmosphere. The ground signature is further processed to obtain the perceived loudness, PLdB. Having a high spatial resolution near the shock wave in the CFD calculation is critical in sonic boom noise prediction. Because the variation in the input parameters for the current uncertainty quantification (UQ) study is likely to lead to change in shock location, angle and strength, the grid adaptation for shock capturing is independently applied for each condition. The final mesh used in the CFD calculation consists of approximately 420 million cells. The pressure signatures are sampled at three, four and five body lengths away from the aircraft to make sure the three dimensional effects around the aircraft are resolved. The results of the UQ analysis shows that within the three aleatory variables, the angle of attack had the most impact against ground noise, followed by the altitude and the Mach number. Between the two SA model parameters, the Kármán constant (𝜅) was significantly more important than the turbulent Prandtl number (𝜎), but these two parameters were only marginally significant in the overall prediction variance in ground noise. The UQ procedure explained in this paper can be widely applied to other model parameters.

Uncertainty Quantifications↗
NASA NTRSDate not supplied

Methods for System-Level Multidisciplinary Uncertainty Analysis of Low-Boom Flight Vehicles

Current research supporting NASA’s Commercial Supersonic Technology project is focused on the efficient prediction of uncertainty in sonic boom loudness generated by low-boom aircraft concepts. This paper focuses on research incorporating aircraft trim and aerostructural analysis into a multidisciplinary system-level uncertainty analysis. This enables the modeling of a steady-state representation of a point in the uncertainty space, simulating the vehicle as it would be flown. This approach also enables multiple uncertain parameters defining the configuration of the vehicle to be reduced to three: Mach number, altitude, and aircraft weight. To demonstrate this methodology, a case study exploring a conceptual low-boom supersonic aircraft is performed. Two different approaches are used to model the interactions between nearfield pressure signature analysis and sonic boom propagation, and their performance is evaluated in terms of accuracy and computational expense. One method uses a set of local surrogate models to generate a large number of nearfield signatures and perform Monte Carlo analysis. This method is found to produce, at a lower expense, uncertainty metrics that are comparable to the second method, in which uncertainty metrics are computed based on loudness metric values obtained directly from simulated nearfield signatures.

UQ↗
NASA NTRSDate not supplied

Integration of Uncertainty Quantification in a Model-Based Systems Analysis and Engineering Framework

This paper presents a technical approach to improve the confidence in the systems analysis process by integrating Uncertainty Quantification (UQ) techniques within a Model-Based Systems Analysis and Engineering (MBSA&E) framework. The MBSA&E architecture uses system models and multidisciplinary analytical solutions as central artifacts for system design and analysis. The integration of UQ enables engineers to assess and mitigate uncertainties associated with a system model, design parameters, and constraint inputs, leading to more complete design studies and further informed decision-making processes. The proposed approach leverages the strengths of MBSA&E and extends it with a UQ methodology to quantify uncertainties in the input parameters and to trace the uncertainties as they propagate throughout the system model. To demonstrate the effectiveness of an integrated MBSA&E-UQ approach, a case study involving a simplified analysis of a Transonic Truss-Braced Wing (TTBW) concept vehicle is performed. This integration enables a more comprehensive evaluation of system performance and behavior under uncertainty and a more robust approach for system design and analysis. Lastly, the paper addresses the challenges and considerations associated with integrating UQ into an MBSA&E framework.

systems analysis↗