Search NASA⌕ Search

Engineering topics

Patrick S Heaney

Publications and source records attributed to Patrick S Heaney.

Space Launch System Unsteady Forces Developed from Unsteady-Pressure-Sensitive-Paint–Based Corcos Model Parameters

During atmospheric ascent, launch vehicles (LVs) experience large dynamic loads at transonic conditions where aerodynamic buffet is most critical. To estimate buffet loads, coupled loads analyses typically utilize suitable forcing functions, called buffet forcing functions (BFFs). One of the key buffet environment contributors is the turbulent boundary layer (TBL) on the LV outer skin. The TBL-induced fluctuating pressures can be estimated using the widely-accepted Corcos model. In the context of transonic buffet, the performance of this model is not well established, partly because of lack of data. To fill this gap, NASA recently acquired extremely high-spatial-density data for the Space Launch System (SLS) vehicle, using the unsteady pressure sensitive paint (uPSP) optical measurement technique. A methodology is developed for validation of the Corcos model using these unique data, with a focus on the LV-design application. The model hypotheses are verified and the model parameters are empirically tuned. For selected panels on the vehicle, BFF coherence factors are derived based on the Corcos model and the associated panel BFFs are compared to uPSP data. It is shown that the modeled BFFs are in agreement with direct integration of uPSP data, except for regions where pressure fluctuations are spatially nonuniform. In those regions, the Corcos-based BFFs exhibit inherent limitations of BFF estimation methods that rely on discrete pressure measurements.

buffet↗

Evaluation of Preliminary Buffet Forcing Function Development based on Wind-Tunnel Tests of Geometrically-similar Models

The current state-of-the-art for launch vehicle buffet load estimation involves wind tunnel testing of highly instrumented rigid models and data processing of experimental unsteady pressure measurements to estimate the fluctuating aerodynamic loads. The model and instrumentation requirements to accomplish this type of testing are significant. The rigid model, in particular, must be built with relatively high geometric fidelity to approximate similar aerodynamic phenomena that will occur during the full-scale vehicle flight. Due to this requirement, several iterative wind tunnel tests are often required during the design maturation of the vehicle. For preliminary buffet load estimation, however, data from previous launch vehicle wind tunnel tests of geometrically similar models can be used to provide initial buffet environment estimates before a high geometric fidelity wind tunnel test has been conducted. In this paper, two components of this preliminary buffet estimation process are described: (1) assembly of buffet forcing functions from multiple data sources and (2) scaling of these buffet forcing functions to geometrically-similar components of a target vehicle configuration. The impacts of these estimation methods on the full-vehicle buffet forcing functions, including their longitudinal distribution along the vehicle, frequency-content at individual stations, and station-to-station coherence, are investigated. Considerations for using these methods to extrapolate wind tunnel data to geometrically similar models are also discussed.

Patrick S Heaney↗

Coherence Analysis of the Space Launch System using Unsteady Pressure Sensitive Paint

Transonic buffet during atmospheric ascent is a major source of unsteady loading on launch vehicles that, in the past, has led to structural failures. Thus, determining accurate buffet forcing functions (BFFs) to properly predict the vehicle response to buffet is of vital importance in launch vehicle design. The state of the art for obtaining the BFFs relies on wind-tunnel tests where the fluctuating pressures are measured by pressure transducers (PTs) at sparse locations on a rigid, geometrically-scaled buffet model. To compute the BFFs, the outer mold line (OML) of the vehicle is mapped onto contiguous panels centered at the location of the PTs and the measured fluctuating pressures are integrated over the panels’ areas. To mitigate conservatism due to the assumption that the measured buffet pressures act in phase across each panel, coherence factors are applied, effectively reducing the integration areas and, therefore, the buffet forces. For coherence factors to provide the proper level of BFF attenuation, accurate knowledge of the spatial and temporal correlation of the buffet pressures is paramount. Unfortunately, even with hundreds of unsteady pressure transducers instrumenting the models, compromises must be made on the spatial resolution of the PTs. Typically, the PT layout aims at resolving the pressure correlation along the longitudinal axis of the vehicle, assuming that coherent structures propagate mostly in the longitudinal direction. As a result, the distribution of azimuthal/cross-stream correlation is not well known and its impact on the estimated BFFs is often neglected. To fill this and other knowledge gaps, extremely high spatial-resolution uPSP data were collected for three different production-design configurations of the Space Launch System in the NASA Ames Research Center Unitary Plan Wind Tunnel 11-Foot Transonic Wind Tunnel. Specifically, two cargo configurations, the Block 1 and Block 1B, and one crew configuration, the Block 1B crew, were surveyed at resolutions ranging from 600,000 to over 1 million uPSP measurement locations. To shed light on the temporal and spectral behavior of are presented. Several OML regions and flow features of interest are investigated, from theexpansion/shock on the Orion Multi-Purpose Crew Vehicle, to the terminal shock environment on the core stage, and the Strouhal shedding behind the boosters forward attach. The sensitivity of these environments to the vehicle attitude is examined. Furthermore, for selected panels,coherence factors that accurately capture the azimuthal coherence distribution of the buffetpressures are derived and their impact on the estimated BFFs is discussed. Finally, distributions of the local convection velocity and cross spectrum phase are presented.

buffet↗

Analysis of Transonic Unsteady Aerodynamic Environments using Unsteady Pressure Sensitive Paint for the Space Launch System Block 1 Cargo Launch Vehicle

Predicting launch vehicle unsteady aerodynamic loads due to buffet remains a significant challenge. Current practices for modeling buffet environments involve the development of buffet forcing functions using unsteady pressure measurements acquired during wind-tunnel tests. These practices often result in significant uncertainty in buffet environments for coupled loads analyses due to the complex spatio-temporal nature of the unsteady pressure field and challenge of its estimation using discrete sensors. Unsteady pressure sensitive paint, on the other hand, can provide unsteady pressure data at a comparatively high-spatial-density and may overcome the challenge of unsteady pressure field estimation with discrete sensors and lead to improvements in the development of buffet forcing functions. In this paper, comparisons of the fluctuating pressure field are made for the Space Launch System Block 1 cargo launch vehicle measured using unsteady pressure sensitive paint and pressure transducers.

buffet↗

Development of Buffet Forcing Functions Using Frequency-Dependent Coherence Factors

The current accepted approach to modeling launch vehicle transonic buffet environments is to acquire time-correlated unsteady pressure measurements at discrete locations on a model-scale wind-tunnel model and use these measurements to develop buffet forcing functions (BFFs). Part of the BFF development process is the application of coherence factors to account for the discrete nature of the pressure measurement used in the development of the BFFs. Presently, the Space Launch System (SLS) program divides the launch vehicle into distinct aerodynamic regions, within which, the coherence lengths are assumed to be constant. The coherence factors are computed by averaging the coherence function between sensors within the region over a specified frequency range. The present work validates and examines the impact of two proposed changes to the development of longitudinal coherence factors used in the development of launch vehicle BFFs. One change is to employ frequency-dependent coherence factors instead of coherence factors based on the mean of the coherence function. The second proposed change replaces the aerodynamic regions with a moving-segment approach that varies the calculated coherence lengths as a function of longitudinal location of the transducers. The impact of these approaches is examined using data from two rigid buffet model wind-tunnel tests: (1) a notional launch vehicle geometry through the comparison of discrete measurement-based BFFs to loads developed by continuous integration of unsteady pressure sensitive paint data and (2) the SLS Block 1 Cargo vehicle configuration for which BFFs have been previously developed using less-mature methods. The trends from this examination of updated coherence factor approaches ultimately result in more intuitive results than currently-accepted coherence methods.

buffet↗

Comparison of Wind-Tunnel and Flight Unsteady Pressure Environments for the Ares I-X Flight Test Vehicle

Wind-tunnel testing of highly-instrumented rigid models is the current standard for the estimation of unsteady environments acting on a launch vehicle during ascent. However, uncertainties are present in how well these constant condition wind-tunnel data model the unsteady environments for a full-scale launch vehicle during accelerating flight. The Ares I-X wind-tunnel and flight tests provided rich data sets for comparative analysis that have been used to develop answers to the question of tunnel-to-flight validity and uncertainty. In this paper, additional comparisons are presented for the unsteady pressure data acquired during wind-tunnel and flight testing to describe current methods that are used to evaluate preflight predictions. Utilizing these methods to generate ensembles of flight-comparable realizations from wind-tunnel data is shown to yield fluctuating magnitudes and spectra that compare well with flight data for the Ares I-X flight test vehicle throughout the transonic flight regime.

buffet↗

Comparison of Wind-Tunnel and Flight Unsteady Pressure Environments for the Ares I-X Flight Test Vehicle

Wind-tunnel testing of highly-instrumented rigid models is the current standard for the estimation of unsteady environments acting on a launch vehicle during ascent. However, uncertainties are present in how well these constant condition wind-tunnel data model the unsteady environments for a full-scale launch vehicle during accelerating flight. The Ares I-X wind-tunnel and flight tests provided rich data sets for comparative analysis that have been used to develop answers to the question of tunnel-to-flight validity and uncertainty. In this paper, additional comparisons are presented for the unsteady pressure data acquired during wind-tunnel and flight testing to describe current methods that are used to evaluate preflight predictions. Utilizing these methods to generate ensembles of flight-comparable realizations from wind-tunnel data is shown to yield fluctuating magnitudes and spectra that compare well with flight data for the Ares I-X flight test vehicle throughout the transonic flight regime.

buffet↗

Validation of the Corcos Model for the Space Launch System using Unsteady Pressure Sensitive Paint

During atmospheric ascent launch vehicles (LVs) experience large dynamic loads at transonic conditions where aerodynamic buffet is most critical. To estimate buffet loads, coupled loads analyses typically utilize suitable forcing functions, called buffet forcing functions (BFFs). One of the key buffet environment contributors is the turbulent boundary layer (TBL) on the LV outer skin. The TBL-induced fluctuating pressures can be estimated using the widely-accepted Corcos model. In the context of transonic buffet, the performance of this model is not well established, partly because of lack of data. To fill this gap, NASA recently acquired extremely high-spatial-density data for the Space Launch System (SLS) vehicle, using the unsteady pressure sensitive paint (uPSP) optical measurement technique. A methodology is developed for validation of the Corcos model using these unique data, with a focus on the LV-design application. The model hypotheses are verified and the model parameters are empirically tuned. For selected panels on the vehicle, BFF coherence factors are derived based on the Corcos model and the associated panel BFFs are compared to uPSP data. It is shown that the modeled BFFs are in agreement with direct integration of uPSP data, except for regions where pressure fluctuations are spatially nonuniform. In those regions, the Corcos-based BFFs exhibit inherent limitations of BFF estimation methods that rely on discrete pressure measurements.

buffet↗

Comparison of Corcos-based and experimentally-derived coherence factors for BFFs estimation

In this paper, high-spatial-resolution unsteady Pressure Sensitive Paint (uPSP) data are utilized to compare two methods for panel Buffet Forcing Functions (BFF) estimation for the Space Launch System (SLS). Such methods are based on discrete pressure measurements within a panel but employ coherence factors to account for partially-correlated fluctuating pressures across the whole panel. In one method, coherence factors are derived based on the Corcos model, whereas the second method utilizes experimentally-derived coherence factors. To simulate discrete measurements using uPSP data, suitable subsets of the data are extracted. When full uPSP resolution is retained, uPSP data provide a benchmark to assess discrete-measurements-based methods. The analysis focuses on the peak SLS buffet environment located downstream of the Forward Attachment Hardware (FAH) between the core stage and solid rocket boosters. Trends of Corcos-based and experimentally-derived coherence factors are in reasonable agreement with the benchmark. However, at certain frequencies, experimentally-derived coherence factors are sensitive to the separation distance between pressure measurements utilized to compute coherence lengths. Such sensitivity originates from deviation of the experimental-based coherence function from an exponential decay assumption. On the other hand, the present implementation of the Corcos model fails to capture certain non-turbulent boundary layer related environments, such as a subharmonic of FAH vortex-shedding. For all methods presented in this paper, at near transonic conditions, increased pressure coherence and spatial nonuniformity lead to BFF overestimation and sensitivity to the pressure measurement location within the panel.

transonic buffet↗

Parametric Study of the Forward Attachment Geometry for the Space Launch System Next Generation Booster

Launch vehicle transonic buffet environments can generate large dynamic structural loads and vibratory responses. For the Space Launch System (SLS) vehicle, the highest transonic buffet environments have been observed in the multibody region between the core and solid rocket boosters, particularly downstream of the booster forward attachment. The buffet environment is particularly sensitive to the outer mold line (OML) of the forward attachment, and even relatively minor geometry changes can have large impacts on buffet and other aerodynamic environments. The SLS program is redesigning the booster for the Block 2 vehicle to support updated mission goals. This redesign necessitated changes in the forward attachment geometry, which raised concerns about the buffet and vibroacoustic environments. A preliminary study was conducted that developed multiple forward attachment geometries that satisfied the programmatic requirements, but the aerodynamic environment impacts were unclear. In March 2022, a wind-tunnel test was conducted at the NASA Ames 11- by 11-foot Transonic Wind Tunnel to study these environments generated from each of the configurations in order to select the most viable candidate. This paper will discuss this test campaign, the results from the parametric study, as well as general observations regarding OML features that impact the buffet environment. Buffet environments will be presented and compared for each configuration and comparisons presented where applicable.

buffet↗

Comparisons of Artemis I and Wind Tunnel Buffet Environments and Induced Structural Responses

This paper presents comparisons of buffet forcing functions (BFFs) and associated structural responses for the Space Launch System from two data sources: 1) Artemis I (AR01) Developmental Flight Instrumentation (DFI) and 2) transonic wind-tunnel (WT) tests. Failures of DFI sensors prevented the development of a complete set of flight-based BFFs, where each BFF is based on azimuthal integration over 360-degrees of unsteady pressures acquired by sensor rings placed at many longitudinal stations along the vehicle. Instead, a set of equivalent flight- and WT-based BFFs was developed based on functional DFI and WT sensors that share the same locations. Root-mean-square (rms) levels of equivalent BFFs from flight and WT data are in-family for most of the cardinal Mach numbers. However, at stations downstream of the booster forward attachment (FA) protuberance, the rms of flight-based BFFs exceed their WT counterparts. Strikingly, vortex-shedding off the FA protuberance occurs at lower frequencies during flight than in WT experiments. This frequency shift propagates onto the spectrum of flight-measured vs. WT-based structural responses. Aside from vortex-shedding frequency mismatch, a generally good agreement between AR01 and WT-based responses is an indirect indication that WT buffet environments are fairly well representative of their AR01 counterpart.

transonic buffet↗

Artemis I Flight Instrumentation Data Quality Assessment and Processing

This paper is in support of the SciTech 2024 Space Launch System Aerosciences Special Sessions being organized by Brent Pomeroy and Jeremy Pinier. On November 16th, 2022, NASA launched the inaugural test flight of the Space Launch System (SLS) carrying the Orion capsule into a high orbit far beyond the Moon. The launch vehicle was instrumented with over three thousand flight instrumentation sensors, which monitored aerodynamic, acoustic, structural, and thermal environments. These data are intended to validate experimental and numerical tools used to predict the design environments which the vehicle experiences during launch and ascent. Prior to launch, tests were performed at the SLS Systems Integration Laboratory (SIL) using flight-like avionics and on the integrated flight hardware of Artemis I at the Vehicle Assembly Building (VAB). The purpose of these tests was to characterize the data acquisition units (DAUs) used to record and telemeter flight data to ground stations in order to assure that flight test objectives can be achieved and to quantify the expected quality of the flight data. In addition, pre-flight assessment and development of tools and methods used to process and disseminate flight data at the Huntsville Operations Support Center (HOSC) were conducted and adjustments made with respect to DAU time-synchronization prior to and after the flight. This paper summarizes these tests and some aspects of the post processing of data are discussed.

Developmental Flight Instrumentation↗

A Database of CFD-Based Buffet Forcing Functions for Artemis I Structural Response Evaluation

Time-accurate FUN3D simulations are utilized to estimate buffet-induced unsteady forces experienced by the Space Launch System during the Artemis I (AR01) flight. A set of FUN3D simulations was developed that employed time-accurate mesh translations to simulate the changing velocity and attitude based on the AR01 best estimated trajectory. In these simulations, referred to as accelerating-flow simulations, the freestream Mach number increased from 0.80 to 1.92. Additional time-accurate simulations were obtained at constant freestream Mach number equal to 0.95, 1.18, 1.70, thereby simulating stationary conditions experienced by the flow in a wind-tunnel. On the grounds of favorable comparisons between simulated and flight-measured environments, the FUN3D-based surface pressures were utilized to develop a buffet forcing function (BFF) database. This BFF database was analyzed to characterize the spatial distribution and frequency content of the buffet forces during transonic and supersonic portions of the AR01 flight. The region of interest is located downstream of the forward attachment (FA) hardware between the core stage and the solid rocket boosters where vortex shedding off the FA protuberance produces significant unsteadiness. The analysis reveals that, at transonic and supersonic conditions, buffet forces that are based on constant freestream Mach number data are a good approximation of those based on accelerating-flow simulations.

FUN3D↗