Method for Rapid Graphical Evaluation of Cooled or Uncooled Turbojet and Turboprop Engine or Component Performance (effects of Variable Specific Heat Included)
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This report provides an assessment of current turbulent flow calculation methods for hypersonic propulsion flowpaths, particularly the scramjet engine. Emphasis is placed on Reynolds-averaged Navier-Stokes (RANS) methods, but some discussion of newer meth- ods such as Large Eddy Simulation (LES) is also provided. The report is organized by considering technical issues throughout the scramjet-powered vehicle flowpath including laminar-to-turbulent boundary layer transition, shock wave / turbulent boundary layer interactions, scalar transport modeling (specifically the significance of turbulent Prandtl and Schmidt numbers) and compressible mixing. Unit problems are primarily used to conduct the assessment. In the combustor, results from calculations of a direct connect supersonic combustion experiment are also used to address the effects of turbulence model selection and in particular settings for the turbulent Prandtl and Schmidt numbers. It is concluded that RANS turbulence modeling shortfalls are still a major limitation to the accuracy of hypersonic propulsion simulations, whether considering individual components or an overall system. Newer methods such as LES-based techniques may be promising, but are not yet at a maturity to be used routinely by the hypersonic propulsion community. The need for fundamental experiments to provide data for turbulence model development and validation is discussed.
Free atom fractions (Beta) of nine elements are calculated in the exhaust plume of CH4- oxygen and RP-1-oxygen fueled rocket engines using free energy minimization method. The Chemical Equilibrium and Applications (CEA) computer program developed by the Glenn Research Center, NASA is used for this purpose. Data on variation of Beta in both fuels as a function of temperature (1600 K - 3100 K) and oxygen to fuel ratios (1.75 to 2.25 by weight) is presented in both tabular and graphical forms. Recommendation is made for the Beta value for a tenth element, Palladium. The CEA computer code was also run to compare with experimentally determined Beta values reported in literature for some of these elements. A reasonable agreement, within a factor of three, between the calculated and reported values is observed. Values reported in this work will be used as a first approximation for pilot rocket engine testing studies at the Stennis Space Center for at least six elements Al, Ca, Cr, Cu, Fe and Ni - until experimental values are generated. The current estimates will be improved when more complete thermodynamic data on the remaining four elements Ag, Co, Mn and Pd are added to the database. A critique of the CEA code is also included.
The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.
Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data are collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. In this study, we introduce the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.
Efforts in integrated circuit (IC) packaging technologies have recently been focused on management of increasing heat density associated with high frequency and high density circuit designs. While current flip-chip package designs can accommodate relatively high amounts of heat density, new materials need to be developed to manage thermal effects of next-generation integrated circuits. Multiwall carbon nanotubes (MWNT) have been shown to significantly enhance thermal conduction in the axial direction and thus can be considered to be a candidate for future thermal interface materials by facilitating efficient thermal transport. This work focuses on fabrication and characterization of a robust MWNT-copper composite material as an element in IC package designs. We show that using vertically aligned MWNT arrays reduces interfacial thermal resistance by increasing conduction surface area, and furthermore, the embedded copper acts as a lateral heat spreader to efficiently disperse heat, a necessary function for packaging materials. In addition, we demonstrate reusability of the material, and the absence of residue on the contacting material, both novel features of the MWNT-copper composite that are not found in most state-of-the-art thermal interface materials. Electrochemical methods such as metal deposition and etch are discussed for the creation of the MWNT-Cu composite, detailing issues and observations with using such methods. We show that precise engineering of the composite surface affects the ability of this material to act as an efficient thermal interface material. A thermal contact resistance measurement has been designed to obtain a value of thermal contact resistance for a variety of different thermal contact materials.
Experimentation in software engineering is important but difficult. One reason it is so difficult is that there are a large number of context variables, and so creating a cohesive understanding of experimental results requires a mechanism for motivating studies and integrating results. This paper argues for the necessity of a framework for organizing sets of related studies. With such a framework, experiments can be viewed as part of common families of studies, rather than being isolated events. Common families of studies can contribute to important and relevant hypotheses that may not be suggested by individual experiments. A framework also facilitates building knowledge in an incremental manner through the replication of experiments within families of studies. Building knowledge in this way requires a community of researchers that can replicate studies, vary context variables, and build abstract models that represent the common observations about the discipline. This paper also presents guidelines for lab packages, meant to encourage and support replications, that encapsulate materials, methods, and experiences concerning software engineering experiments.
Recent experience using ANOPP to predict turbofan engine flyover noise suggests that it over-predicts overall EPNL by a significant amount. An improvement in this prediction method is desired for system optimization and assessment studies of advanced UHB engines. An assessment of the ANOPP fan inlet, fan exhaust, jet, combustor, and turbine noise prediction methods is made using static engine component noise data from the CF6-8OC2, E(3), and QCSEE turbofan engines. It is shown that the ANOPP prediction results are generally higher than the measured GE data, and that the inlet noise prediction method (Heidmann method) is the most significant source of this overprediction. Fan noise spectral comparisons show that improvements to the fan tone, broadband, and combination tone noise models are required to yield results that more closely simulate the GE data. Suggested changes that yield improved fan noise predictions but preserve the Heidmann model structure are identified and described. These changes are based on the sets of engine data mentioned, as well as some CFM56 engine data that was used to expand the combination tone noise database. It should be noted that the recommended changes are based on an analysis of engines that are limited to single stage fans with design tip relative Mach numbers greater than one.
Bayesian optimization (BO) is an effective surrogate-based method that has been widely used to optimize simulation-based applications. While the traditional Bayesian optimization approach only applies to single-fidelity models, many realistic applications provide multiple levels of fidelity with various levels of computational complexity and predictive capability. In this work, we propose a multi-fidelity Bayesian optimization method for design applications with both known and unknown constraints. The proposed framework, called sMF-BO-2CoGP, is built on a multi-level CoKriging method to predict the objective function. An external binary classifier, which we approximate using a separate CoKriging model, is used to distinguish between feasible and infeasible regions. Finally, the sMF-BO-2CoGP method is demonstrated using a series of analytical examples and a flip-chip application for design optimization to minimize the deformation due to warping under thermal loading conditions.
The NASA method (1976) for predicting the effects of flight on jet mixing noise was improved. The earlier method agreed reasonably well with experimental flight data for jet velocities up to about 520 m/sec (approximately 1700 ft/sec). The poorer agreement at high jet velocities appeared to be due primarily to the manner in which supersonic convection effects were formulated. The purely empirical supersonic convection formulation of the earlier method was replaced by one based on theoretical considerations. Other improvements of an empirical nature included were based on model-jet/free-jet simulated flight tests. The revised prediction method is presented and compared with experimental data obtained from the Bertin Aerotrain with a J85 engine, the DC-10 airplane with JT9D engines, and the DC-9 airplane with refanned JT8D engines. It is shown that the new method agrees better with the data base than a recently proposed SAE method.
Various parts of aircraft propulsion engines that are in contact with hot gases often require cooling. Transpiration and film cooling, new methods that supposedly utilize cooling air more effectively than conventional convection cooling, have already been proposed. This report presents material necessary for a comparison of the cooling requirements of these three methods. Correlations that are regarded by the authors as the most reliable today are employed in evaluating each of the cooling processes. Calculations for the special case in which the gas velocity is constant along the cooled wall (flat plate) are presented. The calculations reveal that a comparison of the three cooling processes can be made on quite a general basis. The superiority of transpiration cooling is clearly shown for both laminar and turbulent flow. This superiority is reduced when the effects of radiation are included; for gas-turbine blades, however, there is evidence indicating that radiation may be neglected.
The manner in which the jet engine may start a crash fire was explored in test-stand and full-scale crash studies. On the basis of these studies, a method was devised for inserting and cooling the engine parts that may serve as ignition sources in crash. This method was tried successfully in full-scale crashes of jet-powered airplanes carrying engines in pod nacelles and engines buried within the airplane structure.
We report the multiscale complexity of modern problems in computational science and engineering can prohibit the use of traditional numerical methods in multi-dimensional simulations. Therefore, novel algorithms are required in these situations to solve partial differential equations (PDEs) with features evolving on a wide range of spatial and temporal scales. To meet these challenges, we present a multiresolution wavelet algorithm to solve PDEs with significant data compression and explicit error control. We discretize in space by projecting fields and spatial derivative operators onto wavelet basis functions. We provide error estimates for the wavelet representation of fields and their derivatives. Then, our estimates are used to construct a sparse multiresolution discretization which guarantees the prescribed accuracy. Additionally, we embed a predictor-corrector procedure within the temporal integration to dynamically adapt the computational grid and maintain the accuracy of the solution of the PDE as it evolves. We present examples to highlight the accuracy and adaptivity of our approach.
Method and instruments for measuring seismic energy propagation, velocity, attenuation, frequency spectra, and shear wave velocity in lunar surface rock
Inexpensive cut-and-paste method allows construction of complex three-dimensional models in less than an hour. Models are constructed from film or paper copies made on office copier.
A new method was developed for the calculation of optimum constant admittance solutions for the minimization of the sound radiated from an arbitrary axisymmetric body. This method utilizes both the integral equation technique used in the calculation of the optimum non-constant admittance liners and the independent solution generated as a by product of these calculations. The results generated by both these methods are presented for three duct geometries: (1) a straight duct; (2) the QCSEE inlet; and (3) the QCSEE inlet less its centerbody.
An account is given of methods for the estimation of a wing-body-tail missile configuration's aerodynamic performance by means of the 'component buildup' method, in which the overall aerodynamic loads for an airframe are built up from the assumed characteristics of isolated airframe components and then subjected to additional loads generated by component interference. Attention is given to the cases of missile airframes in steady flow at arbitrary angles of attack and bank; the unifying thread of the present treatment is slender body theory, together with its nonlinear extensions through the equivalent angle-of-attack concept. The estimation of the forces and moments acting on each of the fins is emphasized, so that control cross-coupling effects as well as longitudinal and lateral directional effects can be determined.
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. Here, we propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.