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Haftka, Raphael

Publications and source records attributed to Haftka, Raphael.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Calibration of reactive burn and Jones-Wilkins-Lee parameters for simulations of a detonation-driven flow experiment with uncertainty quantification

Here, uncertainties in the explosive-specific parameters of the Jones-Wilkins-Lee (JWL) equation of state (EOS) are carefully considered in hydrodynamic simulations of an explosive experiment to minimize the error in the flow prediction. Experimental data of the leading shock position in the transverse direction over time serves as the prediction metric for quantifying simulation prediction error. The uncertainty quantification technique, global sensitivity analysis, is utilized to determine the JWL parameters to which the transverse shock propagation is most sensitive. A polynomial response surface (PRS) is constructed in the space of the most influential JWL parameters, and the point of minimum error between the experimental data and the PRS yields calibrated JWL parameters for the experimental flow. The simulation results following the parameter calibration show good agreement with the experimental data. It was found that two significant parameters, the heat release per unit mass of reactant Q and JWL model exponent R 1 are strongly related, which makes it difficult to identify accurate values.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluation of Constitutive Relationship for Silicon Carbide Cladding Material

A curved representative volume element (RVE) was employed to analyze SiC/SiC tubular composites. By imposing periodicity on the tubular model through appropriate boundary conditions, the elastic constants of the composite was evaluated. It was found that as the radius of the tubular composite increases, the more the flat RVE model and curved RVE model disagreed.

Thandaga Nagaraju, Hemanth↗
DOE OSTI2020

Evaluation of Extensional Stiffness Matrix [A]. of Braided Composite Tubes

A novel method for analyzing tubular textile composites was developed by obtaining a curved repeatable unit cell (RUC) from the transformation of a flat RUC. Periodic boundary conditions were imposed on the tubular RUC. The stiffness matrix of the composite was then evaluated using transversely isotropic yarn and isotropic matrix. It was found that the stiffness coefficients A11, A12 of the composite are more sensitive to a change in the radius of the composite compared to A22 and A66.

Thandaga Nagaraju, Hematnh↗
DOE OSTI2020

Stress Analysis and Failure Behavior of SiC/SiC Textile Composite Tubes

A novel method for analyzing tubular textile composites was developed by obtaining a curved representative volume element (RVE) from the transformation of a flat RVE. Periodic boundary conditions were imposed on the curved RVE. The stresses in a transeversely isotropic yarn were compared between the two RVEs. It was found that there was a discrepancy between the two models.

Thandaga Nagaraju, Hemanth↗
DOE OSTI2019

Micromechanics of Composite Tubes by Curved and Flat RVEs

A novel method for analyzing tubular composites was developed by obtaining a curved representative volume element (RVE) from the transformation of a flat RVE. There was found to be a discrepancy in the stress between the two RVE models of approximately 15%. The curved RVE model was validated using an isotropic material.

Thandaga Nagaraju, Hemanth↗
DOE OSTI2019

Efficiently Measuring Heterogneity of SiC/SiC Tubular Composites

It was found that in SiC/SiC tubular composites there is a large variety in the microstructure and geometric parameters. These variations in microstructure parameters can lead to a potential stochastic variation in the overall strength of the composite materials, therefore it is important to quantify this heterogeneity. Fiber diameters, wall thickness, yarn and fiber angle variations and the porosity distribution were quantified.

Nance, James↗
NASA NTRS2001

Design Space Exploration for MDO on a Teraflop Computer

The objective of this work was to develop global optimization techniques suitable for identifying promising regions in high dimensional aircraft configuration design space. Different optimization methods have been examined to evaluation their ability to efficiently search the design space to locate promising regions. Multistart local optimization methods as well as global search techniques were examined and modified to compare their performance on high dimensional design space.

Haftka, Raphael↗
NASA NTRS2000

Neural Network and Response Surface Methodology for Rocket Engine Component Optimization

The goal of this work is to compare the performance of response surface methodology (RSM) and two types of neural networks (NN) to aid preliminary design of two rocket engine components. A data set of 45 training points and 20 test points obtained from a semi-empirical model based on three design variables is used for a shear coaxial injector element. Data for supersonic turbine design is based on six design variables, 76 training, data and 18 test data obtained from simplified aerodynamic analysis. Several RS and NN are first constructed using the training data. The test data are then employed to select the best RS or NN. Quadratic and cubic response surfaces. radial basis neural network (RBNN) and back-propagation neural network (BPNN) are compared. Two-layered RBNN are generated using two different training algorithms, namely solverbe and solverb. A two layered BPNN is generated with Tan-Sigmoid transfer function. Various issues related to the training of the neural networks are addressed including number of neurons, error goals, spread constants and the accuracy of different models in representing the design space. A search for the optimum design is carried out using a standard gradient-based optimization algorithm over the response surfaces represented by the polynomials and trained neural networks. Usually a cubic polynominal performs better than the quadratic polynomial but exceptions have been noticed. Among the NN choices, the RBNN designed using solverb yields more consistent performance for both engine components considered. The training of RBNN is easier as it requires linear regression. This coupled with the consistency in performance promise the possibility of it being used as an optimization strategy for engineering design problems.

Vaidyanathan, Rajkumar↗