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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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313 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Modeling Unsteady Cavitation and Dynamic Loads in Turbopumps

A computational fluid dynamics (CFD) model that includes representations of effects of unsteady cavitation and associated dynamic loads has been developed to increase the accuracy of simulations of the performances of turbopumps. Although the model was originally intended to serve as a means of analyzing preliminary designs of turbopumps that supply cryogenic propellant liquids to rocket engines, the model could also be applied to turbopumping of other liquids: this can be considered to have been already demonstrated, in that the validation of the model was performed by comparing results of simulations performed by use of the model with results of sub-scale experiments in water. The need for this or a similar model arises as follows: Cavitation instabilities in a turbopump are generated as inlet pressure drops and vapor cavities grow on inducer blades, eventually becoming unsteady. The unsteady vapor cavities lead to rotation cavitation, in which the cavities detach from the blades and become part of a fluid mass that rotates relative to the inducer, thereby generating a fluctuating load. Other instabilities (e.g., surge instabilities) can couple with cavitation instabilities, thereby compounding the deleterious effects of unsteadiness on other components of the fluid-handling system of which the turbopump is a part and thereby, further, adversely affecting the mechanical integrity and safety of the system. Therefore, an ability to predict cavitation- instability-induced dynamic pressure loads on the blades, the shaft, and other pump parts would be valuable in helping to quantify safe margins of inducer operation and in contributing to understanding of design compromises. Prior CFD models do not afford this ability. Heretofore, the primary parameter used in quantifying cavitation performance of a turbopump inducer has been the critical suction specific speed at which head breakdown occurs. This parameter is a mean quantity calculated on the basis of assumed steady-state operation of the inducer; it does not account for dynamic pressure loads associated with unsteady flow caused by instabilities. Because cavitation instabilities occur well before mean breakdown in inducers, engineers have, until now, found it necessary to use conservative factors of safety when analyzing the results of numerical simulations of flows in turbopumps.

Hosangadi, Ashvin↗
NASA NTRS2007

Space Propulsion Design and Analysis

This software provides an improved methodology for predicting launcher base pressure and heat loads for RSRM (Reusable Solid Rocket Motor) launchers by accounting for complex anisotropic stress/strains and variable turbulent Prandtl and Schmidt numbers. A "building block" approach to turbulence model development, and validation has been applied for improved missile/launcher base region analysis. Modifications to existing kappa - epsilon turbulence models and application of scalar variance models are incorporated into a RANS-based method for aeropropulsive flow modeling, directly related to base flow methodology. (RANS stands for Reynolds-averaged Navier-Stokes.) The models are applied in a RANS solver framework and can improve analysis of other complex flow fields. The enhanced models provide a more accurate predictive capability for improving the design and analysis of RSRM launcher configuration. The kappa - epsilon model enhancements have been shown to improve the capability for predicting turbulence effects in base blow environments. The scalar variance models have been assessed over a wide range of flow configurations to improve prediction of turbulent scalar mixing.

Sinha, Neeraj↗
NASA NTRS2007

Analysis of Complex Valve and Feed Systems

A numerical framework for analysis of complex valve systems supports testing of propulsive systems by simulating key valve and control system components in the test loop. In particular, it is designed to enhance the analysis capability in terms of identifying system transients and quantifying the valve response to these transients. This system has analysis capability for simulating valve motion in complex systems operating in diverse flow regimes ranging from compressible gases to cryogenic liquids. A key feature is the hybrid, unstructured framework with sub-models for grid movement and phase change including cryogenic cavitations. The multi-element unstructured framework offers improved predictions of valve performance characteristics under steady conditions for structurally complex valves such as pressure regulator valve. Unsteady simulations of valve motion using this computational approach have been carried out for various valves in operation at Stennis Space Center such as the split-body valve and the 10-in. (approx.25.4-cm) LOX (liquid oxygen) valve and the 4-in. (approx.10 cm) Y-pattern valve (liquid nitrogen). Such simulations make use of variable grid topologies, thereby permitting solution accuracy and resolving important flow physics in the seat region of the moving valve. An advantage to this software includes possible reduction in testing costs incurred due to disruptions relating to unexpected flow transients or functioning of valve/flow control systems. Prediction of the flow anomalies leading to system vibrations, flow resonance, and valve stall can help in valve scheduling and significantly reduce the need for activation tests. This framework has been evaluated for its ability to predict performance metrics like flow coefficient for cavitating venturis and valve coefficient curves, and could be a valuable tool in predicting and understanding anomalous behavior of system components at rocket propulsion testing and design sites.

Ahuja, Vineet↗
NASA NTRS2005

Computing Thermal Effects of Cavitation in Cryogenic Liquids

A computer program implements a numerical model of thermal effects of cavitation in cryogenic fluids. The model and program were developed for use in designing and predicting the performances of turbopumps for cryogenic fluids. Prior numerical models used for this purpose do not account for either the variability of properties of cryogenic fluids or the thermal effects (especially, evaporative cooling) involved in cavitation. It is important to account for both because in a cryogenic fluid, the thermal effects of cavitation are substantial, and the cavitation characteristics are altered by coupling between the variable fluid properties and the phase changes involved in cavitation. The present model accounts for both thermal effects and variability of properties by incorporating a generalized representation of the properties of cryogenic fluids into a generalized compressible-fluid formulation for a cavitating pump. The model has been extensively validated for liquid nitrogen and liquid hydrogen. Using the available data on the properties of these fluids, the model has been shown to predict accurate temperature-depression values.

Hosangadi, Ashvin↗
NASA NTRS2011

More-Accurate Model of Flows in Rocket Injectors

An improved computational model for simulating flows in liquid-propellant injectors in rocket engines has been developed. Models like this one are needed for predicting fluxes of heat in, and performances of, the engines. An important part of predicting performance is predicting fluctuations of temperature, fluctuations of concentrations of chemical species, and effects of turbulence on diffusion of heat and chemical species. Customarily, diffusion effects are represented by parameters known in the art as the Prandtl and Schmidt numbers. Prior formulations include ad hoc assumptions of constant values of these parameters, but these assumptions and, hence, the formulations, are inaccurate for complex flows. In the improved model, these parameters are neither constant nor specified in advance: instead, they are variables obtained as part of the solution. Consequently, this model represents the effects of turbulence on diffusion of heat and chemical species more accurately than prior formulations do, and may enable more-accurate prediction of mixing and flows of heat in rocket-engine combustion chambers. The model has been implemented within CRUNCH CFD, a proprietary computational fluid dynamics (CFD) computer program, and has been tested within that program. The model could also be implemented within other CFD programs.

Hosangadi, Ashvin↗
DOE OSTI2026

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗