Results for “Interface parameters” — Page 22

NASA NTRS1997

Ultrasonic Studies of Composites Undergoing Thermal and Fatigue Loading

New composite materials possess attractive properties for use in advanced aircraft. A necessary requirement for their introduction into aeronautic use is an accurate understanding of their long term aging processes so that proper design criteria can be established. In order to understand those properties, these composites must be exposed to thermal and load cycles that are characteristic of flight conditions. Additionally, airline companies will require nondestructive evaluation (NDE) methods that can be used in the field to assess the condition of these new materials as they age. As part of an effort to obtain the required information about new composites for aviation use, we are performing ultrasonic measurements both in the NDE laboratory and in the materials testing laboratory at NASA. The materials testing laboratory is equipped with environmental chambers mounted on load frames so that composite samples can be exposed to thermal and loading cycles representative of flight protocols. Applying both temperature and load simultaneously will help to highlight temperature and load interactions during the aging of these composite materials. This study reports on our initial ultrasonic attenuation results from thermoset and thermoplastic composite samples. Ultrasonic attenuation measurements have been used reliably to assess the effects of material degradation. For example, recently, researchers have shown that by using frequencies of ultrasound on the order of 24 MHz, they could obtain adequate contrast in the evaluation of thermal degradation in these composites. This paper will present data that shows results at a lower frequency range. In addition, we report results on the frequency dependence of attenuation as the slope of attenuation with respect to frequency, beta = delta alpha (f) / delta f. The slope of attenuation is an attractive parameter since it is quantitative, yet does not require interface corrections like conventional quantitative attenuation measurements. This is a consequence of the assumption that interface correction terms are frequently independent. Uncertainty in those corrections terms compromises the value of conventional quantitative attenuation data.

Madaras, Eric I.↗

NASA NTRS2011

STRS SpaceWire FPGA Module

An FPGA module leverages the previous work from Goddard Space Flight Center (GSFC) relating to NASA s Space Telecommunications Radio System (STRS) project. The STRS SpaceWire FPGA Module is written in the Verilog Register Transfer Level (RTL) language, and it encapsulates an unmodified GSFC core (which is written in VHDL). The module has the necessary inputs/outputs (I/Os) and parameters to integrate seamlessly with the SPARC I/O FPGA Interface module (also developed for the STRS operating environment, OE). Software running on the SPARC processor can access the configuration and status registers within the SpaceWire module. This allows software to control and monitor the SpaceWire functions, but it is also used to give software direct access to what is transmitted and received through the link. SpaceWire data characters can be sent/received through the software interface, as well as through the dedicated interface on the GSFC core. Similarly, SpaceWire time codes can be sent/received through the software interface or through a dedicated interface on the core. This innovation is designed for plug-and-play integration in the STRS OE. The SpaceWire module simplifies the interfaces to the GSFC core, and synchronizes all I/O to a single clock. An interrupt output (with optional masking) identifies time-sensitive events within the module. Test modes were added to allow internal loopback of the SpaceWire link and internal loopback of the client-side data interface.

Lux, James P.↗

NASA NTRS2013

Low Power, Low Mass, Modular, Multi-band Software-defined Radios

Methods and systems to implement and operate software-defined radios (SDRs). An SDR may be configured to perform a combination of fractional and integer frequency synthesis and direct digital synthesis under control of a digital signal processor, which may provide a set of relatively agile, flexible, low-noise, and low spurious, timing and frequency conversion signals, and which may be used to maintain a transmit path coherent with a receive path. Frequency synthesis may include dithering to provide additional precision. The SDR may include task-specific software-configurable systems to perform tasks in accordance with software-defined parameters or personalities. The SDR may include a hardware interface system to control hardware components, and a host interface system to provide an interface to the SDR with respect to a host system. The SDR may be configured for one or more of communications, navigation, radio science, and sensors.

Haskins, Christopher B.↗

NASA NTRS2016

Experimenting Galileo on Board the International Space Station

The SCaN Testbed is an advanced integrated communications system and laboratory facility installed on the International Space Station (ISS) in 2012. The testbed incorporates a set of new generation of Software Defined Radio (SDR) technologies intended to allow researchers to develop, test, and demonstrate new communications, networking, and navigation capabilities in the actual environment of space. Qascom, in cooperation with ESA and NASA, is designing a Software Defined Radio GalileoGPS Receiver capable to provide accurate positioning and timing to be installed on the ISS SCaN Testbed. The GalileoGPS waveform will be operated in the JPL SDR that is constituted by several hardware components that can be used for experimentations in L-Band and S-Band. The JPL SDR includes an L-Band Dorne Margolin antenna mounted onto a choke ring. The antenna is connected to a radio front end capable to provide one bit samples for the three GNSS frequencies (L1, L2 and L5) at 38 MHz, exploiting the subharmonic sampling. The baseband processing is then performed by an ATMEL AT697 processor (100 MIPS) and two Virtex 2 FPGAs. The JPL SDR supports the STRS (Space Telecommunications Radio System) that provides common waveform software interfaces, methods of instantiation, operation, and testing among different compliant hardware and software products. The standard foresees the development of applications that are modular, portable, reconfigurable, and reusable. The developed waveform uses the STRS infrastructure-provided application program interfaces (APIs) and services to load, verify, execute, change parameters, terminate, or unload an application. The project is divided in three main phases. 1)Design and Development of the GalileoGPS waveform for the SCaN Testbed starting from Qascom existing GNSS SDR receiver. The baseline design is limited to the implementation of the single frequency Galileo and GPS L1E1 receiver even if as part of the activity it will be to assess the feasibility of a dual frequency implementation (L1E1+L5E5a) in the same SDR platform.2)Qualification and test the GalileoGPS waveform using ground systems available at the NASA Glenn Research Center. Experimenters can have access to two SCaN Testbed ground based systems for development and verification: the Experimenter Development System (EDS) that is intended to provide initial opportunity for software testing and basic functional validation and the Ground Integration Unit (GIU) that is a high fidelity version of the SCaN Testbed flight system and is therefore used for more controlled final development testing and verification testing.3)Perform in-orbit validation and experimentation: The experimentation phase will consists on the collection of raw measurements (pseudorange, Carrier phase, CN0) in space, assessment on the quality of the measurements and the receiver performances in terms of signal acquisition, tracking, etc. Finally computation of positioning in space (Position, Velocity and time) and assessment of its performance.(Complete abstract in attached document).

GPS↗

NASA NTRS2016

Experimenting Galileo on Board the International Space Station

The SCaN Testbed is an advanced integrated communications system and laboratory facility installed on the International Space Station (ISS) in 2012. The testbed incorporates a set of new generation of Software Defined Radio (SDR) technologies intended to allow researchers to develop, test, and demonstrate new communications, networking, and navigation capabilities in the actual environment of space. Qascom, in cooperation with ESA and NASA, is designing a Software Defined Radio GalileoGPS Receiver capable to provide accurate positioning and timing to be installed on the ISS SCaN Testbed. The GalileoGPS waveform will be operated in the JPL SDR that is constituted by several hardware components that can be used for experimentations in L-Band and S-Band. The JPL SDR includes an L-Band Dorne Margolin antenna mounted onto a choke ring. The antenna is connected to a radio front end capable to provide one bit samples for the three GNSS frequencies (L1, L2 and L5) at 38 MHz, exploiting the subharmonic sampling. The baseband processing is then performed by an ATMEL AT697 processor (100 MIPS) and two Virtex 2 FPGAs. The JPL SDR supports the STRS (Space Telecommunications Radio System) that provides common waveform software interfaces, methods of instantiation, operation, and testing among different compliant hardware and software products. The standard foresees the development of applications that are modular, portable, reconfigurable, and reusable. The developed waveform uses the STRS infrastructure-provided application program interfaces (APIs) and services to load, verify, execute, change parameters, terminate, or unload an application. The project is divided in three main phases. 1)Design and Development of the GalileoGPS waveform for the SCaN Testbed starting from Qascom existing GNSS SDR receiver. The baseline design is limited to the implementation of the single frequency Galileo and GPS L1E1 receiver even if as part of the activity it will be to assess the feasibility of a dual frequency implementation (L1E1+L5E5a) in the same SDR platform.2)Qualification and test the GalileoGPS waveform using ground systems available at the NASA Glenn Research Center. Experimenters can have access to two SCaN Testbed ground based systems for development and verification: the Experimenter Development System (EDS) that is intended to provide initial opportunity for software testing and basic functional validation and the Ground Integration Unit (GIU) that is a high fidelity version of the SCaN Testbed flight system and is therefore used for more controlled final development testing and verification testing.3)Perform in-orbit validation and experimentation: The experimentation phase will consists on the collection of raw measurements (pseudorange, Carrier phase, CN0) in space, assessment on the quality of the measurements and the receiver performances in terms of signal acquisition, tracking, etc. Finally computation of positioning in space (Position, Velocity and time) and assessment of its performance.(Complete abstract in attached document).

Galileo↗

NASA NTRS2019

CFD Jet Mixing Model Validation Against Zero-Boil-Off Tank (ZBOT) Microgravity Experiment

The Zero-Boil-Off Tank (ZBOT) experiments are a series of small scale two-phase fluids physics experiments conducted aboard the International Space Station (ISS) in 2017. A goal of these experiments is to study various fundamental fluid flow, heat and mass transport, and phase change phenomena that control storage tank pressurization and pressure control in microgravity. In this paper, results of a state-of-the-art two-phase CFD (Computational Fluid Dynamics) storage tank model are presented and validated against jet mixing data provided by microgravity ZBOT experiments. Jet-ullage interaction was studied in the experiment for different jet speeds representing different flow regimes, and tank fill levels. Effects of various physical parameters, such as, jet angle, gravitational acceleration and surface tension value were investigated. Computational parameters, such as turbulence and surface tension models and interface capturing scheme, affecting jet-ullage interaction were also studied. Computational results were compared to experimental results for model validation.

Turbulence↗

NASA NTRS2019

CFD Jet Mixing Model Validation against Zero-Boil-Off Tank (ZBOT) Microgravity Experiment

The Zero-Boil-Off Tank (ZBOT) experiments are a series of small scale two-phase fluids physics experiments conducted aboard the International Space Station (ISS) in 2017. A goal of these experiments is to study various fundamental fluid flow, heat and mass transport, and phase change phenomena that control storage tank pressurization and pressure control in microgravity. In this paper, results of a state-of-the-art two-phase CFD storage tank model are presented and validated against jet mixing data provided by microgravity ZBOT experiments. Jet-ullage interaction was studied in the experiment for different jet speeds representing different flow regimes, and tank fill levels. Effects of various physical parameters, such as, jet angle, gravitational acceleration and surface tension value were investigated. Computational parameters, such as turbulence and surface tension models and interface capturing scheme, affecting jet-ullage interaction were also studied. Computational results were compared to experimental results for model validation.

Mixing↗

NASA NTRSDate not supplied

The Pheno-Evo Model: Evolution of Microbial Phenotypic Diversity in 2D Space

Biologists appreciate microbes for their simplicity and predictability: we assume that a population of genetically identical cells in a uniform environment will all behave the same way. While this assumption is often useful, it is also often wrong. Not only might microbes in a clonal population act differently from one another, the differences may be categorical (growing v. non-growing; toxin-sensitive v. tolerant), and this diversity might be an evolved trait conferring increased fitness on the population. How does such phenotypic heterogeneity evolve? And how does a population find the optimal distribution of phenotypes for a given environment? Efforts at modeling microbial phenotypic heterogeneity often focus on populations with two discrete phenotypic types; phenotypes in continuous distributions remain poorly explored. To address this gap and to explore the role of spatial relationships, we use agent-based modeling to simulate a phenotypically diverse population of microbial cells evolving in the presence of periodic toxic stress. Cells on patches in a 2D grid may degrade toxin, suffer damage from toxin, switch phenotype, and reproduce. An individual's phenotype its toxin degradation rate-- for which there is a tradeoff with reproduction-- and the genotype encodes the distribution of phenotypic values in the population. The rate of toxin diffusion determines how individuals influence their neighbors' environments. We examine the effects of toxin concentration, diffusion rate, and environmental predictability on the survival success of populations with different phenotype distributions, and populations' evolutionary trajectories when phenotype distribution is allowed to evolve. We conduct all simulations on the platform NetLogo, which provides a friendly interface allowing users of any experience level to tweak parameters and run their own simulations. We have also created custom tools in R for analyzing and visualizing the results of multiple runs.

microbial evolution↗

DOE OSTI2020

H2Fills™ [SWR-19-67]

H2FillS: Hydrogen Filling Simulation The Hydrogen Filling Simulation (H2FillS) software is a thermodynamic model designed to track and report on the transient change in hydrogen temperature, pressure, and mass flow when filling a fuel cell electric vehicle (FCEV). H2FillS simulates gas flow from the hydrogen station to the FCEV storage system. Using empirical fueling data sets, the model has been validated over a range of fueling conditions to match common light-duty FCEV fill profiles. Overall, it provides significant benefits to the light-duty fueling market and fill knowledge gaps of the interaction between a hydrogen station and an FCEV. Capabilities Use the comprehensive H2FillS model to: Safely design and operate a hydrogen fueling station Support code refinement with readily available data Develop system and operational improvements to reduce capital or operating costs at hydrogen stations or on-board FCEVs. How It Works H2FillS uses a "drag-and-drop" graphical user interface to simulate station and vehicle systems with preset parameters for common hydrogen station components as default values. When needed, users can define their own parameters to create their own station or vehicle components. Download H2FillS in Two Steps Read the terms of the end user software license agreement. Register to download the software. There are two versions of the model: A full-station model starts the simulation at high-pressure ground storage, runs through a dispenser, and ends at a vehicle storage system. A partial-station model starts at the dispenser breakaway and consists solely of the dispenser components and the vehicle storage system. H2FillS will automatically output fill performance data from the vehicle by tracking pressure and temperature throughout the fill. Users can input their own fill profiles into the model to run a variety of simulations.

Peters, Michael↗

NASA NTRS2009

Configuring a Graphical User Interface for Managing Local HYSPLIT Model Runs Through AWIPS

Responding to incidents involving the release of harmful airborne pollutants is a continual challenge for Weather Forecast Offices in the National Weather Service. When such incidents occur, current protocol recommends forecaster-initiated requests of NOAA's Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model output through the National Centers of Environmental Prediction to obtain critical dispersion guidance. Individual requests are submitted manually through a secured web site, with desired multiple requests submitted in sequence, for the purpose of obtaining useful trajectory and concentration forecasts associated with the significant release of harmful chemical gases, radiation, wildfire smoke, etc., into local the atmosphere. To help manage the local HYSPLIT for both routine and emergency use, a graphical user interface was designed for operational efficiency. The interface allows forecasters to quickly determine the current HYSPLIT configuration for the list of predefined sites (e.g., fixed sites and floating sites), and to make any necessary adjustments to key parameters such as Input Model. Number of Forecast Hours, etc. When using the interface, forecasters will obtain desired output more confidently and without the danger of corrupting essential configuration files.

Wheeler, mark M.↗

DOE OSTI2025

Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota’s iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is presented wherein the combined effects of temperature and loading rate on the predicted response of a braided composite is investigated.

thermo-mechanical; MATLAB; GUI-Based Too↗

DOE OSTI2020

The αs and θs in Rayleigh–Taylor and Richtmyer–Meshkov instabilities

The alphas (α b , α s ) and thetas (θ b , θ s ) are the essential parameters describing the asymptotic growth behavior of the mixing layer driven by Rayleigh–Taylor (RT) and Richtmyer–Meshkov (RM) instabilities at the interface between the fluids. Extensive simulations and experiments have been conducted in the determination of these parameters. The results depend on numerical algorithms, resolutions, and the accuracy of experimental measurements. In this article, we present a unique relationship between the alphas and thetas by applying a simple buoyancy-drag mixing model. Our first main result is that, for the same fluids, these parameters are not independent because they are fundamentally connected by physics principles and they all are functions of the drag coefficients of the fluids. As a consequence, the alphas and thetas are intrinsically coupled. All are functions of any single one of these. Additionally, our second main result is that the RT bubble growth rate (α b ) is determined by the bubble competition process – a merger model – at the edge of the mixing layer. Together, these two main results determine all the alphas and thetas by strictly theoretical reasoning. Applications to both hydro and laser driven experiments at OMEGA are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗