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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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At least 37 records · Page 2

Progress in hypersonic combustion technology with computation and experiment

Design of successful airbreathing engines for operation at near-orbital speeds presents significant challenges in all the disciplines involved, including propulsion. This paper presents a discussion of the important physics of hypersonic combustion and an assessment of the state of the art of ground simulations with pulse facilities and with computational techniques. Recent examples of experimental and computational simulations are presented and discussed. The need for continued application of these tools to establish the credibility and fidelity of engineering design methods for practical hypersonic combustors is emphasized along with the critical need for improved diagnostic methods for hypervelocity reacting flows.

Anderson, Griffin Y.↗

Shuttle Debris Impact Tool Assessment Using the Modern Design of Experiments

Computational tools have been developed to estimate thermal and mechanical reentry loads experienced by the Space Shuttle Orbiter as the result of cavities in the Thermal Protection System (TPS). Such cavities can be caused by impact from ice or insulating foam debris shed from the External Tank (ET) on liftoff. The reentry loads depend on cavity geometry and certain Shuttle state variables, among other factors. Certain simplifying assumptions have been made in the tool development about the cavity geometry variables. For example, the cavities are all modeled as shoeboxes , with rectangular cross-sections and planar walls. So an actual cavity is typically approximated with an idealized cavity described in terms of its length, width, and depth, as well as its entry angle, exit angle, and side angles (assumed to be the same for both sides). As part of a comprehensive assessment of the uncertainty in reentry loads estimated by the debris impact assessment tools, an effort has been initiated to quantify the component of the uncertainty that is due to imperfect geometry specifications for the debris impact cavities. The approach is to compute predicted loads for a set of geometry factor combinations sufficient to develop polynomial approximations to the complex, nonparametric underlying computational models. Such polynomial models are continuous and feature estimable, continuous derivatives, conditions that facilitate the propagation of independent variable errors. As an additional benefit, once the polynomial models have been developed, they require fewer computational resources to execute than the underlying finite element and computational fluid dynamics codes, and can generate reentry loads estimates in significantly less time. This provides a practical screening capability, in which a large number of debris impact cavities can be quickly classified either as harmless, or subject to additional analysis with the more comprehensive underlying computational tools. The polynomial models also provide useful insights into the sensitivity of reentry loads to various cavity geometry variables, and reveal complex interactions among those variables that indicate how the sensitivity of one variable depends on the level of one or more other variables. For example, the effect of cavity length on certain reentry loads depends on the depth of the cavity. Such interactions are clearly displayed in the polynomial response models.

DeLoach, R.↗

Implementation of Model-Based Design of Experiments: Application of Computational Modeling to Support HRP Studies

This study illustrates the potential gains obtained by leveraging computational modeling to improve experimental efficiency in NASA research and counter measures studies through implementation of Model-Based Design of Experiments (MBDOE). MBDOE is a method to utilize analogous computational models to improve understanding of complex, multifactor, experimental responses and to determine experimental conditions and optimize information in the fewest number of experimental tests.

physiological response↗

Computational design aspects of a NASP nozzle/afterbody experiment

This paper highlights the influence of computational methods on design of a wind tunnel experiment which generically models the nozzle/afterbody flow field of the proposed National Aerospace Plane. The rectangular slot nozzle plume flow field is computed using a three-dimensional, upwind, implicit Navier-Stokes solver. Freestream Mach numbers of 5.3, 7.3, and 10 are investigated. Two-dimensional parametric studies of various Mach numbers, pressure ratios, and ramp angles are used to help determine model loads and afterbody ramp angle and length. It was found that the center of pressure on the ramp occurs at nearly the same location for all ramp angles and test conditions computed. Also, to prevent air liquefaction, it is suggested that a helium-air mixture be used as the jet gas for the highest Mach number test case.

Ruffin, Stephen M.↗

Development of user guidelines for ECAS display design, volume 1

Experiment computer application software (ECAS) display design and command usage guidelines were developed, which if followed by spacelab experiments, would standardize methods and techniques for data presentation and commanding via ECAS. These guidelines would provide some commonality among experiments which would enhance crew training and flight operations. The guidelines are applicable to all onboard experiment displays, whether allocated by ECAS or a dedicated experiment processor. A brief description of the spacelab data display system characteristics and of the services provided by the experiment computer operating system is included. Guidelines concerning data presentation and layout of alphanumeric and graphic information are presented along with guidelines concerning keyboard commanding and command feedback.

Dodson, D. W.↗

Development of display design and command usage guidelines for Spacelab experiment computer applications

Individual Spacelab experiments are responsible for developing their CRT display formats and interactive command scenarios for payload crew monitoring and control of experiment operations via the Spacelab Data Display System (DDS). In order to enhance crew training and flight operations, it was important to establish some standardization of the crew/experiment interface among different experiments by providing standard methods and techniques for data presentation and experiment commanding via the DDS. In order to establish optimum usage guidelines for the Spacelab DDS, the capabilities and limitations of the hardware and Experiment Computer Operating System design had to be considered. Since the operating system software and hardware design had already been established, the Display and Command Usage Guidelines were constrained to the capabilities of the existing system design. Empirical evaluations were conducted on a DDS simulator to determine optimum operator/system interface utilization of the system capabilities. Display parameters such as information location, display density, data organization, status presentation and dynamic update effects were evaluated in terms of response times and error rates.

Dodson, D. W.↗

Application of advanced computational codes in the design of an experiment for a supersonic throughflow fan rotor

Increased emphasis on sustained supersonic or hypersonic cruise has revived interest in the supersonic throughflow fan as a possible component in advanced propulsion systems. Use of a fan that can operate with a supersonic inlet axial Mach number is attractive from the standpoint of reducing the inlet losses incurred in diffusing the flow from a supersonic flight Mach number to a subsonic one at the fan face. The design of the experiment using advanced computational codes to calculate the components required is described. The rotor was designed using existing turbomachinery design and analysis codes modified to handle fully supersonic axial flow through the rotor. A two-dimensional axisymmetric throughflow design code plus a blade element code were used to generate fan rotor velocity diagrams and blade shapes. A quasi-three-dimensional, thin shear layer Navier-Stokes code was used to assess the performance of the fan rotor blade shapes. The final design was stacked and checked for three-dimensional effects using a three-dimensional Euler code interactively coupled with a two-dimensional boundary layer code. The nozzle design in the expansion region was analyzed with a three-dimensional parabolized viscous code which corroborated the results from the Euler code. A translating supersonic diffuser was designed using these same codes.

Wood, Jerry R.↗

Application of advanced computational codes in the design of an experiment for a supersonic throughflow fan rotor

Increased emphasis on sustained supersonic or hypersonic cruise has revived interest in the supersonic throughflow fan as a possible component in advanced propulsion systems. Use of a fan that can operate with a supersonic inlet axial Mach number is attractive from the standpoint of reducing the inlet losses incurred in diffusing the flow from a supersonic flight Mach number to a subsonic one at the fan face. The design of the experiment using advanced computational codes to calculate the components required is described. The rotor was designed using existing turbomachinery design and analysis codes modified to handle fully supersonic axial flow through the rotor. A two-dimensional axisymmetric throughflow design code plus a blade element code were used to generate fan rotor velocity diagrams and blade shapes. A quasi-three-dimensional, thin shear layer Navier-Stokes code was used to assess the performance of the fan rotor blade shapes. The final design was stacked and checked for three-dimensional effects using a three-dimensional Euler code interactively coupled with a two-dimensional boundary layer code. The nozzle design in the expansion region was analyzed with a three-dimensional parabolized viscous code which corroborated the results from the Euler code. A translating supersonic diffuser was designed using these same codes.

Wood, Jerry R.↗

On the Use of Statistics in Design and the Implications for Deterministic Computer Experiments

Perhaps the most prevalent use of statistics in engineering design is through Taguchi's parameter and robust design -- using orthogonal arrays to compute signal-to-noise ratios in a process of design improvement. In our view, however, there is an equally exciting use of statistics in design that could become just as prevalent: it is the concept of metamodeling whereby statistical models are built to approximate detailed computer analysis codes. Although computers continue to get faster, analysis codes always seem to keep pace so that their computational time remains non-trivial. Through metamodeling, approximations of these codes are built that are orders of magnitude cheaper to run. These metamodels can then be linked to optimization routines for fast analysis, or they can serve as a bridge for integrating analysis codes across different domains. In this paper we first review metamodeling techniques that encompass design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We discuss their existing applications in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of metamodeling techniques in given situations and how common pitfalls can be avoided.

Simpson, Timothy W.↗

Progress on the FabrIc for Frontier Experiments project at Fermilab

The FabrIc for Frontier Experiments (FIFE) project is an ambitious, major-impact initiative within the Fermilab Scientific Computing Division designed to lead the computing model for Fermilab experiments. FIFE is a collaborative effort between experimenters and computing professionals to design and develop integrated computing models for experiments of varying needs and infrastructure. The major focus of the FIFE project is the development, deployment, and integration of Open Science Grid solutions for high throughput computing, data management, database access and collaboration within experiment. To accomplish this goal, FIFE has developed workflows that utilize Open Science Grid sites along with dedicated and commercial cloud resources. The FIFE project has made significant progress integrating into experiment computing operations several services including new job submission services, software and reference data distribution through CVMFS repositories, flexible data transfer client, and access to opportunistic resources on the Open Science Grid. Hence, the progress with current experiments and plans for expansion with additional projects will be discussed. FIFE has taken a leading role in the definition of the computing model for Fermilab experiments, aided in the design of computing for experiments beyond Fermilab, and will continue to define the future direction of high throughput computing for future physics experiments worldwide

97 MATHEMATICS AND COMPUTING↗

Progress and challenges in modeling turbulent aerodynamic flows

Progress in modeling external aerodynamic flows achieved by using computations and experiments designed to guide turbulence modeling is presented. The computational procedures emphasize utilization of the Reynolds-averaged Navier-Stokes equations and various statistical modeling approaches. Developments for including the influence of compressibility are provided; they point up some of the complexities involved in modeling high-speed flows. Examples of complementary studies that provide the status, limitations, and future challenges of modeling for transonic, supersonic, and hypersonic flows are given.

Marvin, Joseph G.↗

Optimal experimental design using eigenvalue-based criteria with Pyomo.DoE

New developments in automated optimal experimental design within the PSE+ software ecosystem. Advancements in user experience (to reduce the time taken to perform optimal experiment design) and computational capabilities (allowing more diverse experimental design) are shown with an example relevant to critical minerals and materials. Also, a small tutorial on science-based optimal experimental design and novel contributions therein are presented.

97 MATHEMATICS AND COMPUTING↗

Deep learning-based predictive models for laser direct drive at the Omega Laser Facility

The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Bridging the gap between experiments and simulations using machine learning

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Designing Critical Experiments Using Gaussian Process Optimization

New nuclear applications may lack similarity between the application and existing ICSBEP benchmarks. Without accurate benchmark data, predictive simulations will have high uncertainty in the estimate of a parameter of interest (such as k eff ). The design of critical experiments tuned for specific applications of interest is one solution to reducing uncertainty in the necessarily nuclear data. We propose an optimization technique to design critical experiments that match the cross-section sensitivities of an application model while minimizing the number of simulation runs in the optimization. The optimization of a critical experiment is slow because the potential design space covers many qualitative variables (types of material and geometries) and corresponding quantitative variables (e.g., material dimensions) and the evaluation of any specific design requires running an expensive code like MCNP1 and Whisper. The optimization of expensive black-box computer codes with quantitative variables has been extensively explored in the literature of design of computer experiments. We propose a modified version of standard Gaussian process (GP) optimization that optimizes over multiple discrete configurations of qualitative variables in parallel which reduces the computational cost of the entire optimization. The goal of this work is to minimize the number of MCNP evaluations while still exploring many different design configurations in parallel.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Optimizing Batch Crystallization with Model-based Design of Experiments

Poster for Foundations of Computer Aided Process Design (FOCAPD) 2024 describing the formality of model-based design of experiments for batch crystallization. This paper focuses on the implementation of model-based design of experiments for parameter precision, enabled by Pyomo.DoE, a package in Python. Specifically, this work outlines an identifiability analysis for the estimability of the model parameters in a batch crystallization system.

Lynch, Hailey↗