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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Statistical Wind-Tunnel Experimentation Advancements for eVTOL Aircraft Aero-Propulsive Model Development

Compared to many conventional aircraft, electric vertical takeoff and landing (eVTOL) vehicle designs present greater aero-propulsive complexity and many interacting factors requiring development of novel testing and model development strategies. This paper describes several advances in statistically-rigorous experiment design methods for wind-tunnel testing of eVTOL aircraft enabling accurate aero-propulsive model development. The techniques are applied to a subscale tilt-rotor eVTOL aircraft configuration built at NASA Langley Research Center that has 24 independent control effectors. The general testing approach, leveraging design of experiments and response surface methodology, is outlined and followed by a detailed description of the experiment design and modeling approach employed for different phases of the wind-tunnel testing. A new approach for gravitational tare modeling is developed and validated, which reduces the required wind-tunnel test time by nearly 50%. A mostly automated trim envelope determination approach is formulated to rapidly determine the aircraft flight envelope to inform transition testing for aero-propulsive model development. Efficient powered-airframe characterization tests are conducted using a nested I-optimal design approach formulated in previous simulation-based testing. The collective wind-tunnel testing approach facilitates rapid characterization of eVTOL aircraft configurations and produces accurate aero-propulsive models that can be used in flight dynamics simulations.

design of experiments↗
NASA NTRS2011

Teaching the Next Generation of Scientists and Engineers the NASA Design Process

The Integrated Product Team (IPT) program, led by The University of Alabama in Huntsville (UAH), is a multidisciplinary, multi-university, multi-level program whose goal is to provide opportunities for high school and undergraduate scientists and engineers to translate stakeholder needs and requirements into viable engineering design solutions via a distributed multidisciplinary team environment. The current program supports three projects. The core of the program is the two-semester senior design experience where science, engineering, and liberal arts undergraduate students from UAH, the College of Charleston, Southern University at Baton Rouge, and Ecole Suprieure des Techniques Aronautiques et de Construction Automobile (ESTACA) in Paris, France form multidisciplinary competitive teams to develop system concepts of interest to the local aerospace community. External review boards form to provide guidance and feedback throughout the semester and to ultimately choose a winner from the competing teams. The other two projects, the Innovative Student Project for the Increased Recruitment of Engineering and Science Students (InSPIRESS) Level I and Level II focus exclusively on high school students. InSPIRESS Level I allows high schools to develop a payload to be accommodated on the system being developed by senior design experience teams. InSPIRESS Level II provides local high school students first-hand experience in the senior design experience by allowing them to develop a subsystem or component of the UAH-led system over the two semesters. This program provides a model for NASA centers to engage the local community to become more involved in design projects.

Caruso, Pamela W.↗
DOE OSTI2025

Neutron irradiation & thermomechanical experiment (NITE) - design

For the reliable long-term operation of fusion power plants, it is crucial to understand and predict the lifetime of materials in use. These materials include all structural and functional materials utilized at the first wall, blanket, magnets, and shielding. The key challenge is, that the harsh environment including high heat fluxes, high thermal stress and stress cycling, neutron irradiation, and sputtering on such materials should not be viewed separately. Currently, the synergistic loads cannot be evaluated experimentally because of the lack of adequate facilities. The purpose of that work is to design a synergetic Neutron Irradiation and Thermomechanical Experiment (NITE) for fusion materials. This design will leverage the existing Advanced-Test-Reactor (ATR), a fission reactor at the Idaho National Laboratory. We also acknowledge that with existing fission reactors the exact fusion condition cannot be created, and the limitations are critically discussed. The combination of neutron irradiation with a high heat flux is the focus. This is realized with an irradiation capsule design that includes a TRISO fueled region inside the capsule to enable a steady-state heat flux on one side of the specimen. In conclusion, the experimental design modeling showed that steady-state heat fluxes of 2.4 MW/m 2 with a thermal gradient of above 250°C can be achieved in a 5 mm thick specimen.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2009

Highly Efficient Design-of-Experiments Methods for Combining CFD Analysis and Experimental Data

It is the purpose of this study to examine the impact of "highly efficient" Design-of-Experiments (DOE) methods for combining sets of CFD generated analysis data with smaller sets of Experimental test data in order to accurately predict performance results where experimental test data were not obtained. The study examines the impact of micro-ramp flow control on the shock wave boundary layer (SWBL) interaction where a complete paired set of data exist from both CFD analysis and Experimental measurements By combining the complete set of CFD analysis data composed of fifteen (15) cases with a smaller subset of experimental test data containing four/five (4/5) cases, compound data sets (CFD/EXP) were generated which allows the prediction of the complete set of Experimental results No statistical difference were found to exist between the combined (CFD/EXP) generated data sets and the complete Experimental data set composed of fifteen (15) cases. The same optimal micro-ramp configuration was obtained using the (CFD/EXP) generated data as obtained with the complete set of Experimental data, and the DOE response surfaces generated by the two data sets were also not statistically different.

Anderson, Bernhard H.↗
NASA NTRS2012

Directed Design of Experiments for Validating Probability of Detection Capability of a Testing System

A method of validating a probability of detection (POD) testing system using directed design of experiments (DOE) includes recording an input data set of observed hit and miss or analog data for sample components as a function of size of a flaw in the components. The method also includes processing the input data set to generate an output data set having an optimal class width, assigning a case number to the output data set, and generating validation instructions based on the assigned case number. An apparatus includes a host machine for receiving the input data set from the testing system and an algorithm for executing DOE to validate the test system. The algorithm applies DOE to the input data set to determine a data set having an optimal class width, assigns a case number to that data set, and generates validation instructions based on the case number.

Generazio, Edward R.↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
NASA NTRS1990

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.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design can have a profound impact on life-cycle cost (LCC). Widely accepted that nearly 80% of LCC is committed. Decisions made during early design must be well informed. Advanced Concepts Office (ACO) at Marshall Space Flight Center aids in decision making for launch vehicles. Provides rapid turnaround pre-phase A and phase A studies. Provides customer with preliminary vehicle sizing information, vehicle feasibility, and expected performance.

Zwack, Matthew R.↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRS1991

Use of Taguchi Design of Experiments to Determine ALPLS Ascent Delta-5 Sensitivities and Total Mass Sensitivities to Release Conditions and Vehicle Parameters

The objective of this study is to evaluate the use of Taguchi's Design of Experiment Methods to improve the effectiveness of this and future parametric studies. Taguchi Methods will be applied in addition to the typical approach to provide a mechanism for comparing the results and the cost or effort necessary to complete the studies. It is anticipated that results of this study should include an improved systematic analysis process, an increase in information obtained at a lower cost, and a more robust, cost effective vehicle design.

Carrasco, Hector Ramon↗
NASA NTRS1981

Earth Radiation Budget Experiment instrument design status

The design configuration of Earth Radiation Budget Experiment (ERBE) instruments are presented. The ERBE scanners and nonscanners are discussed in terms of hardware and optical development, operational parameters, and calibration plans for ground and flight systems. The various instrument designs are given and described in detail. It is emphasized that the design goal is to approach state-of-the-art laboratory radiometric accuracies with spaceflight qualified hardware in an earth orbit environment with a projected operational lifetime of over two years. Accurate inputs to models of weather and climate are expected which will allow a better understanding of radiation transfer and the energy balance of the earth.

Kopia, L. P.↗
NASA NTRS2011

Verification and Validation of Requirements on the CEV Parachute Assembly System Using Design of Experiments

The Crew Exploration Vehicle Parachute Assembly System (CPAS) project conducts computer simulations to verify that flight performance requirements on parachute loads and terminal rate of descent are met. Design of Experiments (DoE) provides a systematic method for variation of simulation input parameters. When implemented and interpreted correctly, a DoE study of parachute simulation tools indicates values and combinations of parameters that may cause requirement limits to be violated. This paper describes one implementation of DoE that is currently being developed by CPAS, explains how DoE results can be interpreted, and presents the results of several preliminary studies. The potential uses of DoE to validate parachute simulation models and verify requirements are also explored.

Schulte, Peter Z.↗