Search NASA⌕ Search

SEARCH · Search NASA

Results for “MLP”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

68 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

Li, Winston↗
NASA NTRS2009

Replacement for a Flex Hose Coating at the Space Shuttle Launch Pad

Aerocoat AR-7 is a coating that has been used to protect stainless steel flex hoses at NASA's Kennedy Space Center launch complex and hydraulic lines of the mobile launch platform (MLP). This coating has great corrosion control performance and low temperature application. AR-7 was developed by NASA and produced exclusively for NASA but its production has been discontinued due to its high content of volatile organic compounds (VOC) and significant environmental impact. The purpose of this project was to select and evaluate candidate coatings to find a replacement coating that is more environmentally friendly, with similar properties to AR-7. No coatings were identified that perform the same as AR-7 in all areas. Candidate coatings failed in comparison to AR-7 in salt fog, beachside atmospheric exposure, pencil hardness, Mandrel bend, chemical compatibility, adhesion, and ease of application tests. However, two coatings were selected for further evaluation.

Whitten, Mary↗
NASA NTRS2004

Space Shuttle Crawler Transporter Sound Attenuation Study

The crawler transporter (CT) is the world's largest tracked vehicle known, weighing 6 million pounds with a length of 131 feet and a width of 113 feet. The Kennedy Space Center (KSC) has two CTs that were designed and built for the Apollo program in the 1960's, maintained and retrofitted for use in the Space Shuttle program. As a key element of the Space Shuttle ground systems, the crawler transports the entire 12-million-pound stack comprising the orbiter, the mobile launch platform (MLP), the external tank (ET), and the solid rocket boosters (SRB) from the Vehicle Assembly Building (VAB) to the launch pad. This rollout, constituting a 3.5-5.0-mile journey at a top speed of 0.9 miles-per-hour, requires over 8 hours to reach either Launch Complex 39A or B. This activity is only a prelude to the spectacle of sound and fury of the Space Shuttle launch to orbit in less than 10 minutes and traveling at orbital velocities of Mach 24. This paper summarizes preliminary results from the Crawler Transporter Sound Attenuation Study, encompassing test and engineering analysis of significant sound sources to measure and record full frequency spectrum and intensity of the various noise sources and to analyze the conditions of vibration. Additionally, data such as ventilation criteria, plus operational procedures were considered to provide a comprehensive noise suppression design for implementation. To date, sound attenuation study and results on Crawler 2 have shown significant noise reductions ranging from 5 to 24 dBA.

Margasahayam, Ravi N.↗
NASA NTRS2009

CATIA V5 Virtual Environment Support for Constellation Ground Operations

This summer internship primarily involved using CATIA V5 modeling software to design and model parts to support ground operations for the Constellation program. I learned several new CATIA features, including the Imagine and Shape workbench and the Tubing Design workbench, and presented brief workbench lessons to my co-workers. Most modeling tasks involved visualizing design options for Launch Pad 39B operations, including Mobile Launcher Platform (MLP) access and internal access to the Ares I rocket. Other ground support equipment, including a hydrazine servicing cart, a mobile fuel vapor scrubber, a hypergolic propellant tank cart, and a SCAPE (Self Contained Atmospheric Protective Ensemble) suit, was created to aid in the visualization of pad operations.

Kelley, Andrew↗
NASA NTRS2007

Exploring the Solid Rocket Boosters and Properties of Matter

I worked for the United Space Alliance, LLC (USA) with the Solid Rocket Booster (SRB) Materials and Process engineers (M&P). I was assigned a project in which I needed to research and collect chemical and physical properties information, material safety data sheets (MSDS), and other product information from the vendor's websites and existing "inhouse" files for a select group of materials used in building and refurbishing the SRBs. This information was then compiled in a report that summarized the information collected. My work site was at the Kennedy Space Center (KSC). This allowed for many opportunities to visit and tour sites operated by NASA, by USA, and by the Air Force. This included the vehicle assembly building (VAB), orbital processing facilities (OPF), the crawler with the mobile launch pad (MLP), and the SRB assembly and refurbishment facility (ARF), to name a few. In addition, the launch, of STS- 117 took place within the first week of employment allowing a day by day following of that mission including post flight operations for the SRBs. Two Delta II rockets were also launched during these 7 weeks. The sights were incredible and the operations witnessed were amazing. I learned so many things I never knew about the entire program and the shuttle itself. The entire experience, especially my work with the SRB materials, inspired my plan for implementation into the classroom.

Moffett, Amy↗
NASA NTRS2016

Mobile Launch Platform Vehicle Assembly Building Area (SWMU 056) Hot Spot 3 Bioremediation Interim Measures Work Plan, Kennedy Space Center, Florida

This Interim Measures Work Plan (IMWP) presents an approach and design for the remediation of chlorinated volatile organic compound (CVOC) groundwater impacts using bioremediation (biostimulation and bioaugmentation) in Hot Spot 3, which is defined by the area where CVOC (trichloroethene [TCE], cis-1,2-dichloroethene [cDCE], and vinyl chloride [VC]) concentrations are greater than 10 times their respective Florida Department of Environmental Protection (FDEP) Natural Attenuation Default Concentration (NADC) [10xNADC] near the western Mobile Launch Platform (MLP) structure. The IM treatment area is the Hot Spot 3 area, which is approximately 0.07 acres and extends from approximately 6 to 22 and 41 to 55 feet below land surface (ft BLS). Within Hot Spot 3, a source zone (SZ; area with TCE concentrations greater than 1% solubility [11,000 micrograms per liter (micrograms/L)]) was delineated and is approximately 0.02 acres and extends from approximately 6 to 16 and 41 to 50 ft BLS.

MLPV↗
NASA NTRSDate not supplied

F-ANG+: A 3-D Augmented-Stencil Face-Averaged Nodal-Gradient Cell-Centered Finite-Volume Method for Hypersonic Flows

We describe the extension of a 2-D simplified face-averaged nodal-gradient (F-ANG) method to 3-D and demonstrate that the 3-D simplified F-ANG method is accomplished by augmenting the nodecentered gradient least squares stencil. This augmented stencil F-ANG method is shown to result in advection and diffusion schemes that are stable for hexahedral, prismatic, pyramidal and tetrahedral cells without having to resort to cell-averaged nodal gradients. In addition, we describe the modifications to the augmented stencil required to support the use of wall function boundary conditions. Finally we describe a consistent, face-stencil based multi-dimensional limiter procedure (MLP), and show it to be fully consistent and compatible with the linearity-preserving unstructured- MUSCL (LP-U-MUSCL) scheme for all values of kappa. These methods and schema are implemented in the cell-centered finite-volume code VULCAN-CFD, which is then used to investigate whether the robustness improvements demonstrated in 2-D carry over to 3-D by computing hypersonic flows using mixed-element grids as well as highly adapted tetrahedral grids.

Weighted Least-Squares↗
NASA NTRS2022

2021 Corrective Measures Implementation and Interim Measures Annual Status Report: Summary of Biosparge System Operation and Maintenance, and Interim Groundwater Monitoring Mobile Launch Platform Rehabilitation Sites / Vehicle Assembly Building Area (SWMU 056) Kennedy Space Center, Florida

This report presents a summary of the Corrective Measures Implementation (CMI) and Interim Measure (IM) implementation activities that occurred from January 2021 through December 2021 at the Mobile Launch Platform Rehabilitation Sites (MLP)/Vehicle Assembly Building (VAB) Area, Solid Waste Management Unit 056 (SWMU 056), located at the John F. Kennedy Space Center, Florida. The following summaries briefly describe areas within SWMU 056 identified by historical site investigation activities where groundwater monitoring and remedial actions have been implemented to date.

Randall K. Sillan↗
NASA NTRS2023

Prediction of Stiffness and Fatigue Lives of Polymer Matrix Composite Laminates Using Artificial Neural Networks

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, both Python and MATLAB-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been developed for both platforms. Results show that the both neural net types can provide an excellent estimate of initial stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminate. RNNs are better able to capture the shape of the fatigue curve of a laminate. This tool can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. The associated surrogate models could also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make multiscale analyses a viable industrial tool for large scale structural problems.

Composite↗
NASA NTRSDate not supplied

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
NASA NTRS2023

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
NASA NTRS2024

2022 Through April 2023 Corrective Measures Implementation and Interim Measures Annual Status Report: Summary of Biosparge System Operation and Maintenance, Interim Measures and Monitoring Mobile Launch Platform Rehabilitation Sites / Vehicle Assembly Building Area (Swmu 056) Kennedy Space Center, Florida

This report presents a summary of the Corrective Measures Implementation (CMI) and Interim Measure (IM) implementation activities that occurred from January 2022 through April 2023 at the Mobile Launch Platform Rehabilitation Sites (MLP)/Vehicle Assembly Building (VAB) Area, Solid Waste Management Unit 056 (SWMU 056), located at the John F. Kennedy Space Center, Florida. The following summaries briefly describe areas within SWMU 056 identified by historical site investigation activities where groundwater monitoring and remedial actions have been implemented to date.

VOCs↗
NASA NTRSDate not supplied

Towards Surrogate Modeling of Subgrid Turbulent Transport for 3D Radiative Hydrodynamic Simulations of the Quiet Sun

In this work, we investigate the use of deep learn-ing techniques as surrogate models, to enhance the estimationof effects of subgrid turbulent transport for 3D radiatuve hy-drodynamic simulations of the quiet Sun. We develop two dis-tinct 3D Convolutional Neural Networks (3DCNNs) to capturespatio-temporal dependencies in 3D velocity fields, leveragingdifferent activation functions and architectural designs. Thesemodels integrate both averaged velocity vector components andscalar features such as plasma density to enhance predictionaccuracy. Additionally, a Multilayer Perceptron (MLP) modelis employed to approximate complex nonlinear relationships,offering a comparison in performance between convolutionaland fully connected architectures. Logarithmic transformationis applied to the targets to handle heavily skewed data, im-proving model performance. All models are compared againsta physics-based Gradient Model. Results show that the 3DCNNmodels excel at approximating Reynolds stress tensors, makingthem a candidate for assisting in producing reduced resolutionsimulations, and thereby reducing computational overheadwhile maintaining higher accuracy than the baseline. Thesefindings demonstrate the potential of deep learning, particu-larly CNNs, to advance scalable and accurate simulations ofsolar dynamics, offering a promising alternative to traditionalturbulence models.

Heliophysics↗
NASA NTRSDate not supplied

Surrogate Modeling of Subgrid Turbulent Transport Based on 3D Radiative Hydrodynamic Simulations of the Quiet Sun

Turbulent Transport: Plays a critical role in astrophysical plasmas, such as the solar interior, spanning multiple scales and challenging traditional modeling approaches. Objective: Develop machine learning (ML) models—MLP and CNN—to predict subgrid Reynolds stress tensors from StellarBox 3D simulations of the solar atmosphere. Benchmarking: Compare ML-driven models against physics-based Gradient and Smagorinsky approaches.

SMD↗