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NASA NTRS2013

Fluid Vessel Quantity Using Non-invasive PZT Technology Flight Volume Measurements Under Zero G Analysis

The purpose of the project is to perform analysis of data using the Systems Engineering Educational Discovery (SEED) program data from 2011 and 2012 Fluid Vessel Quantity using Non-Invasive PZT Technology flight volume measurements under Zero G conditions (parabolic Plane flight data). Also experimental planning and lab work for future sub-orbital experiments to use the NASA PZT technology for fluid volume measurement. Along with conducting data analysis of flight data, I also did a variety of other tasks. I provided the lab with detailed technical drawings, experimented with 3d printers, made changes to the liquid nitrogen skid schematics, and learned how to weld. I also programmed microcontrollers to interact with various sensors and helped with other things going on around the lab.

Garofalo, Anthony A↗

NASA NTRS2013

Fluid Vessel Quantity using Non-Invasive PZT Technology Flight Volume Measurements Under Zero G Analysis

Garofalo, Anthony A.↗

DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Nucleation and Growth of Blue Phase 3D Cubic Structure Under Continuously Changing Curved Boundary Conditions

The blue phase of liquid crystals (BPLCs) with a cubic lattice of disclination lines and 3D nanostructure enables the modulation of photonic bandgap thus casting them in the category of photonic crystals. Its unique nature promises applications in display technologies, electro‐optics, and sensors. To integrate these ordered materials into wearable devices a fundamental understanding of curvature, and spatial confinement is necessary. Although continuous confinement in flat geometries have been studied, confining curvature has shown to induce strong destabilization effects on the cubic structure and formation of topological defects, thereby deteriorating their optical performance. Moreover, limitations in controlling the curvature of droplets further hinder studies of nucleation and growth of BPLCs. Here, to address these challenges, micro‐scale patterned surfaces of concentric cylinders are exploited to systematically control curvatures. The impact of curvature on the confined BPLCs is revealed in terms of phase transition temperature, nucleation and growth, morphology, as well as phase transformation. This research offers valuable insights into the stability, and structural characteristics of BPLCs in adaptive photonic devices, paving the way for future advancements in flexible displays, sensors, and other technologies leveraging liquid crystal (LC) materials.

3D Cubic Structure↗

NASA NTRS2017

Electric Potential and Electric Field Imaging with Dynamic Applications & Extensions

The technology and methods for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field made be used for volumes to be inspected with EFI. The baseline sensor technology (e-Sensor) and its construction, optional electric field generation (quasi-static generator), and current e- Sensor enhancements (ephemeral e-Sensor) are discussed. Critical design elements of current linear and real-time two-dimensional (2D) measurement systems are highlighted, and the development of a three dimensional (3D) EFI system is presented. Demonstrations for structural, electronic, human, and memory applications are shown. Recent work demonstrates that phonons may be used to create and annihilate electric dipoles within structures. Phonon induced dipoles are ephemeral and their polarization, strength, and location may be quantitatively characterized by EFI providing a new subsurface Phonon-EFI imaging technology. Results from real-time imaging of combustion and ion flow, and their measurement complications, will be discussed. Extensions to environment, Space and subterranean applications will be presented, and initial results for quantitative characterizing material properties are shown. A wearable EFI system has been developed by using fundamental EFI concepts. These new EFI capabilities are demonstrated to characterize electric charge distribution creating a new field of study embracing areas of interest including electrostatic discharge (ESD) mitigation, manufacturing quality control, crime scene forensics, design and materials selection for advanced sensors, combustion science, on-orbit space potential, container inspection, remote characterization of electronic circuits and level of activation, dielectric morphology of structures, tether integrity, organic molecular memory, atmospheric science, weather prediction, earth quake prediction, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.

Generazio, Ed↗

DOE OSTI2021

Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems

Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.

42 ENGINEERING↗

DOE OSTI2024

DECOVALEX-2023: Task C Final Report

The Full-scale Emplacement (FE) heater experiment at the Mont Terri Underground Rock Laboratory (URL) was designed and conducted by Nagra to replicate an emplacement tunnel of Nagra’s reference repository design at 1:1 scale. Alongside testing the technical feasibility of constructing disposal tunnels, emplacing waste containers in the tunnels and then backfilling them, the main goals of the FE experiment are (1) to obtain a better understanding of the coupled effects of induced thermo-hydro-mechanical (THM) processes that may occur and (2) to validate existing coupled THM models (Müller et al., 2017). A key aspect of ensuring safety for repositories located in low-permeability rock involves minimizing any damage to the rock itself, thereby preserving its integrity and promoting a stable environment Amongst a number of processes that could damage the rock is the increase in pore pressure due to thermal loading caused by heat emitted from the waste. To reduce the potential damage of the rock, it is important to analyse the evolution of heat over time due to the heat load of the containers and assess possible consequences by coupled THM models. The aim of Task C of DECOVALEX-2023 was to build 3D numerical models of the FE experiment, focussing in particular on the heating induced pore pressure change in the Opalinus Clay. Data from a large number of sensors were available from the FE experiment for model comparison. These sensors measured temperature and relative humidity in the bentonite around the heaters, and temperature, pressure and displacement/strain in the surrounding Opalinus clay. Data were available from the start of excavation (April 2012) up to August 2020 for most sensors (more than 5 years from the start of heating in December 2014). To fulfil the overall aim of the task, the work was broken down into a number of steps, starting with simpler models to build confidence in each team’s approach and then moving to more complex models that better represent the FE experiment. Step 0 consisted of 2D benchmark models, gradually increasing the number of processes that are represented from thermal (T) only models in Step 0a, to coupled thermal hydraulic (TH) models in Step 0b with a representation of changing porosity, to coupled thermo-hydro-mechanical (THM) models in Step 0c, where porosity changes are calculated by the mechanical model. A detailed specification of processes, parameters, initial and boundary conditions was provided for this step, with the ambition that all teams would work towards close agreement in their model results, thus building confidence in the model implementations. vi It was not straightforward to achieve agreement between the teams, so additional steps (Step 0b2, 0b3, 0c2, 0c3) were added along with derivation of some analytical solutions against which the models could be compared. The reasons for the differences between teams were investigated and found to be caused primarily by different conceptual model assumptions (including temperature dependence of the thermal expansion of water), different model formulations (including porosity evolution) and differences in modelled domain sizes, boundary conditions and grid discretisation. This demonstrates that comparisons between multiple modelling teams and/or comparison with analytical results and experimental data are highly beneficial in providing an indication of uncertainty in model predictions. At the conclusion of Step 0, almost all teams had achieved a close agreement in model results and those that had not achieved an agreement knew the reason for this. Step 1 moved from 2D models to 3D models of the FE experiment without adding technical features like shotcrete or EDZ, and only considering the heating phase. Initially the 3D model was tightly specified to continue to build confidence in the model implementations (Step 1a). The results of Step 1a were compared to the data from the FE-experiment without the teams seeing the data. The teams were then provided with a sub-set of the data from the FE-experiment and invited to consider how best to use the large dataset for model comparison (Step 1b). Teams were then asked to use the data provided to calibrate their models, only changing material property values rather than adding features or processes to their models (Step 1c). In Step 1, teams were asked to only model the heating phase of the experiment, so pressure in the Opalinus Clay was reported as change in pressure since the initial conditions were specified rather than modelled. The change from 2D to 3D models was accompanied by an increase in the dispersion of results between the teams. Some of this was resolved during the task, but some remained and is potentially due to model discretisation. Calibration of parameters was useful in improving the fit of the models to the data but the remaining differences indicated that the models were missing features or processes. In Step 2, the teams were asked to update their models with additional features and processes as well as calibrating parameters to try and improve the fit of the models to the data. Teams were encouraged to represent ventilation of the open FE tunnel prior to backfilling with heaters and bentonite and in Step 2, the absolute pressure in the Opalinus Clay was compared between the teams. Teams took different approaches, but there was consideration of adding shotcrete and an EDZ into the model, representing stress change during excavation and different approaches to modelling ventilation of the FE tunnel. Overall, the documented results showed a very good agreement for temperature. The results for porewater pressure evolution showed a significant improvement for most teams compared to Step 1c with a good agreement to the measurements for several teams whereas some teams overpredicted the pressure increase and others overpredicted the drainage effect especially for the sensors close to the heater. Step 3 was an opportunity for teams to use the models developed in Step 1 and Step 2 to make predictions about the temperature and pressure changes that will be expected at the FE experiment over the next few years in light of the planned changes in thermal output of the heaters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2014

FLASH LIDAR Based Relative Navigation

Relative navigation remains the most challenging part of spacecraft rendezvous and docking. In recent years, flash LIDARs, have been increasingly selected as the go-to sensors for proximity operations and docking. Flash LIDARS are generally lighter and require less power that scanning Lidars. Flash LIDARs do not have moving parts, and they are capable of tracking multiple targets as well as generating a 3D map of a given target. However, there are some significant drawbacks of Flash Lidars that must be resolved if their use is to be of long-term significance. Overcoming the challenges of Flash LIDARs for navigation-namely, low technology readiness level, lack of historical performance data, target identification, existence of false positives, and performance of vision processing algorithms as intermediaries between the raw sensor data and the Kalman filter-requires a world-class testing facility, such as the Lockheed Martin Space Operations Simulation Center (SOSC). Ground-based testing is a critical step for maturing the next-generation flash LIDAR-based spacecraft relative navigation. This paper will focus on the tests of an integrated relative navigation system conducted at the SOSC in January 2014. The intent of the tests was to characterize and then improve the performance of relative navigation, while addressing many of the flash LIDAR challenges mentioned above. A section on navigation performance and future recommendation completes the discussion.

Brazzel, Jack↗

NASA NTRS1991

Piezoresistive sensors for measurement of thermally-induced stresses in microelectronics

The general theory of conduction in a piezoresistive material subjected to temperature change is derived and the basic equations needed to design and apply test chips for thermal stress measurement are established. A general expression is obtained for the resistance changes which occur in an arbitrarily oriented 1D filamentary conductor subjected to general 3D stress states and arbitrary temperature changes. The derived formula is valid for crystals such as silicon which have cubic symmetry and diamond lattice structures. Basic equations are presented for the resistance changes experienced by stressed in-plane resistors fabricated on (100) silicon wafers. The established theory is applied to a simple two-element sensor rosette. A test chip containing this rosette configuration is designed and fabricated, and a suitable calibration system is constructed.

Suhling, J. C.↗

NASA NTRS1989

Animation graphic interface for the space shuttle onboard computer

Graphics interfaces designed to operate on space qualified hardware challenge software designers to display complex information under processing power and physical size constraints. Under contract to Johnson Space Center, MICROEXPERT Systems is currently constructing an intelligent interface for the LASER DOCKING SENSOR (LDS) flight experiment. Part of this interface is a graphic animation display for Rendezvous and Proximity Operations. The displays have been designed in consultation with Shuttle astronauts. The displays show multiple views of a satellite relative to the shuttle, coupled with numeric attitude information. The graphics are generated using position data received by the Shuttle Payload and General Support Computer (PGSC) from the Laser Docking Sensor. Some of the design considerations include crew member preferences in graphic data representation, single versus multiple window displays, mission tailoring of graphic displays, realistic 3D images versus generic icon representations of real objects, the physical relationship of the observers to the graphic display, how numeric or textual information should interface with graphic data, in what frame of reference objects should be portrayed, recognizing conditions of display information-overload, and screen format and placement consistency.

Wike, Jeffrey↗

NASA NTRS2012

Experimental Studies of Low-Pressure Turbine Flows and Flow Control. Streamwise Pressure Profiles and Velocity Profiles

This report summarizes research performed in support of the NASA Glenn Research Center (GRC) Low-Pressure Turbine (LPT) Flow Physics Program. The work was performed experimentally at the U.S. Naval Academy faculties. The geometry corresponded to "Pak B" LPT airfoil. The test section simulated LPT flow in a passage. Three experimental studies were performed: (a) Boundary layer measurements for ten baseline cases under high and low freestream turbulence conditions at five Reynolds numbers of 25,000, 50,000, 100,000, 200,000, and 300,000, based on passage exit velocity and suction surface wetted length; (b) Passive flow control studies with three thicknesses of two-dimensional bars, and two heights of three-dimensional circular cylinders with different spanwise separations, at same flow conditions as the 10 baseline cases; (c) Active flow control with oscillating synthetic (zero net mass flow) vortex generator jets, for one case with low freestream turbulence and a low Reynolds number of 25,000. The Passive flow control was successful at controlling the separation problem at low Reynolds numbers, with varying degrees of success from case to case and varying levels of impact at higher Reynolds numbers. The active flow control successfully eliminated the large separation problem for the low Reynolds number case. Very detailed data was acquired using hot-wire anemometry, including single and two velocity components, integral boundary layer quantities, turbulence statistics and spectra, turbulent shear stresses and their spectra, and intermittency, documenting transition, separation and reattachment. Models were constructed to correlate the results. The report includes a summary of the work performed and reprints of the publications describing the various studies.This report summarizes research performed in support of the NASA Glenn Research Center (GRC) Low-Pressure Turbine (LPT) Flow Physics Program. The work was performed experimentally at the U.S. Naval Academy faculties. The geometry corresponded to "Pak B" LPT airfoil. The test section simulated LPT flow in a passage. Three experimental studies were performed: (a) Boundary layer measurements for ten baseline cases under high and low freestream turbulence conditions at five Reynolds numbers of 25,000, 50,000, 100,000, 200,000, and 300,000, based on passage exit velocity and suction surface wetted length; (b) Passive flow control studies with three thicknesses of two-dimensional bars, and two heights of three-dimensional circular cylinders with different spanwise separations, at same flow conditions as the 10 baseline cases; (c) Active flow control with oscillating synthetic (zero net mass flow) vortex generator jets, for one case with low freestream turbulence and a low Reynolds number of 25,000. The Passive flow control was successful at controlling the separation problem at low Reynolds numbers, with varying degrees of success from case to case and varying levels of impact at higher Reynolds numbers. The active flow control successfully eliminated the large separation problem for the low Reynolds number case. Very detailed data was acquired using hot-wire anemometry, including single and two velocity components, integral boundary layer quantities, turbulence statistics and spectra, turbulent shear stresses and their spectra, and intermittency, documenting transition, separation and reattachment. Models were constructed to correlate the results. The report includes a summary of the work performed and reprints of the publications describing the various studies. The folders in this supplement contain processed data in ASCII format. Streamwise pressure profiles and velocity profiles are included. The velocity profiles were acquired using single sensor and cross sensor hot-wire probes which were traversed from the wall to the freestream at various streamwise locations. In some of the flow control cases (3D Trips and Jets) profiles were acquired at multiple spanwise locations.

Volino, Ralph↗

DOE OSTI2023

Aerosol-Jet Printed Sensors for Environmental, Safety, and Health Monitoring: A Review

An emergent direct-write approach, aerosol-jet printing (AJP), is gaining attention for the deployment of rapid and affordable microadditively manufactured energy-efficient sensors and printed electronics. AJP enables a broad range of ink viscosities (0.001–1 Pa s) for printing diverse materials ranging from ceramics and metals to polymers and biological matter. Reproducible, high-spatial-resolution features (≈10 µm), and wide standoff distances (1–11 mm) between the nozzle and the substrate facilitate conformal printing of complex geometrical designs on nonplanar—e.g., stepped or curved—surfaces. Here this paper aims to provide a comprehensive overview of state-of-the-art AJP-based sensors (e.g., strain and temperature gauges, biosensors, photosensors, humidity and surface acoustic wave sensors, dielectric elastomer actuators, and motion, smoke, and hazardous gas detectors) and to discuss prospective applications. The drive toward cost-effective devices that are smaller, lighter, and better-performing remains a frontier challenge in the field of printed electronics. Consequently, as AJP becomes increasingly utilized in the high-volume manufacturing of miniaturized active and passive sensors, it opens a pathway for facile large-scale fabrication of devices for a wide range of consumer and industrial applications, including transportation, agriculture, infrastructure, aerospace, national defense, and healthcare.

3-dimensional↗

NASA NTRS2017

3D Material Response Analysis of PICA Pyrolysis Experiments

Primarily interested in improving ablation modeling for use in inverse reconstruction of flight environments on ablative heat shields. Ablation model is essentially a component of the heat flux sensor, so model uncertainties lead to measurement uncertainties. Non-equilibrium processes have been known to be significant in low density ablators for a long time, but increased accuracy requirements of the reconstruction process necessitates incorporating this physical effect. Attempting to develop a pyrolysis model for implementation in material response based on the PICA data produced by Bessire and Minton. Pyrolysis gas species molar yields as a function of temperature and heating rate. Several problems encountered while trying to fit Arrhenius models to the data led to further investigation of the experimental setup.

Oliver, Brandon A.↗

DOE OSTI2025

3D quantum ghost imaging microscope

Quantum ghost imaging uses quantum-entangled photons to generate a two-dimensional image with only a bucket detector at the sample. Here we expand on this approach to generate a three-dimensional image without scanning. A quantum-entangled light source directly links information between a pair of 2D sensors, one of which captures a standard image from one perspective and a second sensor which captures a ghost image from a perpendicular perspective. By correlating the spatial information from the two detectors for each photon pair, we obtain three dimensions of spatial information (x, y, and z) for each scattered photon. We demonstrate that this system can study microscopic environments by imaging scattering from metallic nanoparticle clusters. This approach has the potential to greatly reduce the flux of light required to obtain a 3D image of a biological sample and thereby extend the number of images that can be obtained before photodamaging the sample.

Eshun, Audrey [Lawrence Livermore National Laborat↗

NASA NTRS2020

NeMO-Net – The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%.

Chirayath, Ved↗

NASA NTRS2002

Structural Health Monitoring of Composite Wound Pressure Vessels

The increasing use of advanced composite materials in the wide range of applications including Space Structures is a great impetus to the development of smart materials. Incorporating these FBG sensors for monitoring the integrity of structures during their life cycle will provide valuable information about viability of the usage of such material. The use of these sensors by surface bonding or embedding in this composite will measure internal strain and temperature, and hence the integrity of the assembled engineering structures. This paper focuses on such a structure, called a composite wound pressure vessel. This vessel was fabricated from the composite material: TRH50 (a Mitsubishi carbon fiber with a 710-ksi tensile strength and a 37 Msi modulus) impregnated with an epoxy resin from NEWPORT composites (WDE-3D-1). This epoxy resin in water dispersed system without any solvents and it cures in the 240-310 degrees F range. This is a toughened resin system specifically designed for pressure applications. These materials are a natural fit for fiber sensors since the polyimide outer buffer coating of fiber can be integrated into the polymer matrix of the composite material with negligible residual stress. The tank was wound with two helical patterns and 4 hoop wraps. The order of winding is: two hoops, two helical and two hoops. The wall thickness of the composite should be about 80 mil or less. The tank should burst near 3,000 psi or less. We can measure the actual wall thickness by ultrasonic or we can burst the tank and measure the pieces. Figure 1 shows a cylinder fabricated out of carbon-epoxy composite material. The strain in different directions is measured with a surface bonded fiber Bragg gratings and with embedded fiber Bragg gratings as the cylinder is pressurized to burst pressures. Figure 2 shows the strain as a function of pressure of carbon-epoxy cylinder as it is pressurized with water. Strain is measured in different directions by multiple gratings oriented in both axial and hoops directions.

Joseph Grant↗

DOE OSTI2023

Carbon Fiber and Polymer Composites

Tooling is an integral part of composites manufacturing. Composite tools, also called ‘soft’ tools, are more easily constructed than ‘hard’ metal tools and, because they are made from materials similar to the manufactured composite part, they can be made in-house and have a good co-efficient of thermal expansion match. However, as the ‘soft’ designation suggests, they are more vulnerable to wear and typically find application in relatively low-volume production. Additive manufacturing of thermoplastics has proven to be a lucrative option that can help improve the tooling functionalities, as it offers more design flexibility. Owing to the inherent nature of additive manufacturing, the possibility of printing sensors within the tool material and at proximity to otherwise inaccessible areas of the tool become a reality. Continuous carbon fiber (CCF)-reinforced three-dimensional (3D) printing of engineering thermoplastics has proven to be a viable option to strengthen a part well beyond the ideal moduli of unmodified thermoplastic. Furthermore, adding nanotubes or graphene to the thermoplastic matrix will also improve both the thermal conductivity and interlaminar shear strength within the tool. Carbon nanotubes (CNTs) and carbon fibers (CFs) are microwave susceptors; exploiting the physics of Joule heating to increase the energy efficiency of tool hardening and cure rates is a core goal of this project. Because of the rapid volumetric heating enabled by the absorption of microwave or radio frequency energy and electromagnetic (EM) annealing presents an attractive opportunity to significantly reduce manufacturing cycle times while improving part performance.

Craps, Matthew G.↗