Search NASA⌕ Search

SEARCH · Search NASA

Results for “Comparative visualization”

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.

At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Vortex breakdown in closed containers with polygonal cross sections

The vortex breakdown bubble in the confined flow generated by a rotating lid in closed containers with polygonal cross sections was analysed both experimentally and numerically for the height/radius aspect ratio equal to 2. The stagnation point locations of the breakdown bubble emergence and the corresponding Reynolds number were determined experimentally and in addition computed numerically by STAR-CCM+ CFD software for square, pentagonal, hexagonal, and octagonal cross section configurations. The flow pattern and the velocity were observed and measured by combining the seeding particle visualization and the temporal accuracy of laser Doppler anemometry. The vortex breakdown size and position on the container axis were determined for Reynolds numbers, ranging from 1450 to 2400. The obtained results were compared with the flow structure in the closed container of cubical and cylindrical configurations. It is shown that the measured evolution of steady vortex breakdown is in close agreement with the numerical results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

THE EXTENDED VIRGO CLUSTER CATALOG

We present a new catalog of galaxies in the wider region of the Virgo cluster, based on the Sloan Digital Sky Survey (SDSS) Data Release 7. The Extended Virgo Cluster Catalog (EVCC) covers an area of 725 deg{sup 2} or 60.1 Mpc{sup 2}. It is 5.2 times larger than the footprint of the classical Virgo Cluster Catalog (VCC) and reaches out to 3.5 times the virial radius of the Virgo cluster. We selected 1324 spectroscopically targeted galaxies with radial velocities less than 3000 km s{sup –1}. In addition, 265 galaxies that have been overlooked in the SDSS spectroscopic survey but have available redshifts in the NASA Extragalactic Database are also included. Our selection process secured a total of 1589 galaxies, 676 of which are not included in the VCC. The certain and possible cluster members are defined by means of redshift comparison with a cluster infall model. We employed two independent and complementary galaxy classification schemes: the traditional morphological classification based on the visual inspection of optical images and a characterization of galaxies from their spectroscopic features. SDSS u, g, r, i, and z passband photometry of all EVCC galaxies was performed using Source Extractor. We compare the EVCC galaxies with the VCC in terms of morphology, spatial distribution, and luminosity function. The EVCC defines a comprehensive galaxy sample covering a wider range in galaxy density that is significantly different from the inner region of the Virgo cluster. It will be the foundation for forthcoming galaxy evolution studies in the extended Virgo cluster region, complementing ongoing and planned Virgo cluster surveys at various wavelengths.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Type Ia supernovae in the star formation deserts of spiral host galaxies

ABSTRACT Using a sample of nearby spiral galaxies hosting 185 supernovae (SNe) Ia, we perform a comparative analysis of the locations and light-curve decline rates (Δm15) of normal and peculiar SNe Ia in the star formation deserts (SFDs) and beyond. To accomplish this, we present a simple visual classification approach based on the UV/H α images of the discs of host galaxies. We demonstrate that, from the perspective of the dynamical time-scale of the SFD, where the star formation is suppressed by the bar evolution, the Δm15 of SN Ia and progenitor age can be related. The SFD phenomenon gives an excellent possibility to separate a subpopulation of SN Ia progenitors with ages older than a few Gyr. We show, for the first time, that the SFDs contain mostly faster declining SNe Ia (Δm15 > 1.25). For the galaxies without SFDs, the region within the bar radius, and outer disc contain mostly slower declining SNe Ia. To better constrain the delay times of SNe Ia, we encourage new studies (e.g. integral field observations) using the SFD phenomenon on larger and more robust datasets of SNe Ia and their host galaxies.

Hakobyan, A. A. (ORCID:0000000173921765)↗
DOE OSTI2022

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Flowable Cementitious Grouts for False Bottoms in Hanford Waste Tanks – 26085

Cementitious grouts with epoxy coatings are being evaluated as an approach to refurbish the bottom of waste tanks at the Hanford Site. This work studies four grout concept materials for this application, two grouts being from commercial vendors and two grouts based off formulations used to close reactors and tanks at the Savannah River Site. All the candidate grouts demonstrate flowability and pumpability at the 0.5 cu ft scale. The commercial vendor products demonstrate the best flow and have lower water/premix ratio and are less likely to have bleed, which would impact epoxy bonding to the grout. The commercial products also demonstrate increased compression strengths compared to the reactor closure grouts. Additional fresh and cured properties for all the grout formulations were similar. The grouts in the tank waste simulant without epoxy coating do not crack but show visual changes to the grout and the surrounding simulant solution, and simulant absorption into the grout. The grout/epoxy bond in ambient conditions has strengths of over 7 MPa and needs to be evaluated for simulant chemical durability. Overall, the results to date justify testing the flowability, pumpability, and placement of the grouts at larger, engineering scale and for further studies of the interactions between the grout and epoxy.

Valdes, Nicholas [Savannah River National Laborato↗
DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗
DOE OSTI2016

SU-F-J-88: Comparison of Two Deformable Image Registration Algorithms for CT-To-CT Contour Propagation

Purpose: To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling. Methods: Both DIR algorithms were used for CT-to-CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagation was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0 0.9 for lungs, > 0.85 for heart, > 0.8 for liver) and similar qualitative assessments (VQS < 0.35, RQS > 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR. Conclusion: The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Improved quasiparticle self-consistent electronic band structure and excitons in β – LiGaO 2

The band structure of β–LiGaO 2 is calculated using the quasiparticle self-consistent QSGWˆ method where the screened Coulomb interaction Wˆ is evaluated including electron-hole interaction ladder diagrams and G is the one-electron Green's function. Improved convergence compared to previous calculations leads to a significantly larger band gap of about 7.0 eV. However, exciton binding energies are found to be large and lead to an exciton gap of about 6.0 eV if also a zero-point-motion correction of about –0.4 eV is included. Furthermore, these results are in excellent agreement with recent experimental results on the onset of absorption. Besides the excitons observed thus far, the calculations indicate the existence of a Rydberg-like series of exciton excited states, which is however modified from the classical Wannier exciton model by the anisotropies of the material and the more complex mixing of Bloch states in the excitons resulting from the Bethe-Salpeter equation. The exciton fine structure and the exciton wave functions are visualized and analyzed in various ways.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Supplemental macronutrients and microbial fermentation products improve the uptake and transport of foliar applied zinc in sunflower ( Helianthus annuus L.) plants. Studies utilizing micro X-ray florescence

Enhancing nutrient uptake and the subsequent elemental transport from the sites of application to sites of utilization is of great importance to the science and practical field application of foliar fertilizers. The aim of this study was to investigate the mobility of various foliar applied zinc (Zn) formulations in sunflower ( Helianthus annuus L.) and to evaluate the effects of the addition of an organic biostimulant on phloem loading and elemental mobility. This was achieved by application of foliar formulations to the blade of sunflower ( H. annuus L.) and high-resolution elemental imaging with micro X-ray fluorescence (μ-XRF) to visualize Zn within the vascular system of the leaf petiole. Although no significant increase of total Zn in petioles was determined by inductively-coupled plasma mass-spectrometer, μ-XRF elemental imaging showed a clear enrichment of Zn in the vascular tissues within the sunflower petioles treated with foliar fertilizers containing Zn. The concentration of Zn in the vascular of sunflower petioles was increased when Zn was applied with other microelements with EDTA (commercial product Kick-Off) as compared with an equimolar concentration of ZnSO₄ alone. The addition of macronutrients N, P, K (commercial product CleanStart) to the Kick-Off Zn fertilizer, further increased vascular system Zn concentrations while the addition of the microbially derived organic biostimulant “GroZyme” resulted in a remarkable enhancement of Zn concentrations in the petiole vascular system. The study provides direct visualized evidence for phloem transport of foliar applied Zn out of sites of application in plants by using μ-XRF technique, and suggests that the formulation of the foliar applied Zn and the addition of the organic biostimulant GroZyme increases the mobility of Zn following its absorption by the leaf of sunflower.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

SU-E-I-51: Use of Blade Sequences in Cervical Spine MR Imaging for Eliminating Motion, Truncation and Flow Artifacts

Purpose: To assess the efficacy of the BLADE technique to eliminate motion, truncation, flow and other artifacts in Cervical Spine MRI compared to the conventional technique. To study the ability of the examined sequences to reduce the indetention and wrap artifacts, which have been reported in BLADE sagittal sequences. Methods: Forty consecutive subjects, who had been routinely scanned for cervical spine examination using four different image acquisition techniques, were analyzed. More specifically, the following pairs of sequences were compared: a) T2 TSE SAG vs. T2 TSE SAG BLADE and b) T2 TIRM SAG vs. T2 TIRM SAG BLADE. A quantitative analysis was performed using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and relative contrast (ReCon) measures. A qualitative analysis was also performed by two radiologists, who graded seven image characteristics on a 5-point scale (0:non-visualization; 1:poor; 2:average; 3:good; 4:excellent). The observers also evaluated the presence of image artifacts (motion, truncation, flow, indentation). Results: Based on the findings of the quantitative analysis, the ReCON values of the CSF (cerebrospinal fluid)/SC (spinal cord) between TIRM SAG and TIRM SAG BLADE were found to present statistical significant differences (p<0.001). Regarding motion and truncation artifacts, the T2 TSE SAG BLADE was superior compared to the T2 TSE SAG and the T2 TIRM SAG BLADE was superior compared to the T2 TIRM SAG. Regarding flow artifacts, T2 TIRM SAG BLADE eliminated more artifacts compared to the T2 TIRM SAG. Conclusion: The use of BLADE sequences in cervical spine MR examinations appears to be capable of potentially eliminating motion, pulsatile flow and trancation artifacts. Furthermore, BLADE sequences are proposed to be used in the standard examination protocols based on the fact that a significantly improved image quality could be achieved.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Model-based CT performance assessment and optimization for iodinated and noniodinated imaging tasks as a function of kVp and body size

Purpose: The goal of this study was to assess the comparative performance of iterative reconstruction in space (IRIS) and filtered back projection (FBP) reconstruction algorithms in terms of image quality and dose across kVps and phantom sizes. Methods: The ACR CT phantom (model 464) was supplemented with the addition of an iodinated spherical capsule (1.5 mm diameter, 3.4 mg iodine per ml) to simulate the contrast filled structures and with an additional circular attachment consisting of an array of 500 um brass beads for spatial resolution measurements. A larger sized phantom was also created by wrapping the original phantom with additional tissue equivalent material of 4 cm thickness. The phantoms were imaged on a 64 detector array multidetector computed tomography scanner (Somatom Definition, Siemens, Germany) using clinically applicable protocols (0.5 s rotation time; 80, 100, 120, and 140 kVp; 64 to 640 mA; 220 to 250 mm field of view). Images were reconstructed using the FBP and the IRIS algorithms. Combining measurements of image noise and spatial resolution with a task function, a figure of merit (FOM) for image quality was generated taking into account the type of visualization required from the image for the detection of either large or small image features with and without iodine content. The FOM was further reported in terms of area under the receiver operating characteristic (ROC) curve (A{sub Z}) to predict the comparative diagnostic performance of the two algorithms at different dose levels. Results: For a given dose level, the predicted A{sub Z} for IRIS consistently outperformed that of FBP. At comparative A{sub Z}, depending on protocol and task, the dose requirement for the optimal technique (optimized kVp with IRIS) was 2-3 times lower than that for standard technique (120 kVp with FBP). The potential for dose reduction was found to be higher when performing small feature detection tasks in comparison to larger feature detection tasks. The optimal kVp was from 80 to 100 kVp for the small phantom, 100 to 120 kVp for the larger phantom. Conclusions: Overall, greater dose reduction may be achieved with IRIS compared to FBP, with enhanced advantage at thinner slice reconstructions. The results highlight how IRIS may offer a superior balance between image quality and dose across a range of imaging tasks, thus enabling dose reduction at constant quality or image quality improvement at constant dose. The prediction of the investigation can be used toward effective design of subsequent clinical studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Argon broad ion beam sectioning and high resolution scanning electron microscopy imaging of hydrated alite

Highlights: • The native fibrous outer and dense inner C-S-H structure are preserved by using low energy argon broad ion beam sectioning. • Possible artefacts of the preparation and imaging of hydrated alite are shown and discussed. • An approach for a semi automated pore analysis of the obtained high resolution images is demonstrated. Scanning electron microscopy (SEM) imaging is able to visualize micro- to nano-structures of cement and concrete. A prerequisite is that the sample preparation preserves the native structure of the specimen. In this study, argon broad ion beam (BIB) sectioning is compared to state-of-the-art sample preparation (resin embedding, polishing) for hydrated alite. Additionally, it is investigated if during BIB, sample cooling is beneficial to avoid deterioration of cement hydrates. The aim is to quantitatively measure pore size distributions in hardened alite pastes. Therefore, not only optimized sample preparation but also optimized imaging conditions are investigated. Finally, it is demonstrated that by image analysis pores down to a diameter of 5 nm in hydrated alite pastes can be quantitatively analysed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High-Resolution Comonomer Sequencing of Blocky Brominated Syndiotactic Polystyrene Copolymers Using 13C NMR Spectroscopy and Computer Simulations

This work demonstrates the first high-resolution comonomer sequencing of Blocky brominated syndiotactic polystyrene (sPS-co-sPS-Br) copolymers based on pentad assign- ments of the quaternary carbon region of the nuclear magnetic resonance spectrum. Copolymers containing p-bromostyrene (Br-Sty) units were prepared in matched sets using postpolymerization bromination methods carried out in the heterogeneous gel state (Blocky) and homogeneous solution state (Random). Quantitative information from the quaternary carbon spectra, heteronuclear multiple bond correlation spectroscopy, electronic structure calculations, and simulated statistically random copolymers was correlated to confirm the carbon resonance assignments for all 20 possible pentad comonomer sequences. Using the experimental pentad sequence prevalences, a computer code was developed to simulate chains with microstructures typical of each sample as a means to visually represent the copolymer blockiness with quantitative precision. Based on the microstructure and distribution of run lengths in these chains, the simulations revealed that the Blocky copolymers contain a high degree of blockiness. By comparing the run lengths in the simulated chains to the average number of styrene units in a crystalline segment of sPS (found by small-angle X-ray scattering), copolymer crystallizability was predicted. For the simulated Blocky B-21% (21 mol % Br-Sty) chain, the probability of randomly selecting a styrene unit in a crystallizable block was 25.8%, while that in the simulated Random R-18% was zero, in excellent agreement with the experimental crystallization behavior measured by differential scanning calorimetry. Additionally, these predictions confirmed that the simulated chains accurately represent the ensemble of chains in their respective copolymer samples. Furthermore, each simulated Blocky chain contained one or more long sPS blocks that paralleled the measured 38-40 styrene units spanning a crystalline segment within the sPS/CCl4 gel. This finding affirmed that the long sPS segments originated from the precise lamellar structure within the heterogeneous gel morphology (i.e., block length is correlated with lamellar thickness). Overall, the ability to tailor the copolymer microstructure through control of the semicrystalline gel morphology opens the door to synthesizing ordered copolymers by postpolymerization functionalization processes with unprecedented levels of compositional control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Assessment of Commercial Building Lighting as a Frequency Regulation Resource

This paper evaluates the potential for automated lighting control as a resource for frequency regulation of the electric grid system in the context of current energy policies, economic incentives, and technological trends. The growing prevalence of renewable energy has increased the need for ancillary services to maintain grid frequency and stability. While demand side resources like heating, ventilating, and air-conditioning systems, as well as water treatment plants are already evaluated as regulation service providers, the potential application to electrical lighting systems has largely been ignored. Yet, aggregations of lighting systems that are retrofitted with intelligent controls could conceivably contribute to frequency regulation services with little impact on user comfort. To further explore the feasibility of lighting potential, this paper explores (1) how lighting control systems are limited by visual comfort perception and acceptability, (2) how such limitations impact the performance of the lighting system as an frequency regulation resource, and (3) how the market potential of lighting systems as demand side resources compares in different regional transmission organizations. Finally, the impact of developing technologies on the application of lighting systems for frequency regulation is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
DOE OSTI2020

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. Finally, the ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Water vapor transmission rate measurement for moisture barriers using infrared imaging

Here, in this work, we demonstrate a spatially resolved imaging methodology for water vapor transmission rate (WVTR) testing that relies on quantified infrared characterization at water absorption bands. This technique is validated using a moisture barrier on a polymer substrate via calibration of the infrared image intensity with moisture content in the polymer substrate from images taken at different times. The method is compared to existing state of the art techniques such as membrane permeation measurement with coulometric phosphorous pentoxide sensor and optical calcium film testing. This fast, non-destructive and in-situ method enables defect visualization and shows the WVTR with a sensitivity limit of 5·10 –5 g/m 2 /day.

47 OTHER INSTRUMENTATION↗