Engineering topics

Forland, Brenda M.

Publications and source records attributed to Forland, Brenda M..

8 records

DOE OSTI2024

A Discrete Event Simulation Approach to Nuclear Arms Enterprise Modeling and Analysis

We overview a discrete event simulation of an arms control enterprise developed to familiarize individuals with the challenges of verification and monitoring at the enterprise level. We will review the design of the simulation applied to a notional country’s nuclear arms enterprise. This simulation models how these nuclear weapons move over their lifetime from deployment, storage, maintenance, and dismantlement. With this output information, we then characterize the “ground truth” of the enterprise. Next, based on these data, we built a sophisticated analysis engine designed to provide treaty monitoring and verification information to support partner country inspectors and their evaluation. This tool was designed to be used as a game, enabling teams to compete on developing the best inspection plan to determine if the host country is complying with the conceptual treaty developed for this effort.

Pulliam, Rachel M.↗

DOE OSTI2024

Data-Driven Invertible Neural Surrogates of Atmospheric Transmission

We present Data-Driven Invertible Neural Surrogates of Atmospheric transmission, or DINSAT. DINSAT is a novel framework for inferring an atmospheric transmission profile from a spectral scene. This framework leverages a lightweight, physics-based simulator that is automatically tuned -- by virtue of autodifferentiation and differentiable programming -- to construct a surrogate atmospheric profile to model the observed data. The framework has utility in (i) performing atmospheric correction, (ii) recasting spectral data between various modalities (e.g. radiance and reflectance at the surface and at the sensor), and (iii) inferring atmospheric transmission profiles, such as absorbing bands and their relative magnitudes. We demonstrate the utility of these methods by performing a canonical atmospheric correction task for the purposes of further analysis - in this case, target detection within a scene.

Koch, James V.↗

DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗

DOE OSTI2024

Optimal spectral resolution for solids and liquids using FT and other infrared spectrometers: How much resolution do you really need?

In this study we investigate the possibility of using spectral resolutions for infrared measurements of solids and liquids that are not powers of two, e.g. are not at 1, 2, 4, 8, or 16 cm-1 resolution. In almost all reported literature of the last fifty years the resolution used to record for a Fourier transform infrared spectrum has been a power of two. This stems from the fact that 1) the Cooley-Tukey algorithm used to compute such a transform was constructed to use only powers of two and was also driven by 2) the fact that the computing horsepower required to compute the Fourier transform increases as N?log?_2 (N), where N is the number of points in the interferogram (spectrum). For typical spectra, however, the CPU time is no longer a consideration. Our study is based on both liquid and solid spectra, all of which were recorded at 2 cm-1 resolution. There were at total of 70 solids spectra representing 2,472 spectral peaks and 61 liquids spectra (1,765 spectral peaks), each peak being inspected for being singlet / multiplet in nature. Of the 1,765 liquid bands examined, only 27 had widths less than 5 cm-1. Of the 2,472 solid bands examined, only 39 peaks have widths less than 5 cm-1. For liquids, the mean peak width is 24.7 cm-1 but the median peak width is 13.7 cm-1, and, similarly, for solids, the mean peak width is 22.2 cm-1 but the median peak width is 11.2 cm-1. In both cases, solids and liquids, a skewed peak widths distribution was observed, the peak of the distribution representing narrower bands in the 7 to 9 cm-1 FWHM range but displaying a long tail to the very broad bands, with some displaying spectral widths of 100 cm-1 or more. Because one of the most important criteria for successful instrumental design in IR spectroscopy is the spectral resolution, the data were further analyzed showing that a value to resolve 95% of all bands is 5.7 cm-1 for liquids and 5.3 cm-1 for solids; such a resolution would capture the native linewidth (no instrumental broadening) of 95% of all the solids and liquid bands, respectively. Based on the present results we suggest that, when accounting only for intrinsic linewidths an optimized resolution of 6.0 cm-1 will capture 91% of all condensed-phase bands for IR detection of chemical, mineral, and biological materials.

Forland, Brenda M.↗

DOE OSTI2024

A method for generating quantitative vapor-phase infrared spectra of solids: results for phenol, camphor, menthol, syringol, dicyclopentadiene and naphthalene

Here, a method is presented to generate quantitative vapor-phase infrared spectra from substances that naturally occur as solids with moderate volatility. The solid is gravimetrically dissolved into a solvent that has few infrared spectral features, typically CS 2 and CCl 4 separately. The solution is flowed at a constant rate from a linearly pumped syringe into a metered stream of nitrogen carrier gas regulated by a mass flow controller. The analyte/solvent mix is flash vaporized by volatilizing the solution across a heated stainless-steel surface as it emanates from the syringe tip. The N 2 gas-solution mixture is flowed into a long-path White cell thermostatted at a desired temperature, the long optical path compensating for the modest analyte mixing ratio. A composite spectrum is generated from typically ten or more 760-Torr pressure-broadened spectra over the 600 to 6500 cm -1 spectral range at 0.1 cm -1 spectral resolution. The solid analytes reported here using this novel technique include dicyclopentadiene, menthol, syringol, phenol, camphor, and naphthalene.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Optimal Spectral Resolution for Infrared Studies of Solids and Liquids

Due to a legacy originating in the limited capability of early computers, the spectroscopic resolution used in Fourier transform infrared spectroscopy and other systems has largely been implemented using only powers of two for more than 50 years. In this study, we investigate debunking the spectroscopic lore of, e.g., using only 2, 4, 8, or 16 cm −1 resolution and determine the optimal resolution in terms of both (i) a desired signal-to-noise ratio and (ii) efficient use of acquisition time. The study is facilitated by the availability of solids and liquids reference spectral data recorded at 2.0 cm −1 resolution and is based on an examination in the 4000–400 cm −1 range of 61 liquids and 70 solids spectra, with a total analysis of 4237 peaks, each of which was also examined for being singlet/multiplet in nature. Of the 1765 liquid bands examined, only 27 had widths <5 cm −1 . Of the 2472 solid bands examined, only 39 peaks have widths <5 cm −1 . For both the liquid and solid bands, a skewed distribution of peak widths was observed: For liquids, the mean peak width was 24.7 cm −1 but the median peak width was 13.7 cm −1 , and, similarly, for solids, the mean peak width was 22.2 cm −1 but the median peak width was 11.2 cm −1 . While recognizing other studies may differ in scope and limiting the analysis to only room temperature data, we have found that a resolution to resolve 95% of all bands is 5.7 cm −1 for liquids and 5.3 cm −1 for solids; such a resolution would capture the native linewidth (not accounting for instrumental broadening) for 95% of all the solids and liquid bands, respectively. After decades of measuring liquids and solids at 4, 8, or 16 cm −1 resolution, we suggest that, when accounting only for intrinsic linewidths, an optimized resolution of 6.0 cm −1 will capture 91% of all condensed-phase bands, i.e., broadening of only 9% of the narrowest of bands, but yielding a large gain in signal-to-noise with minimal loss of specificity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Using synthetic infrared spectra derived from n/k optical constants for standoff detection of chemical deposits

We report results from a recent field experiment to test the validity of using physics-based synthetic infrared spectra to serve as endmembers in a spectral database targeted at chemical deposits. Specifically, the optical constants n and k, (the real and imaginary part of the refractive index) were used to first model infrared reflectance spectra for different thicknesses of chemical layers (e.g. acetaminophen, methylphosphonic acid – MPA, etc.) on various conducting and insulating substrates such as aluminum, wood, and glass. In the experimental portion of the research, thin films of the solid and liquid analytes were deposited onto such substrates to form micron-thick layers of the analytes at different thicknesses: Standoff data from an imaging instrument were then recorded and analyzed to not only identify the different analytes, but also quantify the layer/deposit thickness. To gauge success, the detection results using the synthetic data were compared to the results from hemispherical reflectance (HRF) spectra that were collected for the same sample planchets measured in the field via standoff methods. Preliminary results indicate good agreement between the synthetic reference data as compared to the lab-measured HRF data in terms of their ability to quantitatively reduce longwave infrared data. Specifically, modeled IR spectra for acetaminophen on an aluminum planchet at various thicknesses (1, 2, 5, 10, 15, and 20 µm) were synthesized and compared with standoff field reflectance data as well as HRF laboratory reflectance spectra for two samples: a 5.2 µm- and 12.8 µm-thick layer of acetaminophen on aluminum. Using a first-order approximation, analysis of the field data estimates the thicknesses of the samples to be 2 and 10 µm for the two samples, respectively, while the HRF laboratory data yields thickness estimates of between 5-10 µm and 10 µm, respectively. Both yield reasonable estimates, with the uncertainty most likely due to factors yet to be accounted for in the synthetic spectra such as light scattering.

complex index of refraction, optical constants, hy↗

DOE OSTI2023

A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images

Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.

Koch, James V.↗