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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.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Wave energy resource characterization employing joint distributions in frequency-direction-time domain

Joint and marginal distributions in the frequency, direction, and time domain are employed to demonstrate their value for wave energy resource characterization when full spectra are available. Insights gained through analysis of these distributions support wave energy converter concept design, operation and maintenance. Spatial trends in the wave energy resource and contributing wave energy systems along the continental shelf of the West Coast of the United States are investigated using the most recent two-dimensional wave spectra measurements at four buoys over an eleven year period (2008 to 2018). Resource hot spots and dominant resolved energy resource bands in the frequency-direction-time domain are delineated. Resource attributes, including frequency and directional spreading, and seasonal variability, are characterized using joint distributions and marginal distributions of wave power spectra. North Pacific westerly swells in the winter season, augmented by Aleutian low-pressure southwesterly swells, are the principal suppliers of the dominant resource and main drivers influencing resource attributes. In conclusion, the modification of these systems southward, especially the North Pacific westerly swells, explains the observed spatial resource trends. The dominant resource wave period shifts two seconds to higher wave periods, thirty degrees in the dominant direction band to a more northward orientation, and forward by one month.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

A-type antiferromagnetic order in semiconducting EuMg 2 Sb 2 single crystals

Eu-based Zintl-phase materials EuA 2 Pn 2 (A = Mg, In, Cd, Zn; Pn = Bi, Sb, As, P) have generated significant recent interest owing to the complex interplay of magnetism and band topology. Here, we investigated the crystallographic, magnetic, and electronic properties of the layered Zintlphase single crystals of EuMg 2 Sb 2 with the trigonal CaAl 2 Si 2 crystal structure (space group $P\bar{3}m1$). Electrical resistivity measurements complemented with angle-resolved photoemission spectroscopy (ARPES) studies and density functional theory (DFT) calculations find an activated behavior with intrinsic conductivity at high temperatures indicating a semiconducting electronic ground state with a narrow energy gap of 370 meV. Magnetic susceptibility and zero-field heat capacity measurements indicate that the compound undergoes antiferromagnetic (AFM) ordering at the Néel temperature T N = 8.0(2) K. Here, zero-field neutron-diffraction measurements reveal that the AFM ordering is A-type where the Eu spins (Eu 2+ , S = 7/2) arranged in ab-plane layers are aligned ferromagnetically in the ab plane and the Eu spins in adjacent layers are aligned antiferromagnetically. Eu-moment reorientation within the ab planes in the trigonal AFM domains associated with a very weak inplane magnetic anisotropy is also evident below T N at low fields < 0.05 T. Although isostructural semimetallic EuMg 2 Bi 2 is reported to host Dirac surface states, the observation of narrow-gap semiconducting behavior in EuMg 2 Sb 2 implies a strong role of spin-orbit coupling (SOC) in tuning the electronic states of these materials. Our DFT studies also suggest, besides the SOC, the more electronegative and smaller Sb than Bi shifts the low-lying conduction bands along the Γ-A direction to higher energy, resulting in an indirect bulk band gap between the Γ and M points for EuMg 2 Sb 2 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Reaction Pathways over ZnZrO 2 -Based Catalysts and Catalytic Sorbents

Reactive capture and conversion (RCC) is a process intensification approach that integrates CO 2 capture and hydrogenation within a single unit, removing the CO 2 purification and storage steps of traditional process flow schemes. This alters the catalytic step from a traditional steady-state (SS) flow process to a transient capture and conversion cycle, which could lead to product distributions distinct from those observed in conventional SS experiments. Such differences are investigated in the combined capture and hydrogenation of carbon dioxide to methanol over a ZnZrO 2 catalyst and a ZnZrO 2 + NaNO 3 /Mg 3 AlO x catalytic sorbent (CS) using fixed-bed kinetic measurements, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and steady-state isotopic transient kinetic analysis-DRIFTS (SSITKA-DRIFTS). Under SS conditions, ZnZrO 2 produced methanol through sequential hydrogenation of HCOO* and CH 3 O* intermediates. On the contrary, CO was attributed primarily to CO 2 dissociation at oxygen vacancies, as supported by isotopic shifts and measured reaction orders. For the CS, isotopic switching experiments suggested that monodentate carbonate species (CO 3 2− , abbreviated as m-CO 3 2− ) act as active intermediates that can be hydrogenated to HCOO* and subsequently to CH 3 O. Under RCC conditions, in situ DRIFTS and isotopic experiments reveal that m-CO 3 2− species formed during the CO 2 capture step follow two competing routes upon H 2 exposure: (i) direct hydrogenation to methane on the sorbent domain or (ii) migration of m-CO 3 2− to the ZnZrO 2 domain, where they are hydrogenated to methanol through the HCOO pathway. Overall, RCC enables carbonate hydrogenation routes not observed under SS cofeed conditions. Thus, the reaction pathways and rates during RCC can be different from operation under conventional SS conditions, and the product distribution is determined here by competition between carbonate hydrogenation on sorbent sites and migration to ZnZrO 2 for methanol synthesis.

CCUS↗
DOE OSTI2023

Quantum efficiency enhancement in simulated nanostructured negative electron affinity GaAs photocathodes

We report nanostructured negative electron affinity GaAs photocathodes for a polarized electron source are studied using finite difference time domain optical simulation. The structures studied are nanosquare columns, truncated nanocones, and truncated nanopyramids. Mie-type resonances in the 700–800 nm waveband, suitable for generation of polarized electrons, are identified. At resonance wavelengths, the nanostructures can absorb up to 99% of the incident light. For nanosquare columns and truncated nanocones, the maximum quantum efficiency (QE) at 780 nm obtained from simulation is 27%, whereas for simulated nanopyramids, the QE is ~21%. The high photocathode quantum efficiency is due to the shift of Mie resonance toward the longer wavelength, leading to increased light absorption. The field profile distribution shows the excitation of dipole and quadrupole modes within the nanostructures at resonant frequencies. This leads to enhanced photoabsorption and photoelectron generation closer to emission surfaces than for a flat photocathode. The enhanced photoabsorption and reduced electron transport distance for the nanostructured photocathode enhance its QE compared to that for the flat surface wafer.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Photo-Switching of Protein Dynamical Collectivity

We examine changes in the picosecond structural dynamics with irreversible photobleaching of red fluorescent proteins (RFP) mCherry, mOrange2 and TagRFP-T. Measurements of the protein dynamical transition using terahertz time-domain spectroscopy show in all cases an increase in the turn-on temperature in the bleached state. The result is surprising given that there is little change in the protein surface, and thus, the solvent dynamics held responsible for the transition should not change. A spectral analysis of the measurements guided by quasiharmonic calculations of the protein absorbance reveals that indeed the solvent dynamical turn-on temperature is independent of the thermal stability/photostate however the protein dynamical turn-on temperature shifts to higher temperatures. This is the first demonstration of switching the protein dynamical turn-on temperature with protein functional state. The observed shift in protein dynamical turn-on temperature relative to the solvent indicates an increase in the required mobile waters necessary for the protein picosecond motions, that is, these motions are more collective. Melting-point measurements reveal that the photobleached state is more thermally stable, and structural analysis of related RFP’s shows that there is an increase in internal water channels as well as a more uniform atomic root mean squared displacement. These observations are consistent with previous suggestions that water channels form with extended light excitation providing O2 access to the chromophore and subsequent fluorescence loss. We report that these same channels increase internal coupling enhancing thermal stability and collectivity of the picosecond protein motions. The terahertz spectroscopic characterization of the protein and solvent dynamical onsets can be applied generally to measure changes in collectivity of protein motions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Understanding the diversity of the West African monsoon system change projected by CORDEX-CORE regional climate models

Understanding real uncertainties in projections is needed to support actions of decision and policy makers. In this study, two state-of-the-art regional climate models with ~ 25 km horizontal resolution forced with three different global climate models are employed to investigate the response of the West African Monsoon (WAM) system under the low (RCP2.6) and high (RCP8.5) emission scenarios. We make a step towards assessing the plausibility of rainfall change (ΔPr) patterns based on the models’ ability to realistically represent the WAM system under the historical period, and by analyzing changes in mechanisms associated with ΔPr patterns. Under global warming, experiments exhibit diverse ΔPr patterns underpinned by different combinations of mechanisms operating simultaneously. A dipole-like surface pressure change between the Sahara Desert and the Guinea Coast appears to be a differentiating factor between experiments featuring homogeneous or increased rainfall over all or a part of the Sahel from those simulating heterogeneous or reduced rainfall over the same domain. This dipole acts by modulating the amount and the extent of deepening of the monsoon flux inland, and hence the latitudinal positioning of the monsoon convective system. This process contributes to moistening the whole or central and eastern Sahel in some experiments while drying the Guinea Coast. The West African Westerly Jet (WAWJ) is responsible for strengthening and moistening the western Sahel under RCP2.6. However, the WAMJ becomes much stronger under RCP8.5 and may have contributed to the drying of the western Sahel by shifting moisture eastwards in addition to the mid-tropospheric moisture divergence through enhanced African Easterly Jet. Furthermore, for experiments expecting wetting (drying) of the Sahel, the strengthening (weakening) of the tropical easterly jet may have contributed to the projected intensification (reduction) of WAM rainfall by favoring uplift (downlift) above 600 hPa. It also emerges that the ΔPr patterns from REMO2015 experiments are likely more plausible than those from RegCM4-v7. The impacts of strong warming on the regional hydrological cycle are further investigated and we found that changes gradually intensify with the level of radiative forcing, highlighting the importance of mitigating warming as recommended by the Paris agreement.

CORDEX-CORE↗
DOE OSTI2022

Modified gravitational wave propagation with higher modes and its degeneracies with lensing

Low-energy alternatives to General Relativity (GR) generically modify the phase of gravitational waves (GWs) during their propagation. As detector sensitivities increase, it becomes key to understand how these modifications affect the GW higher modes and to disentangle possible degeneracies with astrophysical phenomena. We apply a general formalism — the WKB approach — for solving analytically wave propagation in the spatial domain with a modified dispersion relation (MDR). We compare this WKB approach to applying a stationary phase approximation (SPA) in the temporal domain with time delays associated to the group or particle velocity. To this end, we extend the SPA to generic signals with higher modes, keeping careful track of reference phases and arrival times. We find that the WKB approach coincides with the SPA using the group velocity, in agreement with the principles of wave propagation. We then explore the degeneracies between a GW propagation with an MDR and a strongly-lensed GW in GR, since the latter can introduce a frequency-independent phase shift which is not degenerate with source parameters in the presence of higher modes. We find that for a particular MDR there is an exact degeneracy for wave propagation, unlike with the SPA for particle propagation. For the other cases, we search for the values of the MDR parameters that minimize the χ 2 and conclude that strongly-lensed GR GWs could be misinterpreted as GWs in modified gravity. As a result, future MDR constraints with higher mode GWs should include the possibility of frequency-independent phase shifts, allowing for the identification of modified gravity and strong lensing distortions at the same time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Distributed Fiber Sensors With High Spatial Resolution in Extreme Radiation Environments in Nuclear Reactor Cores

This paper is a comprehensive experimental report on the neutron radiation effects of distributed optical fiber sensors with enhanced Rayleigh scattering profiles in an in-pile environment. Femtosecond laser direct writing was used to inscribe Type-II modifications in standard telecom fibers and radiation-hardened fibers with fluorine-doped cores. Rayleigh backscattering signals were enhanced for continuous 1.5 m. In-pile lead-out sensors tests were carried out at the MIT Research Reactor for two months, which was operated at a nominal power of 5.7 MW with fast neutron (>0.1 MeV) flux of 1.29 × 1014 n/cm2/s and an in-core temperature of up to 560°C. Using the Optical Frequency Domain Reflectometry technique, the backscattering profiles of fiber sensors were interrogated with a 3-cm spatial resolution to monitor the temperature profile of the reactor. Results show that laser inscribed Type-II modifications in the form of nanogratings are highly stable against extreme temperature and ionizing radiation. Both standard telecom fibers and radiation-hardened fibers with laser-enhanced Rayleigh profiles can continuously perform distributed temperature measurements over the entire duration of the in-pile testing. Temperature coefficients of sensors and spectral shift quality were studied as functions of total radiation fluence. To the best of our knowledge, we present for the first time, the temperature profile of an operating nuclear reactor core with 3-cm spatial resolution, enabled by distributed fiber sensors with laser-enhanced Rayleigh scattering profiles. The high spatial resolution measurements can provide valuable data for the design and validation of digital twin and virtual reality of nuclear energy systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Crystallization Mechanisms of Poly(vinylidene Fluoride- co -chlorotrifluoroethylene): Nucleation Transitions, Growth Kinetics, and Microstructure Evolution

The crystallization kinetics of FK-800, a commercial semicrystalline copolymer of chlorotrifluoroethylene and vinylidene fluoride (poly(CTFE-co-VDF)), dictate its microstructure and functional performance. Crystallization occurs between the glass transition (T g ≈ 31 °C) and melting point (T m ≈ 110 °C), with grain boundaries playing a key role in applications such as memristors, where they regulate filament growth. This study integrates in situ atomic force microscopy (AFM), grazing-incidence wide-angle X-ray scattering (GIWAXS), and differential scanning calorimetry (DSC) to characterize nucleation, growth, and morphology across the full crystallization window. Hot-stage AFM reveals a transition from homogeneous to heterogeneous nucleation near 45 °C and a shift from reaction-limited to diffusion-limited growth above 60 °C. Kinetics are analyzed using Lauritzen–Hoffman, Turnbull–Fisher, and Avrami models to extract surface free energies and activation barriers. A multiscale modeling framework is developed in which nucleation densities and growth rates measured by AFM are used to reconstruct Avrami kinetics, which are then validated against coverage data, and extrapolated to predict bulk crystallization behavior observed by DSC. GIWAXS confirms a primarily flat-on chain orientation at all temperatures, linking orientation to domain morphology. This integrative approach quantitatively connects nanoscale crystallization dynamics with macroscopic phase evolution, establishing a quantitative framework for modeling crystallization across time, temperature, and length scales.

Crystallization↗
DOE OSTI2020

Local atomic structure and Ni nanophase segregation in Zn 1-x Ni x S thin films

Here, the local atomic structure of Zn 1-x Ni x S thin films was investigated using X-ray absorption spectroscopy. The films were grown using RF-sputtering at atomic concentrations x = 0.00, x = 0.04, x = 0.08 and x = 0.14. X-ray diffraction shows that the lattice parameter contracts with increasing Ni concentration, consistent with the smaller atomic radii of Ni compared to that one of Zn. Optical absorption reveals a reduction of the bandgap as Ni concentration increases, except for sample x = 0.14 where a complex behavior is observed. Results from X-ray absorption near edge structure (XANES) spectroscopy indicate a shift in the valence state of Ni for the x = 0.14 sample. The analysis of the extended X-ray absorption fine structure (EXAFS) spectra indicate a contraction of ~0.7 Å in the Ni–S bond distance when compared to the Zn–S bond length, which generates local lattice distortions and an increment of the static disorder as the Ni concentration increases. The EXAFS results for sample x = 0.14 show the presence of a Zn 1-x Ni x S phase and a nanoscopic metallic Ni phase with domain sizes below the diffraction limit. These results reveal that the local atomic structure differs in a significant manner from the average crystalline structure, implying its importance for the determination of the electronic properties of this material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Impact of Lateral Groundwater Flow and Subsurface Lower Boundary Conditions on Atmospheric Boundary Layer Development over Complex Terrain

Credible soil moisture redistribution schemes are essential to meteorological models, as lower boundary moisture influences the balance of surface turbulent fluxes and atmospheric boundary layer (ABL) development. While land surface models (LSMs) have vastly improved in their hydrologic representation, several commonly held assumptions, such as free-draining lower boundary, one-dimensional moisture flux, and lack of groundwater representation, can bias the terrestrial water balance. This study explores the impact of LSM hydrology representation on ABL development in the Weather Research and Forecasting (WRF) meteorological model. The results of summertime WRF simulations with Noah LSM, characterized by 2-m-thick soil and one-dimensional flow, are shown for a domain in the Colorado Rocky Mountain headwaters region. A reference WRF simulation is compared to 1) the same model with soil moisture initialized by the hydrologic model ParFlow; 2) a deep, free-draining simulation; and 3) WRF coupled to ParFlow, a three-dimensional, integrated groundwater-surface water model. Results show that both lateral transport of groundwater and the rate of drainage from the lower soil layer can weaken or reverse the coupling strength between evaporative fraction and ABL over a 5-month summer period. The resulting shifts in low-level moist convection in river valleys and thermally driven airflows yield strengthened anabatic upslope winds and perturbations to regional precipitation.

Forrester, Mary M.↗
DOE OSTI2023

The structure of the high-affinity nickel-binding site in the Ni,Zn-HypA•UreE2 complex

Abstract The maturation pathway for the nickel-dependent enzyme urease utilizes the protein UreE as a metallochaperone to supply Ni(II) ions. In Helicobacter pylori urease maturation also requires HypA and HypB, accessory proteins that are commonly associated with hydrogenase maturation. Herein we report on the characterization of a protein complex formed between HypA and the UreE2 dimer. Nuclear magnetic resonance (NMR) coupled with molecular modelling show that the protein complex apo, Zn-HypA•UreE2, forms between the rigorously conserved Met-His-Glu (MHE motif) Ni-binding N-terminal sequence of HypA and the two conserved His102A and His102B located at the dimer interface of UreE2. This complex forms in the absence of Ni(II) and is supported by extensive protein contacts that include the use of the C-terminal sequences of UreE2 to form additional strands of β-sheet with the Ni-binding domain of HypA. The Ni-binding properties of apo, Zn-HypA•UreE2 and the component proteins were investigated by isothermal titration calorimetry using a global fitting strategy that included all of the relevant equilibria, and show that the Ni,Zn-HypA•UreE2 complex contains a single Ni(II)-binding site with a sub-nanomolar KD. The structural features of this novel Ni(II) site were elucidated using proteins produced with specifically deuterated amino acids, protein point mutations, and the analyses of X-ray absorption spectroscopy, hyperfine shifted NMR features, as well as molecular modeling coupled with quantum-mechanical calculations. The results show that the complex contains a six-coordinate, high-spin Ni(II) site with ligands provided by both component proteins.

Zambelli, Barbara (ORCID:0000000238760051)↗
DOE OSTI2023

Exceptional enhancement of mechanical properties in high-entropy alloys via thermodynamically guided local chemical ordering

Understanding the local chemical ordering propensity in random solid solutions, and tailoring its strength, can guide the design and discovery of complex, paradigm-shifting multicomponent alloys. First, we present a simple thermodynamic framework, based solely on binary enthalpies of mixing, to select optimal alloying elements to control the nature and extent of chemical ordering in high-entropy alloys (HEAs). Next, we couple high-resolution electron microscopy, atom probe tomography, hybrid Monte-Carlo, special quasirandom structures, and density functional theory calculations to demonstrate how controlled additions of Al and Ti and subsequent annealing drive chemical ordering in nearly random equiatomic face-centered cubic CoFeNi solid solution. We establish that short-range ordered domains, the precursors of long-range ordered precipitates, inform mechanical properties. Specifically, a progressively increasing local order boosts the tensile yield strengths of the parent CoFeNi alloy by a factor of four while also substantially improving ductility, which breaks the so-called strength–ductility paradox. Finally, we validate the generality of our approach by predicting and demonstrating that controlled additions of Al, which has large negative enthalpies of mixing with the constituent elements of another nearly random body-centered cubic refractory NbTaTi HEA, also introduces chemical ordering and enhances mechanical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring

Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

03 NATURAL GAS↗
DOE OSTI2023

Lifetime and Degradation Study of Poly(Methyl Methacrylate) via a Data-Driven Study Protocol Approach

We report to optimize and extend the service life of polymeric materials in outdoor environments, a domain knowledge-based and data-driven approach was utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms, and rates under various stepwise accelerated exposure conditions. Six formulations of poly(methyl methacrylate) (PMMA) with different combinations of stabilizing additives, including one unstabilized formulation, were exposed in three accelerated weathering conditions. Degradation was dependent on wavelength as samples in UV light at 340 nm (UVA) exposure showed the most yellowing. The unstabilized PMMA formulation showed much higher yellowness index values (59.5) than stabilized PMMA formulations (2–12). Urbach edge analysis shows a shift toward longer wavelength from 285 to 500 nm with increasing exposure time and an increased absorbance around 400 nm of visible region as the unstabilized samples increase in yellowing. The degradation mechanisms of PMMA were tracked using induced absorbance to dose at specific wavelengths that correspond to known degradation mechanisms. The degradation pathway of PMMA was modeled in a framework using network structural equation modeling (netSEM). netSEM showed changes in degradation pathway as PMMA transition stages of degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Flow-based likelihoods for non-Gaussian inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

79 ASTRONOMY AND ASTROPHYSICS↗