The hydration of bentonite buffer material revealed by modeling analysis of a long-term in situ test
Not Available
Zheng, Liange↗
SEARCH · Engineering Papers
Results for “In situ model”
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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Integrating in situ environmental covariates in an American lobster catch model to improve impact assessment
The installation and operation of floating offshore wind power is an integral component of societal transition to renewable energy generation where fixed bottom offshore wind is not possible. However, it will cause unique ecosystem changes. To disentangle the effects of offshore wind installations from the concurrent effects of climate change and the fishing practices on commercially significant resources, we must develop detailed characterizations of the resources before development occurs. In the Gulf of Maine, American lobster is the most commercially and culturally important fishery. At the time of writing, this is the largest fishery by value in North America. Our understanding of baseline localized parameters (such as catch per trap at the spatial scale of individual turbines) should be informed by relationships to environmental, biological, and survey-specific functional drivers of catch. A more mechanistic understanding of catch will allow for strategic adjustments to Post- Deployment fishery responses and ultimately, the development of research- and commercial-scale floating offshore wind development. Here, we used survey data from the New England Aqua Ventus Pre-Construction Commercial Trapping Survey to develop Generalized Additive Models describing seasonal catch per trap for legal and sublegal lobsters. We found fall catch to be nearly twice that of spring. Bottom temperature dynamics could be used to predict catch, and the Fall survey was associated with a warmer temperature regime. By using analytical tools that incorporate environmental heterogeneity, we developed monitoring methods from preconstruction baseline data that will be applicable over the post-construction operating period of an offshore wind farm.
BACI↗
Characterization of in-situ ion irradiated Fe-21Cr-32Ni austenitic model alloy and alloy 800H at low
Explore the source record for details and available documents.
Ayanoglu, M.↗
Evaluation of elastic constants of M23C6 and M7C3 embedded in Fe-Cr-C alloys using in-situ XRD tensile test and self-consistent model
Not Available
Lee, Hoon (ORCID:0000000320040395)↗
Differentiable Preisach Modeling for Characterization and Optimization of Particle Accelerator Systems with Hysteresis
Future improvements in particle accelerator performance are predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high precision accelerators. Here, we combine the classical Preisach model of hysteresis with machine learning techniques to efficiently create nonparametric, high-fidelity models of arbitrary systems exhibiting hysteresis. We experimentally demonstrate how these methods can be used in situ, where a hysteresis model of an accelerator magnet is combined with a Bayesian statistical model of the beam response, allowing characterization of magnetic hysteresis solely from beam-based measurements. Finally, we explore how using these joint hysteresis-Bayesian statistical models allows us to overcome optimization performance limitations that arise when hysteresis effects are ignored.
43 PARTICLE ACCELERATORS↗
Modeling of deposit formation in mesoporous substrates via atomic layer deposition: Insights from pore‐scale simulation
Atomic layer deposition (ALD) has been a promising technique in fabricating membranes and tuning their properties with a precision at the atomic level. Fabrication of zeolitic imidazolate framework (ZIF) membranes using the ligand-induced permselectivation (LIPS) method starts with the formation of an oxide in a mesoporous substrate by ALD and is followed by the transformation of this oxide to ZIF using imidazolate vapor treatment. The objective of the ALD step is to block the mesopores with a thin deposit, that is, one with small penetration depth and small thickness on the top surface of the substrate. Unlike typical ALD on nonporous substrates, where all available sites react per ALD cycle, thin deposit formation in a mesoporous substrate requires that only a small fraction of the available deposition sites (i.e., close to the substrate surface) is subjected to ALD. Consequently, reactant dosing and duration of pulses are important process variables which, together with diffusion and reaction kinetics determine the deposit structure. Quantitative understanding of the interplay of these variables and phenomena can enable the rational design of ALD within mesoporous substrates. Here, we extend our earlier modeling effort considering the coexistence of ALD both inside the pores and on the external surface of the substrate. Finite-volume based models were developed and validated to simulate the two distinct modes of deposition cycle by cycle. The total mass uptake of the substrate with ALD cycles can be predicted using the combined surface deposition and pore reaction–diffusion models as affirmed by in situ quartz crystal microbalance experimental data. The ALD reactor model combined with the deposition model can accurately capture the number of ALD cycles needed to block the pores of the substrate. Based on the model, we designed a modified ALD process and examined the performance of the corresponding LIPS membranes. Furthermore, the present modeling work provides a new understanding of the deposit formation via ALD within mesoporous substrates for a variety of membrane applications.
atomic layer deposition↗
Computer vision models and advanced TEM imaging for microstructures of irradiated AM316 stainless steels
Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.
36 MATERIALS SCIENCE↗
The Spectral and Chemical Measurement of Pollutants on Snow Near South Pole, Antarctica
Remote sensing of light-absorbing particles (LAPs), or dark colored impurities, such as black carbon (BC) and dust on snow, is a key remaining challenge in cryospheric surface characterization and application to snow, ice, and climate models. We present a quantitative data set of in situ snow reflectance, measured and modeled albedo, and BC and trace element concentrations from clean to heavily fossil fuel emission contaminated snow near South Pole, Antarctica. Over 380 snow reflectance spectra (350-2500 nm) and 28 surface snow samples were collected at seven distinct sites in the austral summer season of 2014-2015. Snow samples were analyzed for BC concentration via a single particle soot photometer and for trace element concentration via an inductively coupled plasma mass spectrometer. Snow impurity concentrations ranged from 0.14 to 7000 part per billion (ppb) BC, 9.5 to 1200 ppb sulfur, 0.19 to 660 ppb iron, 0.013 to 1.9 ppb chromium, 0.13 to 120 ppb copper, 0.63 to 6.3 ppb zinc, 0.45 to 82 parts per trillion (ppt) arsenic, 0.0028 to 6.1 ppb cadmium, 0.062 to 22 ppb barium, and 0.0044 to 6.2 ppb lead. Broadband visible to shortwave infrared albedo ranged from 0.85 in pristine snow to 0.62 in contaminated snow. LAP radiative forcing, the enhanced surface absorption due to BC and trace elements, spanned from less than 1 W m(exp. -2) for clean snow to approximately 70 W m(exp. -2) for snow with high BC and trace element content. Measured snow reflectance differed from modeled snow albedo due to specific impurity-dependent absorption features, which we recommend be further studied and improved in snow albedo models.
pollution↗
Role of Pb/PbSO 4 Morphologies in Dynamic Charge Acceptance of Lead Acid Batteries
The dynamic charge acceptance (DCA) of negative pastes in lead acid batteries is studied using a continuum level model that is validated against in situ X-ray diffraction experiments. The model matches the current transient during a ten-minute, 2.4 V charge at 80% state of charge (SOC) using a distribution of PbSO 4 particles. <2% by volume of small, reactive particles is shown to provide ∼50% of the capacity during the first ten seconds. Increases in DCA with increasing carbon content are captured with increasing lead surface area. The increase in DCA when the 2.4 V charge is done after discharging to 80% SOC (vs charging to 80%) is captured by reducing the PbSO 4 particle sizes. It is hypothesized that the lower DCA after charging is caused by the absence of smaller particles that were consumed during the initial charge to 80% SOC, which is confirmed from X-ray measurements. Finally, two-particle case studies show that reducing the PbSO 4 particle size is more effective for increasing DCA than increasing the PbSO 4 dissolution rate alone. For a given lead surface area, the optimal DCA occurs when the particle sizes are large enough to avoid blocking the surface but small enough to provide dissolution/diffusion improvements.
Knehr, Kevin W. [Argonne National Laboratory (ANL)↗
The effect of water vapor on surface oxygen exchange kinetics of thin film (La,Sr)(Co,Fe)O 3-δ
Oxygen reduction and transport in electrochemical applications are strongly affected by the reactant gas composition, including impurities such as water. Understanding the relationship between oxygen reactivity and humidity is key for attaining stability and high efficiency of solid oxide fuel cells, which are especially attractive for transportation applications as low emission power generation devices. Both short- and long-term effects of moisture have previously been associated with use of ambient air. To better understand these effects, we study oxygen exchange kinetics at the cathode surface and the cathode/electrolyte interface using La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ (LSCF) epitaxial thin films as a model-surface system. In situ synchrotron X-ray techniques evaluate the oxygen reduction reaction (ORR) kinetics in response to environmental variables. The results suggest a clear enhancement of the ORR rate upon short term exposure of the perovskite-structured films to water. Finally, on the basis of these measurements, along with in situ X-ray characterization of Sr segregation and computational first-principles studies, we suggest a model for this increased ORR activity that may lead to further improvements by stabilizing active cathode surface configurations.
25 ENERGY STORAGE↗
Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters
One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.
Total suspended solids↗
Climate Information and Lessons Learned from the NASA Earth eXchange (NEX), Short-term Prediction Research and Transition Center (NASA SPoRT), and the NASA Regional Climate Model Evaluation System (RCMES)
Climate resilience can emerge from a combination of (1) climate projections that enable future-oriented planning and (2) contemporary observations that can be used for adaptation in the face of realized climate. NASA is uniquely positioned to provide global climate projections and satellite-based observations to users who must take a global perspective on climate risk (e.g., supply chain managers). Here, we will share the capabilities and experiences of three NASA projects. The first project, the NASA Earth eXchange (NEX) provides two downscaled climate projections, NEX-GDDP-CMIP6, a global 25 km resolution product, and NEX-DCP30-CMIP6, an 800 m resolution product covering the coterminous U.S. specifically designed for U.S. National Climate Assessment (NCA). NEX also offers land surface products that mirror the twice-daily products provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) but are derived from operational geostationary weather satellite data collected at 5-10 minutes intervals. The second project, the Short-term Prediction Research and Transition Center (SPoRT), was established in 2002 to transition unique NASA satellite products and capabilities to stakeholders to improve decision-making that benefits society. SPoRT provides products and capabilities derived from NASA observations and models to end users in government, academia, and the private sector. SPoRT translates NASA Earth science into actionable solutions reaching over 400 stakeholders across 80 organizations. Finally, the Regional Climate Model Evaluation System (RCMES) is designed to facilitate regional-scale evaluations of climate models by providing standardized access to a vast and comprehensive set of observations, such as satellite, in-situ, and reanalyses, and modeling resources such as CMIP & CORDEX. These three projects form part of the core capabilities at the NASA Centers deliver climate information under the new NASA Earth Science to Action (ES2A) strategy.
NEX↗
Automated classification of big X-ray diffraction data using deep learning models
Abstract In current in situ X-ray diffraction (XRD) techniques, data generation surpasses human analytical capabilities, potentially leading to the loss of insights. Automated techniques require human intervention, and lack the performance and adaptability required for material exploration. Given the critical need for high-throughput automated XRD pattern analysis, we present a generalized deep learning model to classify a diverse set of materials’ crystal systems and space groups. In our approach, we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties. We also employ an expedited learning technique to refine our model’s expertise to experimental conditions. In addition, we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making. We evaluate our models using experimental data, materials unseen in training, and altered cubic crystals, where we observe state-of-the-art performance and even greater advances in space group classification.
Chemistry↗
In-situ effect in cross-ply laminates under various loading conditions analyzed with hybrid macro/micro-scale computational models
In this article, multi-scale finite element analyses based on three-dimensional (3D) hybrid macro/micro-scale computational models subjected to various loading conditions are carried out to examine the in-situ effect imposed by the neighboring plies on the failure initiation and propagation of cross-ply laminates. A detailed comparative study on crack suppression mechanisms due to the effect of embedded laminar thickness and adjacent ply orientation is presented. Furthermore, we compare the results of in-situ transverse failure strain and strength between the computational models and analytical predictions. Good agreements are generally observed, indicating the constructed computational models are highly accurate to quantify the in-situ effect. Subsequently, empirical formulas for calculating the in-situ strengths as a function of embedded ply thickness and different ply angle between embedded and adjacent plies are developed, during which several material parameters are obtained using a reverse fitting method. Finally, a new set of failure criteria for σ 22 -τ 12 , σ 22 -τ 23 , and σ 11 -τ 12 accounting for the in-situ strengths are proposed to predict laminated composites failure under multi-axial stress states. This study demonstrates an effective and efficient computational technique towards the accurate prediction of the failure behaviors and strengths of cross-ply laminates by including the in-situ effects.
36 MATERIALS SCIENCE↗
Survey of in-situ and remote sensing methods for soil moisture determination
General methods for determining the moisture content in the surface layers of the soil based on in situ or point measurements, soil water models and remote sensing observations are surveyed. In situ methods described include gravimetric techniques, nuclear techniques based on neutron scattering or gamma-ray attenuation, electromagnetic techniques, tensiometric techniques and hygrometric techniques. Soil water models based on column mass balance treat soil moisture contents as a result of meteorological inputs (precipitation, runoff, subsurface flow) and demands (evaporation, transpiration, percolation). The remote sensing approaches are based on measurements of the diurnal range of surface temperature and the crop canopy temperature in the thermal infrared, measurements of the radar backscattering coefficient in the microwave region, and measurements of microwave emission or brightness temperature. Advantages and disadvantages of the various methods are pointed out, and it is concluded that a successful monitoring system must incorporate all of the approaches considered.
Schmugge, T. J.↗
Satellite estimates of downwelling longwave irradiance at the surface during FIFE
Two methods of estimating the downward longwave flux at the earth's surface from satellite measurements are evaluated by comparing estimate calculations to in situ measurements. Attention is given to the low degree of accuracy inherent in longwave irradiance measurements and the subsequent difficulty in validating radiative transfer models. The fluxes are determined by using the models by Morcrette et al. (1986) and by Gupta (1989), and the models are found to be highly correlated for the same atmospheric profiles although an inherent bias is noted. The differences between the model computations and in situ measurements are found to be of the same order of magnitude as the differences among the measurements themselves. The difficulty of quantifying the accuracy of radiative transfer models is related to the paucity of reliable surface measurements.
Breon, Francois-Marie↗
Self-diagnosis of model suitability for continuous measurements of stream-dissolved organic carbon derived from in situ UV–visible spectroscopy
Application of high-frequency monitoring of dissolved organic carbon (DOC) is difficult in instances where training datasets are challenging to develop (e.g., remote locations) and the relationship between optical features and DOC concentration changes due to environmental or landscape shifts (e.g., climate or land-use change). We developed and compared three partial least squares (PLS) models using in situ water level measurements, conductivity, and UV–Vis spectral attenuation to predict DOC. Two site-specific models were developed using data from a hillslope-dominated forest or a low-relief wetland-pond-dominated stream catchment. The third model, using data from both sites, exhibited the best performance (DOC range = 4–15.5 mg C L −1 , mean = 8.38 mg C L −1 , training RMSE = 0.34 mg C L −1 , internal validation RMSE = 0.50 mg C L −1 , external validation RMSE = 2.43 mg C L −1 ). We further demonstrate using PLS model statistics to monitor performance and elucidate when and how models should be updated. These statistics, Hotelling's T 2 and squared prediction errors, are useful consistency checks for the predictions made and detect underlying inconsistencies that, if undetected, can reduce the robustness of DOC prediction. For example, via the T 2 statistic, we identified the summer–autumn transition as a period when DOC composition differed from what was represented in the training dataset. We also determined that elevated SUVA 254 values contributed to the overall bias observed in predictions made during the subsequent year as part of the external validation. This enabled the application of a bias correction that reduced the RMSE from 2.43 to 0.89 mg C L −1 . The method presented here could be applied to future monitoring programs enabling model updates to monitor DOC fluxes accurately from optical datasets (e.g., attenuance or fluorescence) in the face of developing datasets in remote locations or environmental change. In conclusion, implementation of this approach may also identify possible regime shifts or landscape and hydrologic change associated with climate and other environmental changes relevant to terrestrial to aquatic fluxes.
UV-VIS spectroscopy↗