Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “interpreted”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

UV-POSIT: Web-Based Tools for Rapid and Facile Structural Interpretation of Ultraviolet Photodissociation (UVPD) Mass Spectra

UV-POSIT (Ultraviolet Photodissociation Online Structure Interrogation Tools) is a suite of web-based tools designed to facilitate the rapid interpretation of data from native mass spectrometry experiments making use of 193 nm ultraviolet photodissociation (UVPD). The suite includes four separate utilities which assist in the calculation of fragment ion abundances as a function of backbone cleavage sites and sequence position; the localization of charge sites in intact proteins; the calculation of hydrogen elimination propensity for a-type fragment ions; and mass-offset searching of UVPD spectra to identify unknown modifications and assess false positive fragment identifications. UV-POSIT is implemented as a Python/Flask web application hosted at http://uv-posit.cm.utexas.edu . UV-POSIT is available under the MIT license, and the source code is available at https://github.com/jarosenb/UV-POSIT . .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An atomistic survey of shear coupling in asymmetric tilt grain boundaries and interpretation using the disconnections framework

Grain Boundaries (GB) play an important role in determining the behavior of polycrystalline materials. While the mechanisms of motion and associated shear response for symmetric tilt grain boundaries (STGBs) are well studied, the same is not true for asymmetric tilt grain boundaries (ATGBs) despite their greater prevalence in polycrystals. Here, this study aims to investigate the shear response of a large collection of asymmetric tilt grain boundaries (ATGBs) using molecular dynamics (MD) simulations and interpret the data using a discrete disconnections model that works remarkably well for STGBs. MD simulations of shear-driven ATGBs show that the plastic shear (shear coupling factor) in the region swept by a GB exhibits a complex dependence on the inclination angle, and this dependence changes with the misorientation of the GB. In addition, the shear response was observed to be highly sensitive to the applied shear rate and temperature. Recognizing the spatial and temporal scale limitations of MD simulations, we extended the discrete disconnections mesoscale model of Khateret al. (2012) to calculate the nucleation barriers of disconnection modes and predict the effective shear coupling of an ATGB. We observed that the mesoscale model’s predictions of the shear coupling factor of ATGBs do not agree with those observed in MD simulations. Finally, we examine the hypotheses of our mesoscale model that contribute to disagreements between MD simulations and the mesoscale model and propose improvements to the mesoscale model for future work.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Concerning Mössbauer experiments in a rotating system and their physical interpretation

We shortly review different attempts to interpret the results of Mössbauer rotor experiments in a rotating system and particularly we show that the latest work on this subject by J. Iovane and E. Benedetto (2019), which claims that the outcomes of these experiments can supposedly be explained via “desynchronization of clocks” in the rotating frame and in the laboratory frame, is inapplicable to all of the Mössbauer rotor experiments performed up to date and thus does not have any significance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification

With the rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most viable platforms for improving building design and management. Researchers investigated the possibility of integrating energy management systems with cyber-physical systems to form energy-cyber-physical systems in order to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is a major source of uncertainty for energy management, ignoring it often results in both energy waste caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposes an occupancy-linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilizes ensemble classification algorithms to extract three forms of occupancy information. It creates a data interface to link energy management systems and cyber-physical systems and allows for automated occupancy detection and interpretation by assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy-linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy data type, the proposed model can potentially save about 26.4% of energy consumption in cooling and ventilation demands.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Establishing nationwide power system vulnerability index across US counties using interpretable machine learning

Power outages have become increasingly frequent, intense, and prolonged in the US due to climate change, aging electrical grids, and rising energy demand. However, largely due to the absence of granular spatiotemporal outage data, we lack data-driven evidence and analytics-based metrics to quantify power system vulnerability. This limitation has hindered the ability to effectively evaluate and address vulnerability to power outages in US communities. Here, in this work, we collected ∼179 million power outage records at 15-min intervals across 3022 US contiguous counties (96.15 % of the area) from 2014 to 2023. We developed a power system vulnerability assessment framework based on three dimensions (intensity, frequency, and duration) and applied interpretable machine learning models (XGBoost and SHAP) to compute Power System Vulnerability Index (PSVI) at the county level. Our analysis reveals a consistent increase in power system vulnerability across the US counties over the past decade. We identified 318 counties across 45 states as hotspots for high power system vulnerability, particularly in the West Coast (California and Washington), the East Coast (Florida and the Northeast area), the Great Lakes megalopolis (Chicago-Detroit metropolitan areas), and the Gulf of Mexico (Texas). Our heterogeneity analysis indicates that urban counties and those located along regional transmission boundaries tend to exhibit significantly higher vulnerability. Our results highlight the significance of the proposed PSVI for evaluating the vulnerability of communities to power outages. The findings underscore the widespread and pervasive impact of power outages across the country and offer crucial insights to support infrastructure operators, policymakers, and emergency managers in formulating policies and programs aimed at enhancing the resilience of the US power infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Interpretation of wide-angle x-ray diffraction patterns from mesophase pitch-based carbon fibers – a simulation and experimental study

Carbon fiber is a critical material in a wide range of industries, where it is highly valued for its high specific strength/stiffness, excellent wear resistance, efficient electrical and thermal transport properties, chemical resistance, and low coefficient of thermal expansion. The properties of a specific carbon fiber are closely tied to its structural characteristics at all length scales. Here, in this work, we applied wide-angle x-ray diffraction to a set of heat-treated mesophase pitch-based carbon fibers, with the goal of elucidating the crystalline structures as a function of fiber orientation. To assist with analysis and interpretation of the experimental data, we employed diffraction pattern simulations using the scalar and vector forms of the Debye scattering equation to determine the influence of basal plane orientation, crystalline ordering (turbostratic-graphitic), and basal plane asymmetry on the diffraction patterns. The results presented here suggest that growth of the transverse crystallites in mesophase pitch-based carbon fiber is fixed until graphitization temperatures are reached. The work completed here provides a framework for the analysis of carbon fiber and other oriented carbon-based materials via diffraction.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity

The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of direct measurement of these internal variables for calibration and validation, and the weak physical underpinning of those phenomenological laws have long been criticized as barriers to creating realistic models. In this work, geometric machine learning on graph data (e.g. finite element solutions) is used as a means to establish a connection between nonlinear dimensional reduction techniques and plasticity models. Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a low-dimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space. Finally, a corresponding decoder can then convert these low-dimensional internal variables back into a weighted graph such that the dominating topological features of plastic deformation can be observed and analyzed.

42 ENGINEERING↗
DOE OSTI2024

Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. This work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. Furthermore, the results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: (1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, (2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Response to a comment on Dessert et al. “The dark matter interpretation of the 3.5 keV line is inconsistent with blank-sky observations”

The dark matter explanation of the 3.5 keV line is strongly disfavored by our work in Dessert et al. (2020). Boyarsky et al. (2004) questions that conclusion: modeling additional background lines is claimed to weaken the limit sufficiently to re-allow a dark matter interpretation. Here, we respond as follows. (1) A more conservative limit is obtained by modeling additional lines; this point appeared in its entirety in our work in Dessert et al. (2020), though we also showed that the inclusion of such lines is not necessary. (2) Despite suggestions in Boyarsky et al. (2004), even the more conservative limits strongly disfavor a decaying dark matter origin of the 3.5 keV line.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Unveiling stimulation fluid-driven alterations in shale pore architecture through combined interpretation of TD-NMR and multi-component gas adsorption

The hydraulic fracturing stimulation process can alter petrophysical and flow properties of the shale matrix in unconventional reservoirs. These alterations can result from interactions with native and exogenous fluids, and can be detrimental to reservoir productivity. Different experimental procedures have been proposed to study reactive processes and thereby understand the aforementioned interactions. Most published works do not consider the rock initial saturation with formation fluid, or the effect of organic matter (OM). To gain insights into these effects, two reactive experimental sets, differing in experimental conditions, were conducted. In the first set, at 125°C and 45 MPa, a stimulation fluid surrogate was put in contact with Baxter Shale samples, previously saturated and equilibrated with synthetic formation fluid. A baseline consisted of exposure to formation fluid exclusively. In the second set, at 0.08 MPa and 25°C, contact with stimulation fluid took place as in the first set. Two additional assays in this second set either skipped the initial formation fluid saturation or removed the OM, respectively. Petrophysical properties analyses, focused on the pore architecture, relied on the combined interpretation of gas adsorption and Time-Domain Nuclear Magnetic Resonance (TD-NMR). Results show that the presence of the formation fluid significantly restricts pore space accessibility to the stimulation fluid and mitigates the pore-architecture alteration to the macro-pore region. Mineral dissolution and precipitation dominate over surface or wettability alteration. Next, results suggest that the OM restricts accessibility to some pores. Ultimately, the equilibration process with synthetic formation fluid drives mainly surface alteration, unhindered by OM.

58 GEOSCIENCES↗
DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗
DOE OSTI2017

Formation of solid thorium monoxide at near-ambient conditions as observed by neutron reflectometry and interpreted by screened hybrid functional calculations

Oxidation of a ~1000 Å sputter-deposited thorium thin film at 150 °C in 100 ppm of flowing oxygen in argon produces the long-sought solid form of thorium monoxide. Changes in the scattering length density (SLD) distribution in the film over the 700-min experiment measured by in-situ, dynamic neutron reflectometry (NR) shows the densities, compositions and thickness of the various thorium oxides layers formed. Screened, hybrid density-functional theory calculations of potential thorium oxides aid interpretation, providing atomic-level picture and energetics for understanding oxygen migration. NR provided evidence of the formation of substoichiometric thorium oxide, ThO y (y < 1) at the interface between the unreacted thorium metal and its dioxide overcoat which grows inward, consuming the thorium at a rate of 2.1 Å/min while y increases until reaching 1:1 oxygen-to-thorium. Its presence indicates that kinetically-favored solid-phase ThO can be preferentially generated as a majority phase under the thermodynamically-favored ThO 2 top layer at conditions close to ambient.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Novel interpretation of the mean structure of feroxyhyte

The structure of the iron oxyhydroxide called feroxyhyte (δ-FeOOH), which shows an elusive X-ray powder diffraction pattern, has been represented so far using models describing a mean structure based on the crystalline network of the iron(III) oxide hematite (α-Fe{sub 2}O{sub 3}). In this paper, a novel description of the mean structure of feroxyhyte is presented, which is based on the structure of the thermodynamically stable iron oxyhydroxide goethite. Starting from different local arrangements present in the goethite network, a mean structural model is determined which shows an X-ray powder diffraction pattern almost coincident with previous studies. This outcome enables to integrate the structure of feroxyhyte among those of other well characterized iron oxyhydroxides. - Graphical abstract: The structure of the iron oxy-hydroxide feroxyhyte can be described by local arrangements present in the goethite network. - Highlights: • The structure of feroxyhyte (δ-FeOOH) proposed in literature is discussed. • The structure of goethite (α-FeOOH) is analyzed. • A structural relationship between feroxyhyte and goethite is found. • New interpretation of the mean structure of δ-FeOOH is given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Interfacial negative capacitance in planar perovskite solar cells: An interpretation based on band theory

Highlights: • PSCs with different ETL strategies were characterized by EIS measurement under various conditions. • The negative capacitance solely occurred in the moderate frequency region when special conditions are met. • A model based on semiconductor band theory was proposed to interpret the corresponding mechanism. • This achievement provide important insight into the physical mechanism behind the negative capacitance of PSCs. - Abstract: The negative capacitance is a peculiar phenomenon frequently observed in electrochemical impedance spectroscopy (EIS) measurement for perovskite solar cells (PSCs). However, the origin and mechanism of the negative capacitance in PSCs remains ambiguous. Here, planar PSCs with different electron transport layer (ETL) strategies (SnO{sub 2}, TiO{sub 2}, or without ETL) were characterized by EIS in a wide range of frequencies under various conditions. The negative capacitance solely occurred in the moderate frequency region as the perovskite layer directly contacted with the conductive SnO{sub 2}: F (FTO) substrate under illumination and open-circuit conditions. Based on semiconductor band theory, the origin of negative capacitance to the formation of an inversion layer in the perovskite/FTO interface induced by the interfacial energy band over-bent under illumination and open-circuit conditions. Our study provides important insight into the physical mechanism behind the negative capacitance of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optical spectroscopy of molten fluorides: Methods, electronic and vibrational data, structural interpretation, and relevance to radiative heat transfer

In this study, to help address the need for predicting radiative heat transfer (RHT) behavior of molten salts, we conducted a comprehensive review of methods and data from optical spectroscopic measurements on molten fluoride salts. Transmittance, reflectance, and trans-reflectance experimental methods are discussed, along with the corresponding data reduction methodology and the limitations of each technique. Optical spectroscopy is a convenient indirect probe for changes in structural parameters with temperature and composition. Electronic and vibrational absorption data for transition-metal, lanthanide, and actinide solutes and vibrational absorption data for alkali and alkaline earth fluoride solvents are compiled, and the corresponding structural interpretation is discussed and compared with other experimental and theoretical work. We find that solvent and solute vibrational absorption can be significant in the mid-infrared, resulting in near-infrared edges of significance to RHT. Extrapolation and averaging of existing edge data leads to estimated gray absorption coefficient values at 700 °C of 546 m —1 for FLiBe and 276 m —1 for FLiNaK, both within the range of 1 – 6000 m —1 identified to be of engineering relevance for radiative heat transfer analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2015

2700 years of Mediterranean environmental change in central Italy: a synthesis of sedimentary and cultural records to interpret past impacts of climate on society

We report abrupt climate change in the past is thought to have disrupted societies by accelerating environmental degradation, potentially leading to cultural collapse. Linking climate change directly to societal disruption is challenging because socioeconomic factors also play a large role, with climate being secondary or sometimes inconsequential. Combining paleolimnologic, historical, and archaeological methods provides for a more secure basis for interpreting the past impacts of climate on society. We present pollen, non-pollen palynomorph, geochemical, paleomagnetic and sedimentary data from a high-resolution 2700 yr lake sediment core from central Italy and compare these data with local historical documents and archeological surveys to reconstruct a record of environmental change in relation to socioeconomic history and climatic fluctuations. Here we document cases in which environmental change is strongly linked to changes in local land management practices in the absence of clear climatic change, as well as examples when climate change appears to have been a strong catalyst that resulted in significant environmental change that impacted local communities. During the Imperial Roman period, despite a long period of stable, mild climate, and a large urban population in nearby Rome, our site shows only limited evidence for environmental degradation. Warm and mild climate during the Medieval Warm period, on the other hand, led to widespread deforestation and erosion. The ability of the Romans to utilize imported resources through an extensive trade network may have allowed for preservation of the environment near the Roman capital, whereas during medieval time, the need to rely on local resources led to environmental degradation. Cool wet climate during the Little Ice Age led to a breakdown in local land use practices, widespread land abandonment and rapid reforestation. Our results present a high-resolution regional case study that explores the effect of climate change on society for an under-documented region of Europe.

58 GEOSCIENCES↗
DOE OSTI2021

Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease

The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. Finally, we review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗