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DOE OSTI2020

Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

Engineering↗

DOE OSTI2016

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Report on PTT Imaging of Defects in AM Metallic Materials-Part 2

36 MATERIALS SCIENCE↗

DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Maximum strength and dislocation patterning in multi–principal element alloys

Multi–principal element alloys (MPEAs) containing three or more components in high concentrations render a tunable chemical short-range order (SRO). Leveraging large-scale atomistic simulations, we probe the limit of Hall-Petch strengthening and deformation mechanisms in a model CrCoNi alloy and unravel chemical ordering effects. The presence of SRO appreciably increases the maximum strength and lowers the propensity for faulting and structure transformation, accompanied by intensification of planar slip and strain localization. Deformation grains exhibit notably different microstructures and dislocation patterns that prominently depend on their crystallographic orientation and the number of active slip planes. Grain of single-planar slip attains the highest volume fraction of deformation-induced structure transformation, and grain with double-slip planes develops the densest dislocation network. These results advancing the fundamental understanding of deformation mechanisms and dislocation patterning in MPEAs suggest a mechanistic strategy for tuning mechanical behavior through simultaneously tailoring grain texture and local chemical order.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Modeling of the L-shell copper X-pinch plasma produced by the compact generator of Ecole polytechnique using pattern recognition

Principal component analysis is applied and compared with the line ratios of special Ne-like transitions for investigating the electron beam effects on the L-shell Cu synthetic spectra. The database for the principal component extraction is created over a non Local Thermodynamic Equilibrium (non-LTE) collisional radiative L-shell Copper model. The extracted principal components are used as a database for Artificial Neural Network in order to estimate the plasma electron temperature, density, and beam fractions from a representative time-integrated spatially resolved L-shell Cu X-pinch plasma spectrum. The spectrum is produced by the explosion of 25-μm Cu wires on a compact LC (40 kV, 200 kA, and 200 ns) generator. The modeled plasma electron temperatures are about T{sub e} ∼ 150 eV and N{sub e} = 5 × 10{sup 19} cm{sup −3} in the presence of the fraction of the beams with f ∼ 0.05 and a centered energy of ∼10 keV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Patterns of Precipitation Change and Climatological Uncertainty among CMIP5 Models, with a Focus on the Midlatitude Pacific Storm Track

Projections of modeled precipitation ( P ) change in global warming scenarios demonstrate marked intermodel disagreement at regional scales. Empirical orthogonal functions (EOFs) and maximum covariance analysis (MCA) are used to diagnose spatial patterns of disagreement in the simulated climatology and end-of-century P changes in phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive. The term principal uncertainty pattern (PUP) is used for any robust mode calculated when applying these techniques to a multimodel ensemble. For selected domains in the tropics, leading PUPs highlight features at the margins of convection zones and in the Pacific cold tongue. The midlatitude Pacific storm track is emphasized given its relevance to wintertime P projections over western North America. The first storm-track PUP identifies a sensitive region of disagreement in P increases over the eastern midlatitude Pacific where the storm track terminates, related to uncertainty in an eastward extension of the climatological jet. The second PUP portrays uncertainty in a zonally asymmetric meridional shift of storm-track P , related to uncertainty in the extent of a poleward jet shift in the western Pacific. Both of these modes appear to arise primarily from intermodel differences in the response to radiative forcing, distinct from sampling of internal variability. The leading storm-track PUPs for P and zonal wind change exhibit similarities to the leading uncertainty patterns for the historical climatology, indicating important and parallel sensitivities in the eastern Pacific storm-track terminus region. However, expansion coefficients for climatological uncertainties tend to be weakly correlated with those for end-of-century change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Potential impacts of the Arctic on interannual and interdecadal summer precipitation over China

After the end of the 1970s, there has been a tendency for enhanced summer precipitation over South China and the Yangtze River valley and drought over North China and Northeastern China. Coincidentally, Arctic ice concentration has decreased since the late 1970s, with larger reduction in summer than spring. However, the Arctic warming is more significant in spring than summer, suggesting that spring Arctic conditions could be more important in their remote impacts. This study investigates the potential impacts of the Arctic on summer precipitation in China. The leading spatial patterns and time coefficients of the unfiltered, interannual, and interdecadal precipitation (1960-2008) modes were analyzed and compared using empirical orthogonal function (EOF) analysis, which shows that the first three EOFs can capture the principal precipitation patterns (northern, central and southern patterns) over eastern China. Regression of the Arctic spring and summer temperature onto the time coefficients of the leading interannual and interdecadal precipitation modes shows that interdecadal summer precipitation in China is related to the Arctic spring warming, but the relationship with Arctic summer temperature is weak. Moreover, no notable relationships were found between the first three modes of interannual precipitation and Arctic spring or summer temperatures. Finally, correlations between summer precipitation and the Arctic Oscillation (AO) index from January to August were investigated, which indicate that summer precipitation in China correlates with AO only to some extent. Overall, this study suggests important relationships between the Arctic spring temperature and summer precipitation over China at the interdecadal time scale.

Arctic↗

DOE OSTI2023

The Intrinsic 150‐Day Periodicity of the Southern Hemisphere Extratropical Large‐Scale Atmospheric Circulation

Abstract The variability of the Southern Hemisphere (SH) extratropical large‐scale circulation is dominated by the Southern Annular Mode (SAM), whose timescale is extensively used as a key metric in evaluating state‐of‐the‐art climate models. Past observational and theoretical studies suggest that the SAM lacks any internally generated (intrinsic) periodicity. Here, we show, using observations and a climate model hierarchy, that the SAM has an intrinsic 150‐day periodicity. This periodicity is robustly detectable in the power spectra and principal oscillation patterns (aka dynamical mode decomposition) of the zonal‐mean circulation, and in hemispheric‐scale precipitation and ocean surface wind stress. The 150‐day period is consistent with the predictions of a new reduced‐order model for the SAM, which suggests that this periodicity is associated with a complex interaction of turbulent eddies and zonal wind anomalies, as the latter propagate from low to high latitudes. These findings present a rare example of periodic oscillations arising from the internal dynamics of the extratropical turbulent circulations. Based on these findings, we further propose a new metric for evaluating climate models, and show that some of the previously reported shortcomings and improvements in simulating SAM's variability connect to the models' ability in reproducing this periodicity. We argue that this periodicity should be considered in evaluating climate models and understanding the past, current, and projected Southern Hemisphere climate variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Using Green's Functions to Compute 3 Dimensional Neutron Flux Maps - 16016

The knowledge of the radionuclide content of radioactive waste is of utmost importance for safety and waste management reasons. Numerical simulations are used by EDF-DP2D to anticipate dismantling and radioactive waste management. The main numerical activation schemes include the computing of a 3 dimensional neutron flux map, obtained on the basis of a neutron propagation calculation. The codes used solve the transport equation called the Boltzmann equation without simplification. The Monte Carlo method is used to solve the Boltzmann equation. Random series of numbers are used to simulate the lives of millions of neutrons. The codes follow each neutron individually, from its birth to its disappearance by leakage, absorption, or fission, and then the neutron flux map is calculated at the nominal power rating conditions. The main drawback of the Monte Carlo codes is the large computing time needed to converge within acceptable statistical criteria. This extended time period is incompatible with the multiple fuel managements used by EDF to operate its NPPs because it is necessary to make a Monte Carlo calculation for each fuel loading pattern. The solution developed by EDF-DP2D is to use the importance factors of Green's Functions to simplify the computing of the 3 dimensional neutron flux maps. Physicists talk about Green's Functions where mathematicians talk about elementary solutions of linear equations with constant coefficients. With an activation scheme based on the Green's Functions approach the 3D neutron flux is obtained on the basis of the convolution of the normalized source contributions (i.e. normalized response matrices) with the neutrons emitted by the fuel assemblies. The response matrices to unitary sources are computed with the Monte Carlo code MCNP for different tallies of interest and the neutron sources emitted by the fuel are computed with a core code for different fuel loading patterns. The principal interest of this approach is the calculation of the response matrices to unitary sources that can be used for all power distributions, thus limiting the Monte Carlo computing time. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

Highly mobile segments in crystalline poly(ethylene oxide){sub 8}:NaPF{sub 6} electrolytes studied by solid-state NMR spectroscopy

Two types of high-crystallinity poly(ethylene oxide)/NaPF{sub 6} electrolytes with ethylene oxide (EO)/Na molar ratios of 8:1 and 6:1, termed as PEO{sub 8}:NaPF{sub 6} and PEO{sub 6}:NaPF{sub 6} with M{sub w} = 6000 g mol{sup −1} were prepared, and their ionic conductivity, structure, and segmental motions were investigated and compared. PEO{sub 8}:NaPF{sub 6} polymer electrolyte exhibits the room-temperature ionic conductivity 7.7 × 10{sup −7} S cm{sup −1} which is about five times higher than the PEO{sub 6}:NaPF{sub 6}. By variable-temperature measurements of static powder spectra and {sup 1}H spin-lattice relaxation time in rotation frame ({sup 1}H T{sub 1ρ}), we demonstrate that crystalline segments are more highly mobile in the crystalline PEO{sub 8}:NaPF{sub 6} with higher ionic conductivity than in the PEO{sub 6}:NaPF{sub 6} with lower ionic conductivity. The large-angle reorientation motion of polymer segments in the PEO{sub 8}:NaPF{sub 6} onsets at lower temperature (∼233 K) with a low activation energy 0.31 eV that is comparable with that of the pure PEO crystal. Whereas, the large-angle reorientation motion of polymer segments in the PEO{sub 6}:NaPF{sub 6} starts around 313 K with a high activation energy of 0.91 eV. As a result of the temperature-enhanced large-angle reorientations, the {sup 13}C static powder lineshape changes markedly from a low-temperature wide pattern with apparent principal values of chemical shift δ{sub 33} < δ{sub 22} < δ{sub 11} to a high-temperature narrow pattern of uniaxial chemical shift anisotropy δ{sub 33} > δ{sub 22} (δ{sub 11}). It is suggested that the segmental motion in crystalline PEO-salt complex promotes ionic conductivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Roles of dynamical symmetry breaking in driving oblate-prolate transitions of atomic clusters

This paper explores the driving mechanisms for structural transitions of atomic clusters between oblate and prolate isomers. We employ the hyperspherical coordinates to investigate structural dynamics of a seven-atom cluster at a coarse-grained level in terms of the dynamics of three gyration radii and three principal axes, which characterize overall mass distributions of the cluster. Dynamics of gyration radii is governed by two kinds of forces. One is the potential force originating from the interactions between atoms. The other is the dynamical forces called the internal centrifugal forces, which originate from twisting and shearing motions of the system. The internal centrifugal force arising from twisting motions has an effect of breaking the symmetry between two gyration radii. As a result, in an oblate isomer, activation of the internal centrifugal force that has the effect of breaking the symmetry between the two largest gyration radii is crucial in triggering structural transitions into prolate isomers. In a prolate isomer, on the other hand, activation of the internal centrifugal force that has the effect of breaking the symmetry between the two smallest gyration radii is crucial in triggering structural transitions into oblate isomers. Activation of a twisting motion that switches the movement patterns of three principal axes is also important for the onset of structural transitions between oblate and prolate isomers. Based on these trigger mechanisms, we finally show that selective activations of specific gyration radii and twisting motions, depending on the isomer of the cluster, can effectively induce structural transitions of the cluster. The results presented here could provide further insights into the control of molecular reactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Sequence conservation of protein binding segments in intrinsically disordered regions

Highlights: • Sequence conservation in protein-binding segments in IDRs is analyzed. • Functionally constraint residues in protein-binding segments in IDRs are conserved. • Evolutionary distance should be considered in the comparison of IDRs. • Cryptic binding regions may influence sequence conservation of IDRs. Intrinsically disordered proteins are proteins with intrinsically disordered regions (IDRs) that do not adopt a globular structure in their free state. IDRs have unique regions having protein-binding segments of which play pivotal roles in many biological processes. The binding sites in IDRs are heterogeneous in terms of their sequence conservation: some are conserved and others are not. We have been running a database of intrinsically disordered proteins, IDEAL, and collecting such binding segments, which are called protean segments (ProSs), within it. In this study, we compared the sequence conservation of ProSs, structural domains (SDs), and IDRs other than ProSs (non-ProSs) and found that i) functionally constrained residues in ProSs tend to be conserved, ii) the distribution of conservation scores of ProSs is similar to those of SDs but not non-ProSs, and iii) ProSs found in human proteins are mostly conserved only in vertebrates. These results indicate that the conservation patterns in ProSs principally follow the general rules found in SDs. However, we need to consider evolutionary distance when comparing IDR sequences because ProSs can readily emerge and disappear over the course of protein evolution. Moreover, many ProSs may remain to be identified, which may account for the heterogeneity of the sequence conservation of IDRs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗

DOE OSTI2024

The Role of Cloud Radiative Effects in the Propagating Southern Annular Mode

Abstract The Southern Annular Mode (SAM) is the most dominant natural mode of variability in the mid‐latitudes of the Southern Hemisphere (SH). However, both the sign and magnitude of the feedbacks from the diabatic processes, especially those associated with clouds, onto the SAM remain elusive. By applying the cloud locking technique to the Energy Exascale Earth System Model (E3SM) atmosphere model, this study isolates the positive feedback from the cloud radiative effect (CRE) to the SAM. Feedback analysis based on a wave activity‐zonal momentum interaction framework corroborates this weak but positive feedback. While the magnitude of the CRE feedback appears to be secondary compared to the feedbacks from the dry and other diabatic processes, the indirect CRE effects through the interaction with other dynamical and thermodynamical processes appear to play as important a role as the direct CRE in the life cycle of the SAM. The cross‐EOF analysis further reveals the obstructive effect of the interactive CRE on the propagation mode of the SH zonal wind directly through the CRE wave source and/or indirectly through modulating other diabatic processes. As a result, the propagation mode becomes more persistent and the SAM it represents becomes more predictable when the interactive CRE is disabled by cloud locking. Future efforts on inter‐model comparisons of CRE‐denial experiments are important to build consensus on the dynamical feedback of CRE.

58 GEOSCIENCES↗

DOE OSTI2016

TU-AB-BRA-09: A Novel Method of Generating Ultrafast Volumetric Cine MRI (VC-MRI) Using Prior 4D-MRI and On-Board Phase-Skipped Encoding Acquisition for Radiotherapy Target Localization

Purpose: To develop a technique generating ultrafast on-board VC-MRI using prior 4D-MRI and on-board phase-skipped encoding k-space acquisition for real-time 3D target tracking of liver and lung radiotherapy. Methods: The end-of-expiration (EOE) volume in 4D-MRI acquired during the simulation was selected as the prior volume. 3 major respiratory deformation patterns were extracted through the principal component analysis of the deformation field maps (DFMs) generated between EOE and all other phases. The on-board VC-MRI at each instant was considered as a deformation of the prior volume, and the deformation was modeled as a linear combination of the extracted 3 major deformation patterns. To solve the weighting coefficients of the 3 major patterns, a 2D slice was extracted from VC-MRI volume to match with the 2D on-board sampling data, which was generated by 8-fold phase skipped-encoding k-space acquisition (i.e., sample 1 phase-encoding line out of every 8 lines) to achieve an ultrafast 16–24 volumes/s frame rate. The method was evaluated using XCAT digital phantom to simulate lung cancer patients. The 3D volume of end-ofinhalation (EOI) phase at the treatment day was used as ground-truth onboard VC-MRI with simulated changes in 1) breathing amplitude and 2) breathing amplitude/phase change from the simulation day. A liver cancer patient case was evaluated for in-vivo feasibility demonstration. Results: The comparison between ground truth and estimated on-board VC-MRI shows good agreements. In XCAT study with changed breathing amplitude, the volume-percent-difference(VPD) between ground-truth and estimated tumor volumes at EOI was 6.28% and the Center-of-Mass-Shift(COMS) was 0.82mm; with changed breathing amplitude and phase, the VPD was 8.50% and the COMS was 0.54mm. The study of liver patient case also demonstrated a promising in vivo feasibility of the proposed method Conclusion: Preliminary results suggest the feasibility to estimate ultrafast VC-MRI for on-board target localization with phase skipped-encoding k-space acquisition. Research grant from NIH R01-184173.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Automating Network Node Behavior Characterization by Mining Communication Patterns

Enterprise networks of scale are complex, dynamic computing environments that respond to evolv- ing business objectives and requirements. Characteriz- ing system behaviors in these environments is essential for network management and cyber security operations. Characterization of system’s communication is typical and is supported using network flow information (NetFlow). Related work has characterized behavior using theoretical graph metrics; results are often difficult to interpret by enterprise staff. We propose a different approach, where flow information is mapped to sets of tags that contextualize the data in terms of network principals and enterprise concepts. Frequent patterns are then extracted and are expressed as behaviors. Behaviors can be com- pared, identifying systems expressing similar behaviors. We evaluate the approach using flow information collected by a third party.

cyber system modeling↗

DOE OSTI2019

Experiments and computations of microfluidic liquid–liquid flow patterns

We study two-phase liquid–liquid flow patterns in a 500 μm capillary microchannel for four biphasic systems: ethyl acetate/water, 2-pentanol/water, methyl isobutyl ketone/water, and heptane/water. Flow visualization experiments using laser induced fluorescence (LIF) reveal a total of 7 different flow patterns for all solvent pairs, namely slug flow, droplet flow, slug-droplet flow, parallel, annular, dispersed, and irregular flow. A map of different flow patterns was built to delineate the origin of their formation. We find conventional dimensionless groups are insufficient to uniquely identify the flow patterns. Computational fluid dynamics (CFD) modeling in OpenFOAM shows agreement with the experimental flow patterns for most of the two-phase flows. Principal component analysis reduces the dimensionality of potential descriptors of flow patterns and, unlike prior work using two dimensionless numbers, determines six important features that describe >95% of the variance of the experimental flow patterns. These include the total flow rate, the flow rate ratio between the two phases, the capillary and Ohnesorge numbers of the aqueous phase, and the Weber number and velocity of the organic phase. Lastly, we build a decision-tree model to further regress the data and identify the critical features and demonstrate an accuracy in predicting the flow patterns of up to 93%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗