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At least 1,081 records · Page 60

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

This project studied the capabilities of Model-Based Image Reconstruction (MBIR) and Machine Learning (ML) algorithms in the imaging and estimation of rebar corrosion from ultrasound signals. The application was challenging due to the introduction of a steel liner between the sensor and the concrete containing the rebar. The study focused in a synthetic specimen with a 6.35 mm thick steel liner, and a 19.05 mm diameter rebar at a depth of 44.5 mm and with four levels of corrosion; 0%, 20%, 50%, and 100% corrosion level. The corrosion was applied uniformly around the rebar, generating a ring in the perimeter of the rebar with different acoustic density. In order to generate a realistic synthetic dataset, we added random texture to the concrete of the synthetic specimens. This texture will mimic the variations encountered in real concrete specimens.We adapted our MBIR algorithm for the application and enhanced the algorithm to integrate in the physical model known specimen features, such as the steel liner thickness. The new MBIR method was able to cancel out reverberation from the steel liner and properly image the rebar. In particular, corrosion for the 50% and 100% cases were easily visible and quantitative metrics showed a slight separation for the 0% and 20% levels. The quantitative results were in agreement with the qualitative assessment. We also developed a ML XGBoost model to estimate corrosion level from the ultrasound signals. By combining the ML method with MBIR, we can pinpoint in the ultrasound signals the section that corresponds to the echoes from the rebar. The extract signals are processed by the XGBoost model for prediction. From each system scan, we obtain 15 predictions. The median prediction value is used as the final prediction. The True Acceptance Rate for the method is over 99%.

36 MATERIALS SCIENCE↗

Weak-signal Phase Calibration Strategies for Large DSN Arrays

The NASA Deep Space Network (DSN) is studying arrays of large numbers of small, mass-produced radio antennas as a cost-effective way to increase downlink sensitivity and data rates for future missions. An important issue for the operation of large arrays is the accuracy with which signals from hundreds of small antennas can be combined. This is particularly true at Ka band (32 GHz) where atmospheric phase variations can be large and rapidly changing. A number of algorithms exist to correct the phases of signals from individual antennas in the case where a spacecraft signal provides a useful signal-to-noise ratio (SNR) on time scales shorter than the atmospheric coherence time. However, for very weak spacecraft signals it will be necessary to rely on background natural radio sources to maintain array phasing. Very weak signals could result from a spacecraft emergency or by design, such as direct-to-Earth data transmissions from distant planetary atmospheric or surface probes using only low gain antennas. This paper considers the parameter space where external real-time phase calibration will be necessary, and what this requires in terms of array configuration and signal processing. The inherent limitations of this technique are also discussed.

interferometry↗

Kepler Planet Detection Metrics: Window and One-Sigma Depth Functions for Data Release 25

This document describes the window and one-sigma depth functions relevant to the Transiting Planet Search (TPS) algorithm in the Kepler pipeline (Jenkins 2002; Jenkins et al. 2017). The window function specifies the fraction of unique orbital ephemeris epochs over which three transits are observable as a function of orbital period. In this context, the epoch and orbital period, together, comprise the ephemeris of an orbiting companion, and ephemerides with the same period are considered equivalent if their epochs differ by an integer multiple of the period. The one-sigma depth function specifies the depth of a signal (in ppm) for a given light curve that results in a one-sigma detection of a transit signature as a function of orbital period when averaged over all unique orbital ephemerides. These planet detection metrics quantify the ability of TPS to detect a transiting planet signature on a star-by-star basis. They are uniquely applicable to a specific Kepler data release, since they are dependent on the details of the light curves searched and the functionality of the TPS algorithm used to perform the search. This document describes the window and one-sigma depth functions relevant to Kepler Data Release 25 (DR25), where the data were processed (Thompson et al. 2016) and searched (Twicken et al. 2016) with the SOC 9.3 pipeline. In Section 4, we describe significant differences from those reported in Kepler Data Release 24 (Burke Seader 2016) and document our verification method.

Planet Detection Metrics↗

A second‐generation virtual‐pinhole PET device for enhancing contrast recovery and improving lesion detectability of a whole‐body PET/CT scanner

Purpose We have developed a second‐generation virtual‐pinhole (VP) positron emission tomography (PET) device that can position a flat‐panel PET detector around a patient's body using a robotic arm to enhance the contrast recovery coefficient (CRC) and detectability of lesions in any region‐of‐interest using a whole‐body PET/computed tomography (CT) scanner. Methods We constructed a flat‐panel VP‐PET device using 32 high‐resolution detectors, each containing a 4 4 MPPC array and 16 16 LYSO crystals of 1.0 1.0 3.0 mm 3 each. The flat‐panel detectors can be positioned around a patient's body anywhere in the imaging field‐of‐view (FOV) of a Siemens Biograph 40 PET/CT scanner by a robotic arm. New hardware, firmware and software have been developed to support the additional detector signals without compromising a scanner's native functions. We stepped a 22 Na point source across the axial FOV of the scanner to measure the sensitivity profile of the VP‐PET device. We also recorded the coincidence events measured by the scanner detectors and by the VP‐PET detectors when imaging phantoms of different sizes. To assess the improvement in the CRC of small lesions, we imaged an elliptical torso phantom measuring 316 228 162 mm 3 that contains spherical tumors with diameters ranging from 3.3 to 11.4 mm with and without the VP‐PET device. Images were reconstructed using a list mode Maximum‐Likelihood Estimation‐Maximization algorithm implemented on multiple graphics processing units (GPUs) to support the unconventional geometries enabled by a VP‐PET system. The mean and standard deviation of the CRC were calculated for tumors of different sizes. Monte Carlo simulation was also conducted to image clusters of lesions in a torso phantom using a PET/CT scanner alone or the same scanner equipped with VP‐PET devices. Receiver operating characteristic (ROC) curves were analyzed for three system configurations to evaluate the improvement in lesion detectability by the VP‐PET device over the native PET/CT scanner. Results The repeatability in positioning the flat‐panel detectors using a robotic arm is better than 0.15 mm in all three directions. Experimental results show that the average CRC of 3.3, 4.3, and 6.0 mm diameter tumors was 0.82%, 2.90%, and 5.25%, respectively, when measured by the native scanner. The corresponding CRC was 2.73%, 6.21% and 10.13% when imaged by the VP‐PET insert device with the flat‐panel detector under the torso phantom. These values may be further improved to 4.31%, 9.65% and 18.01% by a future dual‐panel VP‐PET insert device if DOI detectors are employed to triple its detector efficiency. Monte Carlo simulation results show that the tumor detectability can be improved by a VP‐PET device that has a single flat‐panel detector. The improvement is greater if the VP‐PET device employs a dual‐panel design. Conclusions We have developed a prototype flat‐panel VP‐PET device and integrated it with a clinical PET/CT scanner. It significantly enhances the contrast of lesions, especially for those that are borderline detectable by the native scanner, within regions‐of‐interest specified by users. Simulation demonstrated the enhancement in lesion detectability with the VP‐PET device. This technology may become a cost‐effective solution for organ‐specific imaging tasks.

Jiang, Jianyong↗

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗

Detection of small earthquakes with dense array data: example from the San Jacinto fault zone, southern California

We present a technique to detect small earthquakes not included in standard catalogues using data from a dense seismic array. The technique is illustrated with continuous waveforms recorded in a test day by 1108 vertical geophones in a tight array on the San Jacinto fault zone. Waveforms are first stacked without time-shift in nine non-overlapping subarrays to increase the signal-to-noise ratio. The nine envelope functions of the stacked records are then multiplied with each other to suppress signals associated with sources affecting only some of the nine subarrays. Running a short-term moving average/long-term moving average (STA/LTA) detection algorithm on the product leads to 723 triggers in the test day. Using a local P-wave velocity model derived for the surface layer from Betsy gunshot data, 5 s long waveforms of all sensors around each STA/LTA trigger are beamformed for various incident directions. Of the 723 triggers, 220 are found to have localized energy sources and 103 of these are confirmed as earthquakes by verifying their observation at 4 or more stations of the regional seismic network. This demonstrates the general validity of the method and allows processing further the validated events using standard techniques. The number of validated events in the test day is >5 times larger than that in the standard catalogue. Using these events as templates can lead to additional detections of many more earthquakes.

58 GEOSCIENCES↗

An adaptive data-smoothing routine

An adaptive noise reduction algorithm that can be implemented on a microcomputer is developed. Smoothing polynomials are used where the polynomial coefficients are chosen such that the mean-square-error between the noisy and smoothed data is minimized. This approach is equivalent to the implementation of a low-pass finite impulse response filter. The noise reduction depends on the order of the smoothing polynomial. A whiteness test on the error sequence is incorporated to search for the optimal smoothing. Expansion coefficients may be computed via the fast Fourier transform, and the resulting smoothing process is the equivalent of the implementation of an adaptive ideal low-pass filter. Results are obtained for an analytical signal with added white Gaussian noise. The routine may be applied to any smooth signal with additive random noise.

Taylor, Clayborne D.↗

Regional to Global Assessments of Phytoplankton Dynamics From The SeaWiFS Mission

Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the oceanmay be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone.

phytoplankton↗

Methodology for Validation of Unsteady Pressure-Sensitive Paint Measurements using Pressure Transducers

Unsteady Pressure-Sensitive Paint (uPSP) technology provides simultaneously high-frequency and high-spatial resolution surface pressure fluctuation measurements. It has demonstrated value in wind tunnel test facilities to evaluate aerospace vehicle unsteady aerodynamics. The calibration of measurements from digital pixel counts into corresponding physical units relies on both lab-based and in-situ calibration steps, and its validation is both challenging and critical to adoption of the technology. We present a rigorous methodology for direct comparison of uPSP-derived surface pressure measurements to reference pressure transducer data based on a model of the camera-based measurement error, a model of the flow field pressure signal, and their propagation through the uPSP data reduction algorithm. The methodology is demonstrated on data collected in a small-scale, subsonic wind tunnel test as well as data collected from a Space Launch System unsteady aerodynamics characterization test at the NASA Ames Research Center Unitary Plan Wind Tunnel Complex.

Pressure-Sensitive Paint↗

Tunable diode laser absorption spectroscopy-based tomography system for on-line monitoring of two-dimensional distributions of temperature and H{sub 2}O mole fraction

To monitor two-dimensional (2D) distributions of temperature and H{sub 2}O mole fraction, an on-line tomography system based on tunable diode laser absorption spectroscopy (TDLAS) was developed. To the best of the authors’ knowledge, this is the first report on a multi-view TDLAS-based system for simultaneous tomographic visualization of temperature and H{sub 2}O mole fraction in real time. The system consists of two distributed feedback (DFB) laser diodes, a tomographic sensor, electronic circuits, and a computer. The central frequencies of the two DFB laser diodes are at 7444.36 cm{sup −1} (1343.3 nm) and 7185.6 cm{sup −1} (1391.67 nm), respectively. The tomographic sensor is used to generate fan-beam illumination from five views and to produce 60 ray measurements. The electronic circuits not only provide stable temperature and precise current controlling signals for the laser diodes but also can accurately sample the transmitted laser intensities and extract integrated absorbances in real time. Finally, the integrated absorbances are transferred to the computer, in which the 2D distributions of temperature and H{sub 2}O mole fraction are reconstructed by using a modified Landweber algorithm. In the experiments, the TDLAS-based tomography system was validated by using asymmetric premixed flames with fixed and time-varying equivalent ratios, respectively. The results demonstrate that the system is able to reconstruct the profiles of the 2D distributions of temperature and H{sub 2}O mole fraction of the flame and effectively capture the dynamics of the combustion process, which exhibits good potential for flame monitoring and on-line combustion diagnosis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Direct adaptive control of a PUMA 560 industrial robot

The implementation and experimental validation of a new direct adaptive control scheme on a PUMA 560 industrial robot is described. The testbed facility consists of a Unimation PUMA 560 six-jointed robot and controller, and a DEC MicroVAX II computer which hosts the Robot Control C Library software. The control algorithm is implemented on the MicroVAX which acts as a digital controller for the PUMA robot, and the Unimation controller is effectively bypassed and used merely as an I/O device to interface the MicroVAX to the joint motors. The control algorithm for each robot joint consists of an auxiliary signal generated by a constant-gain Proportional plus Integral plus Derivative (PID) controller, and an adaptive position-velocity (PD) feedback controller with adjustable gains. The adaptive independent joint controllers compensate for the inter-joint couplings and achieve accurate trajectory tracking without the need for the complex dynamic model and parameter values of the robot. Extensive experimental results on PUMA joint control are presented to confirm the feasibility of the proposed scheme, in spite of strong interactions between joint motions. Experimental results validate the capabilities of the proposed control scheme. The control scheme is extremely simple and computationally very fast for concurrent processing with high sampling rates.

Seraji, Homayoun↗

Geoscience Laser Altimeter System (GLAS) for the ICESat Mission

The Geoscience Laser Altimeter System (GLAS) is a new generation lidar and is the primary science payload for NASA's ICESat Mission. The GLAS design combines a 10 cm precision surface lidar with a sensitive dual wavelength cloud and aerosol lidar. GLAS will precisely measure the heights of the Earth's polar ice sheets, establish a grid of accurate height profiles of the Earth's land topography, and profile the vertical distribution of clouds and aerosols on a global scale. GLAS will be integrated onto a small spacecraft built by Ball Aerospace, and will be launched into a polar orbit with a 590-630 km altitude at an inclination of 94 degrees. ICESat is is currently planned to launch in winter 2002/03 and GLAS is designed to operate continuously in space for a minimum of 3 years. GLAS will measure the vertical distance from orbit to the Earth's surface with pulses from a ND:YAG laser at a 40 Hz rate. Each 6 nsec wide 1064 nm laser pulse is used to produce a single range measurement. On the surface, the laser footprints have 66 m diameter and approx. 170 m center-center spacings. The GLAS receiver uses a I m diameter telescope to detect laser backscatter and a Si APD to detect the 1064 nm signals. The detector's output is sampled by a digital ranging receiver, which records each transmitted pulse and surface echo waveform with 1 nsec (15 cm) resolution. Each echo pulse is digitized and is reported to ground with a record length of from 200 to 544 samples, depending on the spacecraft's location . The GLAS location and epoch times are measured by a precision GPS receiver carried on the ICESat spacecraft. Initial processing of the echo waveforms within GLAS permits discrimination between cloud and surface echoes for selecting appropriate waveform samples. This selection is guided by an on-board DEM which is used to set the boundaries for the echo pulse search algorithm. Subsequent ground-based echo pulse analysis, along with GPS-based clock frequency estimates, permit final determination of the range to the surface, degree of pulse spreading, and vertical distribution of any vegetation illuminated by the laser. Accurate knowledge of the laser beam's pointing angle is needed to prevent height biases when measuring over tilted surfaces, such as near the boundaries of ice sheets. For surfaces with 2 deg. slopes, knowledge of pointing angle of the beam's centroid angle to better than 10 urad is needed. GLAS uses a stellar reference system (SRS) to measure the pointing angle of each laser firing relative to inertial space. The SRS uses a high precision star camera oriented toward local zenith and a gyroscope to determine the inertial orientation of the SRS optical bench. The far field pattern of each laser is measured pulse relative to the star camera with a laser reference system (LRS). GLAS will also measure the vertical distributions of clouds and aerosols by recording the vertical profiles of laser pulse backscatter at both 1064 and 532 nm. The 1064 rim measurements use the Si APD detector and will be used to measure the height and echo pulse shape from thicker clouds. The lidar receiver at 532 nm uses a narrow bandwidth etalon filter and highly sensitive photon counting detectors. The 532 nm backscatter profiles will be used to measure the vertical extent of thinner clouds and the atmospheric boundary layer. The GLAS instrument component development is complete and the instrument is undergoing final testing and qualification at NASA-Goddard. The GLAS "as-built" characteristics and its expected measurement performance will be discussed.

Abshire, James B.↗

Applicability of Parallel Computing to Partial Wave Analysis

Bound states of Quantum Chromodynamics (QCD) give insights into the nature of confinement, a key element of the strong interaction. States may be identified from weak signals extracted from the analysis of high statistics data from reactions with many final state particles. One of the best tools for the analysis of these reactions is Partial Wave Analysis (PWA). PWA transforms an ensemble of experimental data from a large acceptance detector from free particle eigenstates to angular momentum eigenstates. The PWA program must be fast enough to deal with the large amounts of data available currently, as processing time scales with the number of events. The scope of this research is to study the applicability and scalability of Intel's Xeon Phi using the Many Integrated Core (MIC) architecture when applied to the existing PWA code at Jefferson Laboratory. An algorithm was developed for the Xeon Phi and scaled across 240 available threads, giving parallel functionality to the PWA which was originally written serially. This scaling can make the fitting process fifteen times faster.

Ruger, Justin↗

Frequency Estimator Performance for a Software-Based Beacon Receiver

As propagation terminals have evolved, their design has trended more toward a software-based approach that facilitates convenient adjustment and customization of the receiver algorithms. One potential improvement is the implementation of a frequency estimation algorithm, through which the primary frequency component of the received signal can be estimated with a much greater resolution than with a simple peak search of the FFT spectrum. To select an estimator for usage in a QV-band beacon receiver, analysis of six frequency estimators was conducted to characterize their effectiveness as they relate to beacon receiver design.

Frequency Domain Analysis↗

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multiscale dynamics of reactive fronts in the subsurface

1. Brief summary of progress to date. Major Goals of the Project. Overreaching goals of the proposed activity are to develop an or- ganizing framework to describe nonlinear reactive transport in geologic systems over many length and time scales, and corresponding physics-based adaptive multi-scale hybrid models. We will also provide computational tools for robust uncertainty quantification in hybrid simulations. Major Activities of the Project. The major activities in this reporting period have focused on developing a time-averaging framework for ADR transport (Task 1) and global diagnosis criteria for spatio-temporal algorithm refinement (Task 2), as well as numerical validation and verification of the analytical approaches developed. The main activities are as follows: (a) We use homogenization theory to perform a spatio-temporal upscaling of pore-scale advection- diffusion equation in a periodic porous medium in the presence of fluctuating boundary conditions on some parts of the boundary and nonlinear chemical reaction on the solid-liquid interface and we investigate the constraints under which the macroscopic equation gives an accurate explanation of the underlying physics at the pore scale. We demonstrate that the dynamics at the continuum scale is strongly influenced by the interplay between signal frequency at the boundary and transport processes at the pore level. Furthermore, we show that by choosing appropriate scaling factors, a homogenized equation along with expressions for the effective coefficients reliably describe the concentration of solute at the length and time scales vastly different from physically robust micro-scale provided that some specific scale separation constraints are met. Our analysis proves that if the reaction rate or advection at the micro-scale are large, the effectiveness of the homogenized model is impeded. (b) We test numerically the conditions derived theoretically for advective-diffusive and reactive trans- port in a fracture with planar walls subject to temporally oscillating Dirichlet boundary conditions. The numerical analysis confirms the theoretical predictions. Significant Results. Two paper are in preparation. 2. Description of work yet to be accomplished The Tasks yet to be accomplished are listed below: (a) Quantify the impact of uncertainty in wall geometry of planar fractures on predictions hybrids of ARD transport (b) Develop coupling conditions for time hybridzation (c) Quantify the impact of uncertainty in pore geometry on predictions of hybrids of ARD transport in porous media, i.e. develop stochastic hybrid models (d) Use the hybrids combined with X-Ray tomographic images of pore scale structure (from Dr. Gouze, see letter of collaboration) to model dynamic changes in porosity/permeability

42 ENGINEERING↗