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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Federated Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Evidence for the direct detection of the thermal spectrum of the non-transiting hot gas giant HD 88133 B

We target the thermal emission spectrum of the non-transiting gas giant HD 88133 b with high-resolution near-infrared spectroscopy, by treating the planet and its host star as a spectroscopic binary. For sufficiently deep summed flux observations of the star and planet across multiple epochs, it is possible to resolve the signal of the hot gas giant’s atmosphere compared to the brighter stellar spectrum, at a level consistent with the aggregate shot noise of the full data set. To do this, we first perform a principal component analysis to remove the contribution of the Earth’s atmosphere to the observed spectra. Then, we use a cross-correlation analysis to tease out the spectra of the host star and HD 88133 b to determine its orbit and identify key sources of atmospheric opacity. In total, six epochs of Keck NIRSPEC L-band observations and three epochs of Keck NIRSPEC K-band observations of the HD 88133 system were obtained. Based on an analysis of the maximum likelihood curves calculated from the multi-epoch cross-correlation of the full data set with two atmospheric models, we report the direct detection of the emission spectrum of the non-transiting exoplanet HD 88133 b and measure a radial projection of the Keplerian orbital velocity of 40 ± 15 km s{sup −1}, a true mass of 1.02{sub −0.28}{sup +0.61}M{sub J}, a nearly face-on orbital inclination of 15{sub −5}{sup +6{sup ∘}}, and an atmosphere opacity structure at high dispersion dominated by water vapor. This, combined with 11 years of radial velocity measurements of the system, provides the most up-to-date ephemeris for HD 88133.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids: Preprint

Increase in the proliferation of DERs requires real-time situational awareness for efficient grid operations. State estimation plays an important role for real time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hierarchical spectral clustering-based network partition algorithm followed by a decentralized compressive sensing (DCS) based state estimation. The applicability of the proposed network partitioning algorithm is tested on IEEE-123 bus, IEEE-8500 node, and a 6204-node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6204-node distribution network after dividing the network into 18 optimal partitions. Simulation results show that DCS-based state estimation recovers the system states with high accuracy and low complexity.

ADMM↗
DOE OSTI2019

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated 'Drive Me' project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

MO-FG-202-05: Identifying Treatment Planning System Errors in IROC-H Phantom Irradiations

Purpose: Treatment Planning System (TPS) errors can affect large numbers of cancer patients receiving radiation therapy. Using an independent recalculation system, the Imaging and Radiation Oncology Core-Houston (IROC-H) can identify institutions that have not sufficiently modelled their linear accelerators in their TPS model. Methods: Linear accelerator point measurement data from IROC-H’s site visits was aggregated and analyzed from over 30 linear accelerator models. Dosimetrically similar models were combined to create “classes”. The class data was used to construct customized beam models in an independent treatment dose verification system (TVS). Approximately 200 head and neck phantom plans from 2012 to 2015 were recalculated using this TVS. Comparison of plan accuracy was evaluated by comparing the measured dose to the institution’s TPS dose as well as the TVS dose. In cases where the TVS was more accurate than the institution by an average of >2%, the institution was identified as having a non-negligible TPS error. Results: Of the ∼200 recalculated plans, the average improvement using the TVS was ∼0.1%; i.e. the recalculation, on average, slightly outperformed the institution’s TPS. Of all the recalculated phantoms, 20% were identified as having a non-negligible TPS error. Fourteen plans failed current IROC-H criteria; the average TVS improvement of the failing plans was ∼3% and 57% were found to have non-negligible TPS errors. Conclusion: IROC-H has developed an independent recalculation system to identify institutions that have considerable TPS errors. A large number of institutions were found to have non-negligible TPS errors. Even institutions that passed IROC-H criteria could be identified as having a TPS error. Resolution of such errors would improve dose delivery for a large number of IROC-H phantoms and ultimately, patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Field To Farm Aggregation For Agricultural Systems

The Fields to Farms methodology illustrates the generation of farm parcels from the Crop Data Layer (CDL), a raster dataset containing 133 categories representing various crop types and land uses. This methodology involves two primary steps: Field Delineation and Farm Aggregation. The code specifically addresses the aggregation of pre-delineated fields within a county to form farms, adhering to predefined criteria for farm size categories. It is assumed that the field delineation process, which involves creating vector polygons from CDL raster, has been completed beforehand, possibly through external tools or methods. Upon initialization, the script processes county-level fields, preparing them for farm aggregation. In the Farm Aggregation phase, the code iteratively combines delineated fields into farms based on specified criteria, continuing until the aggregated farm size meets predefined thresholds derived from data from the 2017 National Agricultural Statistics Service (NASS) census. Throughout this iterative process, the script dynamically adjusts the aggregation to ensure alignment with the desired distribution reported by NASS. The resulting output of the script is a GeoDataFrame containing classified farms, which are subsequently saved as GeoPackage files. These files enable further analysis and visualization, facilitating comprehensive exploration of the farm landscape generated through the methodology.

Paudel, Rajiv [Idaho National Laboratory (INL), Id↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015–2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Direct comparisons of X-ray scattering and atomistic molecular dynamics simulations for precise acid copolymers and ionomers

Designing acid- and ion-containing polymers for optimal proton, ion, or water transport would benefit profoundly from predictive models or theories that relate polymer structures with ionomer morphologies. Recently, atomistic molecular dynamics (MD) simulations were performed to study the morphologies of precise poly(ethylene-co-acrylic acid) copolymer and ionomer melts. Here, we present the first direct comparisons between scattering profiles, I(q), calculated from these atomistic MD simulations and experimental X-ray data for 11 materials. This set of precise polymers has spacers of exactly 9, 15, or 21 carbons between acid groups and has been partially neutralized with Li, Na, Cs, or Zn. In these polymers, the simulations at 120 °C reveal ionic aggregates with a range of morphologies, from compact, isolated aggregates (type 1) to branched, stringy aggregates (type 2) to branched, stringy aggregates that percolate through the simulation box (type 3). Excellent agreement is found between the simulated and experimental scattering peak positions across all polymer types and aggregate morphologies. The shape of the amorphous halo in the simulated I(q) profile is in excellent agreement with experimental I(q). We found that the modified hard-sphere scattering model fits both the simulation and experimental I(q) data for type 1 aggregate morphologies, and the aggregate sizes and separations are in agreement. Given the stringy structure in types 2 and 3, we develop a scattering model based on cylindrical aggregates. Both the spherical and cylindrical scattering models fit I(q) data from the polymers with type 2 and 3 aggregates equally well, and the extracted aggregate radii and inter- and intra-aggregate spacings are in agreement between simulation and experiment. Furthermore, these dimensions are consistent with real-space analyses of the atomistic MD simulations. By combining simulations and experiments, the ionomer scattering peak can be associated with the average distance between branches of type 2 or 3 aggregates. Furthermore, this direct comparison of X-ray scattering data to the atomistic MD simulations is a substantive step toward providing a comprehensive, predictive model for ionomer morphology, gives substantial support for this atomistic MD model, and provides new credibility to the presence of stringy, branched, and percolated ionic aggregates in precise ionomer melts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

PV Fleet Performance Data Initiative Program and Methodology

The US Department of Energy’s PV Fleet Performance Data Initiative has been launched in order to collect and evaluate production data across multiple PV fleet partners. Performance statistics are anonymized, aggregated and shared to represent a snapshot of the US commercial and utility-scale fleet. Production data have been collected from over 1500 systems representing more than 1.3 GWdc capacity. Preliminary analysis indicates median performance loss rates are in line with previous publications of system degradation, on the order of –0.6%/yr to –0.9%/yr (preliminary numbers subject to change). These values are higher than module-only degradation rates which are often used in pro-forma estimates of project performance and economics, potentially exposing owner/operators to increased risk if systems under-perform over time.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2016

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

Driven by major scientific advances in analytical methods, biomonitoring, and computational exposure assessment, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the computationally enabled “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. The AEP framework offers an intuitive approach to successful organization of exposure science data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathway and adverse outcome pathways, completing the source to outcome continuum and setting the stage for more efficient integration of exposure science and toxicity testing information. Together these frameworks form and inform a decision making framework with the flexibility for risk-based, hazard-based or exposure-based decisions.

Teeguarden, Justin G.↗
DOE OSTI2018

Introducing a risk aggregation rationale for mapping risks to aquifers from point- and diffuse-sources–proof-of-concept using contamination data from industrial lagoons

Highlights: • Aquifer risk mapping is introduced for contaminants acting as point/diffuse-sources. • Innovations are framed by risk cells based on Source-Pathway-Receptor-Source (SPRC). • Point-source (Risk Cell 1) and diffuse-source (Risk Cell 2) use sets of algorithms. • Algorithms include MODPATH, DRASTIC, GWPI and are integrated on a modular basis. • Risk Cells 1/2 are aggregated giving fit-for-purpose results as proof-of-concept. - Abstract: Proof-of-concept for a methodology is presented on mapping risks to aquifers impacted from point- and diffuse-sources, where mapping or indexing refers to relative but not absolute values. The methodology is generic but tested by investigating impacts of a risk exposure from industrial wastewater lagoons. The methodology is innovative for using the qualitative Source-Pathways-Receptors-Consequences (SPRC) framework to aggregate risks from both point-sources and diffuse-sources through breaking down a study area into SPRC risk cells. In this paper, two risk cells are required as: (i) Cell 1 is directly impacted from a point-source; and (ii) Cell 2 is impacted by diffuse-sources to slowly contaminate the aquifer by infiltration over a large area. Indexing both types of risk cells generically comprise three tiered processes: (i) binary indexing establishes if a grid cell is at a potential risk or not; (ii) graded indexing measures the strength of the risk pathways from source to receptors; and (iii) local indexing measures intrinsic potentials at the grid cell to propagate the risk. These three processes apply to both point- and diffuse-sources but with different mathematical formulations. The proof-of-concept for the methodology of risk aggregation using the SPRC framework is supported by results of a study area, in which a set of performance metrics are used by comparing with the measurements. The results are found to be fit-for-purpose for serving as proactive management tools and to provide a deeper insight into potential impacts of adverse effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-T-471: Small Field Jaw/MLC Reference Data

Purpose: In recent years the need for small field data of MLCs has increased due to the use of intensity-modulated radiation (IMRT), but moreover the use of stereotactic body radiation (SBRT) has increased, which uses not simply small field sizes, but small jaw and field sizes together. Having reference data for these small fields that is reliable would be invaluable to the physics community. Our study has gathered these values and the data distributions from the Radiological Physics Center's (RPC) site visits between 1990 and the present. Methods: For all measurements, the RPC used a 25 × 25 × 25cm water phantom placed at 100cm SSD. All measurements were made with an Exradin A16 cylindrical ion chamber at an effective depth of 10 cm. A total of 42 Varian machine measurements were used to compose the data for a 6 MV beam and 5 TrueBeam 6 MV flattening filter free (FFF) beams were used for FFF data. Results: Jaw/MLC fields were measured for both 6 MV and 6 MF FFF beams with the jaws and MLCs both at the following field sizes: 6×6, 4×4, 3×3, and 2×2cm. Measurements were normalized to the 10×10 field readings (defined by the jaws and MLC). Spread in the data was minimal and demonstrates a high level of accuracy of acquired data. Conclusion: Small field Jaw/MLC reference data for Varian 6MV and 6 MV FFF beams has been analyzed and presented here, composed of the aggregation of numerous RPC site visits. Obtaining reliable small field data remains difficult, however the RPC has collected high fidelity small field Jaw/MLC data. The data are presented as a reference along with their distributions, in such a way that the physicist can act based upon their own desired agreement with the reference data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A review of privacy in energy applications

As the distribution system continues to experience an increase in distributed energy resource (DER) and electric vehicle (EV) penetration, so does the need for new solutions that can help grid operators manage and leverage their capabilities. This will undoubtedly lead to new operational schemes and business opportunities that will transform the traditional consumer into a prosumer who will be more actively engaged in grid operations. Although the field is still under active development, many of the potential use cases presented in literature or industry are built upon edge computing, two-way communications, and other innovative computational constructs to attain their goals. However, at their core, many use cases assume a great level of data access to aid with the decision-making process, an assumption that may need to be revised to ensure fair and equitable operational processes are maintained. This may be particularly true as edge resources are predicted to participate in retail-side, many-to-many, or peer-to-peer markets and thus may lead to financial impacts if data access considerations are ignored. The need to revise data access mechanisms can be further justified by the introduction of new participants into the operational process, who do not have the same level of trust, nor the incentives to focus on energy delivery as their primary objective. At the same time, more consumers are becoming aware of their own data, and the potential impacts of its abuse. To help solution developers better understand these risks, this report has been developed to offer an initial introduction to the topic of privacy. This is achieved by 1) Highlighting the need for privacy-aware solutions; 2) Encouraging system designers to be inquisitive about the status quo; 3) Documenting the existing threat space; 4) Presenting and evaluating tools that may be helpful towards enabling better privacy postures; and 5) Making recommendations to encourage the adoption of better practices. From a technical perspective, the report focuses on evaluating two potential techniques by applying them to the Transactive Energy Space. Based on the obtained results, it can be established that differential privacy (DP) methods may have limited applicability when highly correlated, time-series data records need to be protected. However, DP may be a powerful tool when it is used to aggregate and analyze mid-size and large-size data sets in a more traditional statistical environment. The second tool under evaluation is threshold cryptography, which can guarantee complete secrecy (and thus privacy) but requires the establishment of key management procedures and dedicated communication channels for key coordination. Therefore, due to its increased computational overhead, the use of threshold cryptography must be weighted using a cost/benefit analysis on a per-application basis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Uncertainty Quantification in Vibro-Acoustics Diagnosis of Alkali-Silica Reaction Degradation in Medium-Sized Concrete Samples

The objectives of this ongoing research project focus on health monitoring and data analytics of concrete slabs containing reactive aggregates and thus subjected to degradation due to alkali-silica reaction (ASR). A controlled concrete slab with four pockets of reactive aggregates (pure silica, wells, placitas, and spratt) was cast at Vanderbilt University and cured in representative conditions to accelerate degradation due to ASR. A set of four concrete samples were also cast and cured at the University of Alabama for ASR testing. Of these four samples, two slabs contained reactive aggregates while the other two had the non-reactive aggregate counterparts mixed throughout the samples. Vibro-acoustic testing was used on these slabs to locate ASR damage within the reactive samples. Vibro-acoustic modulation (VAM) is a vibration-based NDE method that utilizes signatures of nonlinear dynamic interactions on contact surfaces of crack or delamination damage to detect and localize the damage. VAM analysis was conducted on both the Vanderbilt and Alabama samples using multiple variables for damage detection and localization. The results from the data analysis of the vibro-acoustic testing on concrete slabs cured at Vanderbilt University and the University of Alabama are discussed in detail in this report. Results for damage localization are dependent on multiple variables used in the vibro-acoustic modulation experiments. A major focus of this report is to quantify the uncertainty in the diagnosis due to multiple factors and uncertainty sources. The uncertainty quantification methodology presented in this report was applied to VAM-based diagnosis and prognosis. However, the methodology is general, and is capable of being applied to multiple technique that collect spatially distributed data. Future work needs to investigate the incorporation of uncertainty quantification in developing a robust Prognostics and Health Management framework. Digital image correlation is a three-dimensional, full-field, optical NDE technique to measure contour, deformation, vibration, and strain. The application of the digital image correlation technique to study ASR-related degradation on a large concrete specimen at the University of Tennessee is discussed and observations are presented in this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗