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DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗

DOE OSTI2023

Interconnection Cost Analysis in ISO-New England

Electric transmission system operators (ISOs, RTOs, or utilities) require new large generators seeking to connect to the grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. Berkeley Lab has collected interconnection cost data for 194 projects in New England from interconnection studies performed between 2010 and 2021. Project-level cost summary data are available for download on this page. We find: -Interconnection costs have grown over time, especially for projects that withdraw. -Interconnection costs are highest for onshore wind, followed by solar and storage. Natural gas and offshore wind projects tend to cost less to interconnect, in comparison. -Economies of scale exist for solar and possibly storage projects, but not for other resource types. -Wind and solar projects requesting capacity network resource interconnection service have higher interconnection costs, despite being evaluated using the same interconnection standard in the analyzed studies. -Low and high interconnection costs can be found throughout the ISO-NE footprint. -Costs are split fairly evenly between investments at the point of interconnection and within the broader network for active and withdrawn projects, while complete projects incur most costs at the point of interconnection.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Simulation of Reverse Osmosis Membrane Compaction Using Material Point Method (MPM)

Access to fresh drinking water in the future requires effective management of industrial wastewater and water purification from available resources. In this study, we present the simulation methodology and the analysis of a Reverse Osmosis (RO) membrane under various pressure conditions using the Material Point Method (MPM). In contrast to other numerical methods, MPM solves the continuum governing equations on material points in a Lagrangian framework. The method does not require a grid connecting the material points hence making it suitable to simulate large deformations during membrane compaction. The time integration is carried out using the explicit Euler method, while the spatial discretization is performed using linear or cubic-spline shape functions. A series of planar images containing detailed pore structures obtained from X-ray tomography experiments is converted to a three-dimensional collection of material points to simulate the membrane. Compressive loads are applied to the top layer of the membrane to simulate the experiments. The membrane deformation and pore size distribution before and after load application are reported and compared with the experimental measurements. The presentation discusses the numerical methods used, the performance of the solver on high-performance computing machines, and the results of membrane compaction in detail.

ENGINEERING,MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Bicontinuous substrates with reduced pore restriction for CO 2 -selective composite membranes

A substrate with bicontinuous structure is attractive for thin-film composite (TFC) membrane synthesis due to its high surface porosity and well-interconnected bulk morphology. In this study, the vapor-induced phase separation (VIPS) of the casting solution comprising water, 2-pyrrolidone, and polyethersulfone (PES) was studied. By tuning the water addition in the casting solution and water vapor exposure time, the optimized substrate exhibited a bicontinuous surface with a high porosity of 24.2% (or 216/µm 2 pore density), thereby mitigating pore restriction and showing a high CO 2 permeance of 2.94×10 5 GPU. Compared to the benchmark PES substrate with cellular surface pores and lower gas permeance, the use of the bicontinuous substrate led to a 12% increase in the CO 2 permeance of the TFC membrane. The mitigated pore restriction was further rationalized by a resistance-in-series model. The analysis of the lateral diffusion and substrate resistances indicates that an even higher surface pore density of the substrate is required to fully realize the potential of ultra-permeable, CO 2 -selective polymers in the TFC configuration. As a result, the scalability of the VIPS process was demonstrated by the roll-to-roll fabrication of the optimized bicontinuous substrate with a width of 21" and a length of >100'.

42 ENGINEERING↗

DOE OSTI2022

Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems

Here, to facilitate low-cost connected automated vehicle (CAV) system development, this study proposes two interpretable dimensionality reduction techniques in vehicle trajectory prediction, i.e., the piecewise Taylor series approximation (PTA) and the piecewise Fourier series approximation (PFA), to lower computation complexity, reduce device investment, and decrease computation energy consumption. Two benchmarks are developed, the long short-term memory (LSTM)-based model without dimensionality reduction and the LSTM-based model with encoder-decoder (a widely used dimensionality reduction technique). Results show that the four predictions have similar accuracy, and the training time (proportional to computation energy consumption) of models with dimensionality reduction techniques is greatly reduced. The reduction is even more significant when PTA/PFA is used. Sensitivity analysis advises PFA/PTA parameter selections to reduce computation complexity without significant loss of prediction accuracy. Further, the robustness of the LSTM PTA/PFA is proven by the investigation of data noises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

A Review of TSUNAMI Applications

The TSUNAMI-1D and TSUNAMI-3D sequences for generating k eff sensitivity coefficients were first released in SCALE 5 in 2004. Several other tools were introduced in the same release, including primarily the TSUNAMI-IP code for uncertainty analysis and similarity assessments. The TSUNAMI sequences added capabilities for identifying important reactions and data for systems using sensitivity coefficients and also introduced new quantitative tools for rigorously assessing the similarity of systems. Over time, the capabilities of these tools have grown, and their use has expanded into other areas such as nuclear data assessment and nuclear covariance data testing. This paper discusses applications of the TSUNAMI tools for sensitivity calculations, sensitivity/uncertainty (S/U)-based validation, and nuclear data testing. This paper is one of a series of papers at this conference on the development and use of TSUNAMI tools. The other papers address the development of the TSUNAMI methods and the use of TSUNAMI for critical experiment design and optimization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Meteorological data from the Billy Bar site at the East River, Colorado

This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original R script. The Data profiling report is a summary and graphical presentation of the data exploration process for data analysis and model building, so that users could focus on understanding data and extracting insights. The report provides a summary of each variable and does data profiling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A Highly Eccentric Warm Jupiter Orbiting TIC 237913194

The orbital parameters of warm Jupiters serve as a record of their formation history, providing constraints on formation scenarios for giant planets on close and intermediate orbits. Here, we report the discovery of TIC 237913194b, detected in full-frame images from Sectors 1 and 2 of the Transiting Exoplanet Survey Satellite (TESS), ground-based photometry (Chilean–Hungarian Automated Telescope, Las Cumbres Observatory Global Telescope), and Fiber-fed Extended Range Optical Spectrograph radial velocity time series. We constrain its mass to M P = 1.942 -0.091 +0.091 M J and its radius to R P = 1.117 -0.047 +0.054 R J , implying a bulk density similar to Neptune’s. It orbits a G-type star (M &lowast; = 1.026 -0.055 +0.057 M ⊙ , V = 12.1 mag) with a period of 15.17 days on one of the most eccentric orbits of all known warm giants (e ≈ 0.58). This extreme dynamical state points to a past interaction with an additional, undetected massive companion. A tidal evolution analysis showed a large tidal dissipation timescale, suggesting that the planet is not a progenitor for a hot Jupiter caught during its high-eccentricity migration. TIC 237913194b further represents an attractive opportunity to study the energy deposition and redistribution in the atmosphere of a warm Jupiter with high eccentricity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Unmanned aircraft system (UAS) detection and assessment via temporal intensity aliasing

A method and system for temporal frequency analysis for identification of unmanned aircraft systems. The method includes obtaining a sequence of video image frames and providing a pixel from an output frame of the video; generating a fluctuating pixel value vector; examining the fluctuating pixel value vector over a period of time; obtaining the frequency information present in the pixel fluctuations; summing the frequency coefficients for the vectorized pixel values from the fluctuating pixel value vector; obtaining an image representing a two dimensional space based on the summed center frequency coefficients; generating a series of still frames equal to a summation of the center frequency coefficients for pixel variations; and combining the temporal information into spatial locations in a matrix to provide a single image containing the spatial and temporal information present in the sequence of video image frame.

Woo, Bryana Lynn↗

DOE OSTI2023

Unmanned aircraft system (UAS) detection and assessment via temporal intensity aliasing

Woo, Bryana Lynn↗

DOE OSTI2021

Preliminary Analysis of Source Physics Experiment Explosion-Triggered Microseismicity Using the Back-Projection Method

A series of four chemical explosions were detonated in a deep borehole within the Yucca Flat Dry Alluvium Geology (DAG) at the Nevada National Security Site between 2018 and 2019. The two larger chemical explosions of 50 tons (DAG-2) and 10 tons (DAG-4) TNT equivalent yield triggered energetic aftershock sequences numbering 1392 and 347 microearthquakes, respectively, within the first 10 days. No significant aftershock activity was observed for the two smaller 1-ton explosions (DAG-1 and DAG-3). Here, we used a back-projection method based on travel-time migration and stacking of signal-to-noise ratio traces to detect, associate and locate aftershocks from a subset of 22-geophones within a larger 2 urn:x-wiley:21699313:media:jgrb54910:jgrb54910-math-0001 2 km seismic array surrounding the borehole. The aftershocks located within 300 m of the borehole and the depths were above the working points of 300 and 50 m depths of DAG-2 and DAG-4, respectively, ruling out triggering slip on geologic faults or disturbances beneath neighboring collapse craters. DAG-2 and DAG-4 aftershocks decayed at similar rates, with power-law exponents of p = 1.48 and p = 1.49, respectively. These decay rates are comparable to aftershocks sequences triggered by earthquakes and historical nuclear explosions at Yucca Flat. A smooth power-law aftershock decay within the first 10 days suggests a triggering mechanism from explosion generated stress relaxation due to the diffusion of high gas pressures in the cavity and radial fractures. A more random and episodic aftershock rate would be expected due to cavity collapse or falling rubble in chimney formation.

58 GEOSCIENCES↗

DOE OSTI2020

Ultrafast Relaxations in Ruthenium Polypyridyl Chromophores Determined by Stochastic Kinetics Simulations

Maximizing the efficiency of solar energy conversion using dye assemblies rests on understanding where the energy goes following absorption. Transient spectroscopies in solution are useful for this purpose, and the time-resolved data are usually analyzed with a sum of exponentials. This treatment assumes that dynamic events are well separated in time, and that the resulting exponential prefactors and phenomenological lifetimes are related directly to primary physical values. Such assumptions break down for coincident absorption, emission, and excited state relaxation that occur in transient absorption and photoluminescence of tris(2,2'-bipyridine)ruthenium(2+) derivatives, confounding the physical meaning of the reported lifetimes. Here, we use inductive modeling and stochastic chemical kinetics to develop a detailed description of the primary ultrafast photophysics in transient spectroscopies of a series of Ru dyes, as an alternative to sums of exponential analysis. Commonly invoked three-level schemes involving absorption, intersystem crossing (ISC), and slow nonradiative relaxation and incoherent emission to the ground state cannot reproduce the experimentally measured spectra. The kinetics simulations reveal that ultrafast decay from the singlet excited state manifold to the ground state competes with ISC to the triplet excited state, whose efficiency was determined to be less than unity. The populations predicted by the simulations are used to estimate the magnitudes of transition dipoles for excited state excitations and evaluate the influence of specific ligands. The mechanistic framework and methodology presented here are entirely general, applicable to other dye classes, and can be extended to include charge injection by molecules bound to semiconductor surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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 OSTI2020

Remote Sensing-Informed Zonation for Understanding Snow, Plant and Soil Moisture Dynamics within a Mountain Ecosystem

In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Nonresonant coherent amplitude transfer in attosecond four-wave-mixing spectroscopy

Attosecond four-wave mixing (FWM) spectroscopy using an extreme ultraviolet (XUV) pulse and two noncollinear near-infrared (NIR) pulses is employed to measure Rydberg wave packet dynamics resulting from XUV excitation of a 3s electron in atomic argon into a series of autoionizing 3s –1 np Rydberg states ~29 eV. The emitted signals from individual Rydberg states exhibit oscillatory structure and persist well beyond the expected lifetimes of the emitting Rydberg states. These results reflect substantial contributions of longer-lived Rydberg states to the FWM emission signals of each individually detected state. A wave packet decomposition analysis reveals that coherent amplitude transfer occurs predominantly from photoexcited 3s –1 (n + 1) p states to the observed 3s –1 np Rydberg states. The experimental observations are reproduced by time-dependent Schrödinger equation simulations using electronic structure and transition moment calculations. Finally, the theory highlights that coherent amplitude transfer is driven nonresonantly to the 3s –1 np states by the NIR light through 3s –1 (n + 1)s and 3s –1 (n – 1)d dark states during the FWM process.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

Effect of Dopant Compensation on the Conductivity of the Intrinsic poly-Si Isolation Region in Passivated IBC Silicon Solar Cells

The performance of IBC solar cells depends on the ability of this region to electrically isolate the doped fingers so that shunting does not occur and reduce the cell fill factor. We report on simulations which model the region between the p- and n-type poly-Si fingers of interdigitated back contact (IBC) solar cells as a series of resistors extending from one doped finger to the other. We demonstrate that the existence of a well compensated region between the doped fingers is enough to prevent shunting and loss of cell performance, despite contamination of dopants from the opposing dopant fingers. We show through these simulations that a net doping concentration below ~1018 cm-3 will enable a high resistivity and identify the conditions under which this highly resistive region forms despite imperfect finger edges. Additionally, we apply this analysis to tails measured by time-of-flight secondary ion mass spectrometry and confirm this hypothesis for our experimentally measured tails.

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

DOE OSTI2024

Wildfire‐induced increases in photosynthesis in boreal forest ecosystems of North America

Abstract Observations of the annual cycle of atmospheric CO 2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape‐level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat‐derived land cover time series to characterize plant functional types (PFTs), and solar‐induced fluorescence (SIF) from the Orbiting Carbon Observatory‐2 (OCO‐2) as a proxy for photosynthesis. We analyze these datasets to characterize post‐fire changes in plant succession and photosynthetic activity using a space‐for‐time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid‐succession yield enhancements in SIF by 8–40% during June and July for 2‐ to 59‐year stands relative to pre‐fire controls. From the analysis of post‐fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long‐term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early‐ and mid‐summer. Second, fire appears to facilitate a long‐term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO 2 exchange without a close coupling to long‐term increases in carbon storage.

Biodiversity & Conservation↗