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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Advanced Method Optimization for Sampling and Analysis Instrumentation

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. In conclusion, this strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical technique.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Primary observables for indirect searches at colliders

We consider the complete set of observables for collider searches for indirect effects of new heavy physics. They consist of SU(3) C × U(1) EM invariant interaction terms/operators that parameterize deviations from the Standard Model. We show that, un der very general assumptions, the leading deviations from the Standard Model are given by a finite number of ‘primary’ operators, with the remaining operators given by ‘Mandelstam descendants’ whose effects are suppressed by powers of Mandelstam variables divided by the mass scale M of the heavy physics. We explicitly determine all 3 and 4-point primary operators relevant for Higgs signals at colliders by using the correspondence between on-shell amplitudes and independent operators. We give a detailed discussion of the methods used to obtain this result, including a new analytical method for determining the independent operators. The results are checked using the Hilbert series that counts independent operators. We also give a rough sketch of the phenomenology, including unitarity bounds on the interaction strengths and rough estimates of their importance for Higgs decays at the HL-LHC. These results motivate further exploration of Higgs decays to $Z\overline{f}f$, $W\overline{f}f'$, $γ\overline{f}f$, and $Zγγ$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

“Innervated” Pipelines: A New Technology Platform for In-Situ Repair and Embedded Intelligence

The overall vision pursued under the project would ultimately enable capability for real-time operational monitoring of natural gas (and other) pipeline infrastructures through the combination of in-situ repair and rehabilitation methods with embedded fiber optic sensing and associated data analytics methods and platform tools. Benefits to the public include more resilient and robust natural gas pipeline infrastructures with the potential to expand the applications of developed technologies under the program to other important areas of critical energy infrastructure in the future such as H2 pipelines and infrastructure, municipal and other civil infrastructure, as well as subsurface oil, gas, and geothermal infrastructure applications. The program has successfully demonstrated feasibility for all of the critical enabling elements and serves as a foundation for future technology maturation, deployment, and customization for other potential applications and high-priority needs into the future. Several underlying innovations have shown potential for commercial deployment and are the subject of continued development and technology transfer based upon patents submitted during the project duration.

02 PETROLEUM↗
DOE OSTI2024

Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach

Modeling the evolution of voids during plastic flow as well as their effects on plastic dissipation is critical for both component manufacturing and lifetime estimation purposes. To this end, we propose a rate-dependent constitutive model to homogenize the effects of semi-randomly distributed voids on single crystal plasticity whilst capturing void interaction and plastic anisotropy. Here, this present work focuses on the case of face centered cubic crystals to introduce an anisotropic gauge function applicable within the crystal plasticity formalism. The approach combines analytical methods to describe the micromechanics of the system in combination with symbolic regression to capture analytically intractable mechanisms from data. The hybrid framework uses a physics-informed genetic programming-based symbolic regression algorithm to solve a multiform optimization problem simultaneously producing a new gauge function and a new strain rate equation. This is also a multi-objective optimization problem with many competing objectives. A new search and selection step is introduced to the genetic algorithm that promotes convergence toward a global solution that better satisfies all the objectives. Overall, the symbolic equations produced leverage data-driven methods to achieve greater accuracy than comparable alternatives on an analytically intractable problem while maintaining model transparency.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Analysis of X-ray images and spectra (aXis2000): A toolkit for the analysis of X-ray spectromicroscopy data

Spectromicroscopy refers to analytical methods that combine imaging and spectroscopy to provide detailed, spatially resolved analytical information about a sample, such as the type and quantitative spatial distributions of chemical components, geometric or magnetic alignment information, crystal structure, etc. The analysis of X-ray images and spectra (aXis2000) software described in this work provides a set of routines within a single, integrated, graphical-oriented package to read, display, manipulate and analyze spectromicroscopy data, with particular focus on soft X-ray spectromicroscopy methods such as scanning transmission X-ray microscopy (STXM), X-ray photoemission electron microscopy (XPEEM), scanning photoelectron X-ray microscopy (SPEM) and transmission X-ray microscopy (TXM). Here, this free software is described and compared to other software that can provide similar or complementary capabilities. Examples of spectromicroscopic analyses using advanced features of aXis2000 are provided.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems

For critical infrastructure restoration planning, the real-time scheduling and coordination of system restoration efforts, the key in decision-making is to prioritize those critical components that are out of service during the restoration. For this purpose, there is a need for component importance analysis. While it has been investigated extensively for individual systems, component importance considering interdependence among transmission, distribution and communication (T&D&C) systems has not been systematically analyzed and widely adopted. In this study, we propose a component importance assessment method in the context of interdependence between T&D&C networks. Analytic methods for multilayer networks and a set of metrics have been applied for assessing the component importance and interdependence between T&D&C networks based on their physical characteristics. The proposed methodology is further validated with integrated synthetic Illinois regional transmission, distribution, and communication (T&D&C) systems, the results reveal the unique characteristics of component/node importance, which are strongly affected by the network topologies and cross-domain node mapping.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A review of rare earth elements and yttrium in coal ash: Content, modes of occurrences, combustion behavior, and extraction methods

Rare earth elements and yttrium (REY) have attracted considerable attention over the last decade because of their vital roles in clean energy, consumer product, national defense and security applications, among other uses. Due to the retention of REY during coal burning, coal combustion ash is considered as potential alternative sources for REY. Understanding the content, speciation, retention and/or transformation behavior of REY during coal combustion not only expands our knowledge of the combustion behavior of the trace elements in coal, but also provides basis for modeling REY partitioning during coal combustion and for developing economically viable REY recovery technologies. This review makes a critical summary of recent progress in the study of REY in coal ash. The contents and the extraction potentials of REY in coal ash derived from 15 major coal-producing countries worldwide were summarized and evaluated. Various analytical methods for determining REY bulk contents and speciation, together with the solid sample pretreatment, analytical accuracy and precision, advantages and disadvantages were summarized and compared. Modern analytical approaches combined indirect methods (e.g., sequential extraction) shed light on the physical distribution, mineralogy, and the chemical state of REY in coal ash. Three types of REY occurrences in coal ash, including Si-Al glassy association, discrete minerals or compounds, and organic association (bound with unburned carbon) were defined in the review. The glassy association can be further divided into REY minerals closely bound to glass phases and dispersed throughout the glassy structure. REY partitioning in various emission streams, the size distribution, and their enrichment behavior in coal ash were discussed. Additionally, thermal behavior and transformation of various REY forms in coal during combustion process, including organic-associated REY, REY phosphates, REY carbonates, clay-bound REY and among others were summarized. Two possible retention mechanisms of REY by aluminosilicate glass at boiler temperature were proposed: the incorporation of the individual REY phases into the glass as inclusions and the diffusion of REY phases throughout Si-Al glass structures in the melting process. Feed coal mineral types, mineral-mineral associations, boiler conditions, and other factors control the retention process. After coal combustion, the speciation of REY in fly ash may be modified by the reactions of REY phases with flue gas components. Further, an overview of REY transformation mechanisms during coal combustion was deeply discussed. Finally, current extraction techniques for REY recovery from coal combustion ash were introduced. Future outlooks and research problems were also identified.

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

Deep eutectic solvents as green and sustainable diluents in headspace gas chromatography for the determination of trace level genotoxic impurities in pharmaceuticals

Genotoxic impurities (GTIs) are potential carcinogens that need to be controlled down to ppm or lower concentration levels in pharmaceuticals under strict regulations. The static headspace gas chromatography (HS-GC) coupled with electron capture detection (ECD) is an effective approach to monitor halogenated and nitroaromatic genotoxins. Deep eutectic solvents (DESs) possess tunable physico-chemical properties and low vapor pressure for HS-GC methods. In this study, zwitterionic and non-ionic DESs have been used for the first time to develop and validate a sensitive analytical method for the analysis of 24 genotoxins at sub-ppm concentrations. Compared to non-ionic diluents, zwitterionic DESs produced exceptional analytical performance and the betaine: 7 (1,4- butane diol) DES outperformed the betaine: 5 (1,4-butane diol) DES. Limits of detection (LOD) down to the 5-ppb concentration level were achieved in DESs. Wide linear ranges spanning over 5 orders of magnitude (0.005–100 µg g –1 ) were obtained for most analytes with exceptional sensitivities and high precision. The method accuracy and precision were validated using 3 commercially available drug substances and excellent recoveries were obtained. Finally, this study broadens the applicability of HS-GC in the determination of less volatile GTIs by establishing DESs as viable diluent substitutes for organic solvents in routine pharmaceutical analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Gas‐Phase Ion‐Molecule Interactions in a Collision Reaction Cell with ICP‐MS / MS : Investigations with CO 2 as the Reaction Gas

Carbon dioxide (CO 2 ) was used as a reaction gas to investigate the gas‐phase ion‐molecule interactions using the Agilent 8900 ICP‐MS/MS. A solution containing forty‐five elements representative of the periodic table was used to supply the ions to react with CO 2 in the collision/reaction cell (CRC). The only significant product ions formed were monoxides. The general reactivity was shown to be consistent with density functional theory (DFT)‐predicted reaction enthalpies, such that all predicted exothermic reactions produced product ions at levels of at least 1% of the unreacted ion. Most endothermic reactions observed had sufficient kinetic energy in excess of the reaction enthalpies. Our results suggest that reaction enthalpy is a reasonable predictor of reactivity with CO 2 on the timescales of the interactions in non‐thermal ICP‐MS/MS systems. The ease and rapidity of data collection with the ICP‐MS/MS and DFT calculations using the NWChem suite has value given the scarcity of thermochemical data of CO 2 reactions in the literature. These studies are especially useful for the identification of targeted reaction chemistries to be leveraged for analytical method development, such as for the inline separation of isobaric interferences from analytes of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Comparison and validation of the QuEChERSER mega-method for determination of per- and polyfluoroalkyl substances in foods by liquid chromatography with high-resolution and triple quadrupole mass spectrometry

Instances of food contamination with per- and polyfluoroalkyl substances (PFAS) continue to occur globally, but sample preparation and analytical methods are quite limited and often monitor for a small percentage of known PFAS. This study aimed to evaluate, validate, and compare performance of two instruments with the recently developed “quick, easy, cheap, effective, rugged, safe, efficient, and robust” (QuEChERSER) sample preparation mega-method – a method developed to monitor chemicals over a broad range of physicochemical properties. Initial evaluation of the QuEChERSER mega-method for determination of PFAS in food demonstrated recoveries, matrix interferences, and co-extractive removal comparable to (or better than) US Food and Drug Administration (FDA) and USDA Food Safety and Inspection Service (FSIS) methods. Subsequent validation of QuEChERSER in beef, catfish, chicken, pork, liquid eggs, and powdered eggs on a high-resolution mass spectrometer achieved acceptable recoveries (70–120%) and precision (RSDs ≤20%) for all 33 target analytes at the 1 and 5 ng g –1 levels and 67–88% of analytes at the 0.1 ng g –1 level, depending on the matrix. Additional validation was performed by tandem mass spectrometry on a triple quadrupole instrument. This approach provided no non-detects and better recoveries at the 0.1 ng g –1 level than the HRMS method but exhibited more variability at 1 and 5 ng g –1 spiking levels. Analysis of NIST SRMs 1946 and 1947 gave accuracies of 70–117%. Furthermore, these results demonstrate the capability of combining PFAS analysis with a mega-method previously validated for 350 analytes, while collecting non-target data for future retrospective analysis of emerging alternatives with a high-resolution mass spectrometry method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Comparative investigations of multi-fidelity modeling on performance of electrostatically-actuated cracked micro-beams

Silicon is a commonly used material for the fabrication of beams for use in micro-electrical-mechanical systems (MEMS). Although silicon is a brittle material, it has been shown to accumulate fatigue damage at the micro-scale. Understanding the effect this has on the overall device performance is critical to the design of reliable devices. Analytical methods for modeling damage provide expedient results but are limited by broad modeling assumptions. Numerical models account for more detailed physical phenomena but can be computationally intensive. In this work, two different crack scenarios are modeled using both analytical techniques and 3D computational simulations. First, the effects of a single surface crack on the static deflection and natural frequency of an electrostatically actuated micro-beam are formulated and compared. Then, a new method for approximating damage associated with realistic distributed crack networks is formulated for use in an analytical model and numerical simulations. A method for utilizing experimentally derived crack statistics to inform the analytical and numerical distributed crack models is developed. Good agreement between the analytical and numerical models is obtained for both crack scenarios. Altogether, these models can be used to effectively simulate a variety of damage and fatigue behaviors in silicon-based MEMS devices.

42 ENGINEERING↗
DOE OSTI2020

Monthly Sewer Monitoring Report for LLNL Livermore Site, April 2020

Lawrence Livermore National Laboratory (LLNL) collects effluent samples from its sewer outfall at the B196 Sewer Monitoring Station (SMS). Effluent flow-proportional composite samples are collected at the SMS on a daily (midnight-to-midnight), weekly (Thursday through Wednesday), and “monthly” (composited from daily) basis; effluent grab samples are also collected each month at that same location. Certified contract laboratories analyzes these compliance samples, and results (Tables 1–3) are used to establish LLNL compliance with the 2019–2020 Wastewater Discharge Permit (Permit 1250) granted by the City of Livermore Water Resources Division (WRD). Supplemental analyses for biochemical oxygen demand (BOD) and total suspended solids (TSS) are performed onsite and reported (Table 4) for the calculation and assessment of sewer service charges. Quarterly, effluent is sampled for metals (24-hour composite) and cyanide (grab sample) concentrations (mg/L). The results are presented in Table 5. In addition to the sampling noted above, the SMS continuously monitors LLNL sewage effluent in real-time for flow rate, pH, metals, and radioactivity. Standard measurement methods are used to monitor flow and pH to determine permit compliance. Unique nonstandard analytical methods monitor for the presence of metals and radioactivity using x-ray fluorescence (XRF) and gamma spectroscopy, respectively. If an anomalous condition is detected by the monitoring system, an alarm activates LLNL’s Sewer Diversion Facility (SDF) and Livermore Water Reclamation Plant (LWRP) operators are notified that a potential release has occurred.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2023

Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

Yucesan, Yigit↗
DOE OSTI2021

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell's survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. In this work, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13 C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Progress report on analytic and numerical studies of x-ray-induced impulse

In a variety of high-energy-density (HED) systems, x-rays of a given energy are used to generate shockwaves, bulk motion, and impulse in materials. As radiation energy is deposited within the material, the material is heated. The number of photons of a certain wavelength absorbed is determined by the spectral intensity of the radiation and the material’s opacity evaluated at that wavelength. This heating results in a pressure increase dictated by the material’s equation of state. Depending on the intensity of the absorbed radiation, it may also cause the material to change phase into a liquid, gas, or plasma. The increased pressure drives the heated surface layer to blow off, imparting impulse and sending a compressive wave into the bulk of the material. Additionally, the compression wave interacts with the solid boundary of the material, resulting in a tensile wave that may cause the material to spall. The impulse generated by the deposition of x-ray energy within the sample can be modeled using purely analytical methods, e.g. the Bethe, Bade, Averell, and Yost (BBAY) model. However, the blow-off process is rather complicated, and proper modeling efforts must account for material ejected by spallation, vaporization, jetting, and plasma ablation. For this reason, analytical models have an unclosed term describing the final energy of the blown-off material Ef(z). Prior modeling efforts have arbitrarily fixed this at some value or modeled it using a limiting set of thermodynamic assumptions. The work we are currently performing uses validated simulations using sophisticated photon transport, equation of state, and strength models/data to provide a fit for Ef(z) that is useful for predictive calculation of impulse. We will apply our methodology and show results for different materials and x-ray sources. This work is particularly useful for the design of experiments studying x-ray impulse generation. The present report is outlined as follows. Section 1.2 describes a series of HED experiments investigating x-ray-generated impulse in materials. Section 1.3 describes the computational approach we employ in this study, and presents validation results against the aforementioned experiments. Section 1.4 introduces analytical models for impulse generation, as well as our method for utilizing impulse from simulations to close the models. We discuss the concept of impulse-spectrum sensitivity, it’s application to uncertainty quantification, and derive a very useful analytical expression for it in 1.5. We then summarize recent progress in this project and discuss future work in section 1.6.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling and analyzing parasitic parameters in high frequency converters

This research focuses on electromagnetic interference (EMI) / electromagnetic compatibility (EMC) design and analysis in power electronics systems. To limit the EMI under the standards, different methods and strategies are investigated. Parasitic parameters of high frequency (HF) transformer are analyzed using a novel analytical method, finite element method (FEM), and experimental measurements for different structures and windings arrangements. Also, the magnetic field, electric field, electric displacement, and electric potential distribution are simulated and analyzed. Moreover, a high voltage system is considered and analyzed to improve the EMC. The EMI propagation paths are analyzed. The EMI noise level of the system is obtained and compared to the IEC61800-3 standard. To improve the EMC, the parasitic parameters of the transformer, as the main path of EMI circulation, are analyzed and optimized to block the propagation. Furthermore, the geometry structure of the HF transformer is optimized to lower the parasitics in the system. Three pareto-optimal techniques are investigated for the optimization. The models and results are verified by 3D-FEM and experimental results for several given scenarios. Furthermore, the EMC modeling and conducted EMI analysis are developed for a system including an AC-DC-DC power supply (rectifier and dual active bridge (DAB) converter). Moreover, the common mode (CM) EMI noise propagation through the system is discussed and the noise sources and effect of components on the noise are analyzed. Additionally, the CM impedance of different parts of the system and the noise levels are discussed. Finally, EMI attenuation techniques were applied to the system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Interpreting strain tensor data to characterize and monitor reservoirs for CO2 storage and other applications

Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and this has opened the door to new opportunities for characterization and monitoring. We have deployed strainmeters and then conducted injection well tests in an underlying reservoir at 530m depth. The resulting data indicated that the horizontal strain at shallow strainmeters (30 to 40m depth) was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 n/d over a few days. The signal at two strainmeters at shallow depth were consistent, although the magnitude of the horizontal strains were different reflecting the different radial directions from the well. The signal at a deep strainmeter deployed at reservoir depth was much different, with tensile vertical strains and compressive horizontal strains. These data can be interpreted by inverting poroelastic forward models developed using numerical and analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. This model is fast and can be inverted to estimate reservoir stiffness and geometry. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. The proxy model is periodically updated and refined using the finite element model to ensure accuracy. This approach significantly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. We have shown that the strain tensor in the caprock is sensitive to pressure in the reservoir, boundaries in the reservoir, and pressure in the caprock caused by leaks. These results indicate that coupling strain tensor data with inversion has the potential to help evaluate reservoirs during initial characterization, and to monitor them during the CO2 injection and storage process.

Murdoch, Larry↗