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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 181 records · Page 10

Alkali triel chalcogenide nanocrystals: a molecular reactivity approach to ternary phase selectivity

Alkali-metal-based materials are promising building blocks for energy conversion and storage technologies. Here, we use a molecular reactivity-based solution-phase approach to selectively synthesize multiple phases and specific polymorphs of lithium- and sodium-containing triel chalcogenide nanocrystals, LiTrCh 2 , NaTrCh 2 , and NaTr 3 Ch 5 , where Tr = Ga and In and Ch = S, Se, and Te. Analogous to the case of binary II–VI and III–V tetrahedral semiconductors, where the two commonly isolated zinc blende and wurtzite polymorphs are separated by only 1–50 meV f.u. −1 , we find that LiTrCh 2 nanocrystals easily adopt tetragonal (chalcopyrite) and orthorhombic polymorphs separated by only 2.7–6.2 meV f.u. −1 Because of this small energy difference, soft colloidal synthesis succeeds in accessing either one of these polymorphs, depending on the specific dichalcogenide precursor used. Highly reactive diethyl diselenide favors the thermodynamically more stable tetragonal I$\bar{4}2$d phase, whereas mildly reactive diphenyl diselenide favors the kinetic, metastable orthorhombic Pna2 1 phase. Density functional theory calculations confirm the relative energies among multiple LiTrCh 2 polymorphs and also model the observed powder X-ray diffraction pattern of a new C2 NaIn 3 Te 5 phase. 7 Li, 69 Ga, and 77 Se solid-state NMR spectra are consistent with phase-pure ternary LiGaSe 2 nanocrystals. A majority of the nanocrystal compositions are visible-light emitters. This work opens the door to new Li/Na-based ternary triel chalcogenide nanostructures for energy storage and conversion applications.

Pavel, Md Riad Sarkar [Iowa State Univ., Ames, IA ↗

A semblance measure for model comparison

Algorithmic and computational advances have made it possible that geophysical survey and earth model design can be aided by many systematic trial inverse-modelling runs with synthetic data. Such may, for example, come up in machine-learning approaches. Automated image appraisal pertaining to such applications will involve common statistical tests for goodness-of-data fit as a primary evaluation method. However, solution non-uniqueness may render multiple images equivalent in terms of their data fit, requiring secondary categorizers. A logical choice for classifying synthetic-imaging results quantifies the goodness of model fit where a known reference model replaces the observational input. The task of model intercomparison in terms of measuring the resemblance to the reference model poses challenges to common distance-based metrics like root mean square error and mean absolute error. First, distance-based metrics can introduce spurious contributions when smooth models with fuzzy target contours are to be compared against a sharp reference. Second, large differences due to parameter-estimation overshoots can dominate distance metrics. Here, we propose a remedy that is referred to as semblance and is based on the idea of logistic functions, where a binary-dependent variable adds non-zero or zero accumulation terms for the, respectively, passing or failing of preset target thresholds. This classifying approach is amenable to an objective where model feature recognition is primary. Numerical comparisons to distance-based metrics provide evidence for the advantages of the semblance in view of this objective. Geophysical imaging in conjunction with machine-learning is seen as a benefitting upcoming application area.

58 GEOSCIENCES↗

Distribution System Blackstart and Restoration Using DERs and Dynamically Formed Microgrids

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids (MGs), sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated MGs. In DERs-aided blackstart, the continuous operation of these MGs is limited by the finite energy capacity of commonly used blackstart units like battery energy storage (BES)-based gridforming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between dynamic MGs and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition is developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottomup blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. In conclusion, the proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Austin Chalk/Eagle Ford Field Laboratory (ACEFFL)

This project, led by Texas A&M University, developed a science-based field laboratory in the Austin Chalk/Eagle Ford Shale Formation to determine the stimulated reservoir volume created by the fracturing of multiple wells. Utilizing newly developed monitoring solutions, the team delivered comprehensive high-quality field data on the extent and morphologies of productive fractures created from these wells. Advanced field monitoring was complemented by laboratory testing on cores and drill cuttings, and coupled modeling for design, prediction, calibration, and code validation. The Austin Chalk/Eagle Ford Field Laboratory (ACEFFL) was hosted by SM Energy, which provided access to the wells of opportunity.

02 PETROLEUM↗

Bench-Scale Electrolytic Dissolution of Quarter-Scale FCA Cans

In 2016, the Savannah River National Laboratory (SRNL) led, in support of and under sponsorship of the Department of Energy’s National Nuclear Security Administration (DOE/NNSA) Office of Material Management and Minimization (M3), the removal and transfer of the plutonium based Fast Critical Assembly (FCA) fuel from the Japan Atomic Energy Agency (JAEA) Tokai facility to the Savannah River Site (SRS). The team also included JAEA, multiple organizations in Savannah River Nuclear Solutions (SRNS), International Nuclear Services, and many other entities. The FCA fuel removal project completion was a key deliverable for M3 to the 2016 Nuclear Security Summit and constituted the largest inventory of weapons-usable plutonium removed under the nonproliferation program. The FCA materials consist of thousands of stainless steel (SS) clad plates and hundreds of SS clad rods. The FCA fuel elements were packaged in a carrier can and stored at SRS pending disposition of the fuel. Following an assessment of candidate disposition options, SRNS identified electrolytic dissolution (ED) as the most promising disposition option for the FCA plates and their preferred option was endorsed by DOE. This option entails electrochemically dissolving the entire FCA carrier can with fuel elements and was based on bench-scale laboratory testing and historical work on processing SS-clad and zirconium-clad uranium-based fuel in the H-Canyon electrolytic dissolver (last operated in 1980). The FCA plate consists of a plutonium-aluminum metal alloy core hermitically sealed in SS cladding.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Microgrid Design Toolkit (MDT) User Guide: Software v1.4

The MDT is a decision support software that can provide the information needed to identify optimal microgrid designs in the early stages of the design process. MDT searches the trade space of alternative microgrid designs in terms of user-defined objectives, such as performance, reliability, and cost. It produces a Pareto frontier of solutions embodying the efficient tradeoffs amongst multiple user-defined objectives.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Review of Distributed Wind Interconnection Technology and Standards

The objectives of this report are to discuss the uniqueness of power converters in distributed wind (DW), identify challenges in complying with existing standards, prioritize technological innovations to facilitate interconnection of DW energy systems, and, where necessary, propose potential clarifications or revisions to standards. This report evaluates IEEE 1547-2018, IEEE 1547.1-2020, and UL 1741 SB standards and their implications for DW. A standards review and interviews of DW industry stakeholders were conducted to identify challenges in the application of current standards to DW. The study concludes with a combination of recommendations of pathways towards facilitating DW product commercialization and DW interconnection, which draws on participation from multiple stakeholders in the space. These solutions can continue to be explored to improve DW standards compliance, improve the applicability of the standards to DW, reduce certification testing barriers, and ensure the safe and reliable operation of DW energy resources and electric power systems.

17 WIND ENERGY↗

Plant Biosystems Design for a Carbon-Neutral Bioeconomy

Our society faces multiple daunting challenges including finding sustainable solutions towards climate change mitigation; efficient production of food, biofuels, and biomaterials; maximizing land-use efficiency; and enabling a sustainable bioeconomy. Plants can provide environmentally and economically sustainable solutions to these challenges due to their inherent capabilities for photosynthetic capture of atmospheric CO 2 , allocation of carbon to various organs and partitioning into various chemical forms, including contributions to total soil carbon. In order to enhance crop productivity and optimize chemistry simultaneously in the above- and belowground plant tissues, transformative biosystems design strategies are needed. Concerted research efforts will be required for accelerating the development of plant cultivars, genotypes, or varieties that are cooptimized in the contexts of biomass-derived fuels and/or materials aboveground and enhanced carbon sequestration belowground. Here, we briefly discuss significant knowledge gaps in our process understanding and the potential of synthetic biology in enabling advancements along the fundamental to applied research arc. Ultimately, a convergence of perspectives from academic, industrial, government, and consumer sectors will be needed to realize the potential merits of plant biosystems design for a carbon neutral bioeconomy.

59 BASIC BIOLOGICAL SCIENCES↗

Preventing Voltage Divergence in Series-Stacked Bidirectional Battery Energy Storage DC-DC Converter under Asymmetrical Operating Conditions

DC stacked topologies have gained popularity for interfacing individual photovoltaic panels to a high-voltage DC bus. A similar approach can be used to interface individual battery packs to the same DC bus, which allows active balancing of battery substrings and the use of lower voltage switches. Any imbalance in battery current, however, can cause the converter output voltages to diverge. This paper describes the source of voltage divergence and demonstrates a solution that allows seamless stacking of multiple battery packs to be interfaced to a high voltage DC bus. To prevent the voltage divergence phenomenon and allow asymmetrical battery charging for efficient battery state of charge management, the PI-based decentralized voltage control enabling both capacitor voltage sharing and asymmetrical capacitor voltage control is proposed. The effectiveness of the decentralized voltage control strategy is demonstrated through controller hardware-in-the-loop test results.

Kim, Namwon↗

Efficiency, Power Loss, and Power Factor Measurement of Quadrupole Magnet Power Supplies at the Spallation Neutron Source

The linear accelerator (LINAC) quadrupole magnets are powered by 42 silicon-controlled rectifier (SCR) based power supplies at the Spallation Neutron Source (SNS) facility of Oak Ridge National Laboratory. These 35V, 525A power supplies are bulky, inefficient and require both air and water cooling. The reliability of the SNS facility is impacted due to water leaks internal to power supplies and current readback issues associated with their unique control system interface, resulting in multiple downtime events. Hence, an alternate solution is necessary for the continued reliable operation of the SNS. To mitigate the above-mentioned problems, this paper proposes the use of off-the-shelf Switch Mode Power Supplies (SMPS) rated for 20V, 500A with serial control interface. These SMPS are air-cooled, more efficient and more compact owing to their switching speeds of approximately 160 kHz. The performance enhancements of the SMPS in comparison with the existing SCR power supply are discussed in detail in this paper. The features of the SMPS, along with experimental results for both power supplies, like efficiency, power losses and stability, are presented. Ongoing work is also discussed.

Harave, Sudarshan↗

Raman Spectroscopy Coupled with Chemometric Analysis for Speciation and Quantitative Analysis of Aqueous Phosphoric Acid Systems

Complex chemical systems that exhibit varied and matrix-dependent speciation are notoriously difficult to monitor and characterize on-line and in real-time. Optical spectroscopy is an ideal tool for in situ characterization of chemical species that can enable quantification as well as species identification. Chemometric modeling, a multivariate method, has been successfully paired with optical spectroscopy to enable measurement of analyte concentrations even in complex solutions where univariate methods such as Beer’s law analysis fail. Here, Raman spectroscopy is used to quantify the concentration of phosphoric acid and its three deprotonated forms during a titration. In this system, univariate approaches would be difficult to apply due to multiple species being present simultaneously within the solution as pH is varied. Locally Weighted Regression (LWR) modeling was used to determine phosphate concentration from spectral signature. LWR results, in tandem with Multivariate Curve Resolution modeling, provide direct measurement of the concentration of each phosphate species using only the Raman signal. Furthermore, results are presented within the context of fundamental solution chemistry, including Pitzer equations to compensate for activity coefficients and non-idealities associated with high ionic strength systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Efficient and Selective Chemical Transformations in Highly Charged and Confined Nanodroplets

The acceleration of chemical reaction rates and the increased product selectivity in microdroplets compared to that in bulk solutions has become a topic of increasing interest that has been extensively characterized by electrospray ionization mass spectrometry (ESI-MS). However, the sources of this acceleration and the detailed relationships between droplet properties and resulting reaction rate acceleration are still under debate. Moreover, droplet properties are governed by multiple interrelated experimental parameters, i.e., electrospray voltage, solution flow rate, etc., which makes it difficult and time-consuming to explore this diverse parameter space using traditional manual experimental or computational approaches. In this work, we developed an automated experimental platform integrating reactions in controlled charged microdroplet environments with ESI-MS characterization and sequential hybrid Bayesian modeling, as well as an optimal experimental design framework, to achieve multidimensional parameter optimization for higher reaction turnover rates, based on a model reaction of tetraethylenepentamine (TEPA) with carbon dioxide. With the current platform, we have achieved automated scans with a range of electrospray voltages and solution flow rates, and determined and optimized parameter settings to achieve increased reaction turnovers. We have also linked this platform to the underlying properties of droplets via a hybrid model incorporating physics, high-level theoretical calculations, and machine learning (ML) approaches. The autonomous platform is broadly applicable to a range of chemical reactions relevant to DOE’s mission in chemical separations, catalysis, and materials synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

COVID-19, An Exercise in Data Governance at Sandia National Laboratories

In April of 2020, Sandia National Laboratories had an urgent need to identify and manage the data that could be used to create mobile applications, models, reports, and visualizations to assist management in safely bringing the workforce onsite during the COVID-19 pandemic. Multiple divisions volunteered to design and build software solutions; meanwhile, requests for new data sources, including duplicate requests, were inundating Information Technology (IT) and data owners. The Enterprise Data Governance Team was assigned to resolve obtaining and accessing new sources of data in an accelerated timeframe. Through successful collaboration with multiple stakeholders and domain owners across Sandia, the Enterprise Data Governance Team rapidly developed a centralized data strategy and solution for use in safeguarding the Sandia workforce during the COVID-19 pandemic. This foundation enabled teams to successfully develop solutions, including reports for executives and management as well as the data for modeling and scientific analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

The Birth of Minerals: From Single Step to Multiple Step Mechanisms

Mineral formation from ions in aqueous solutions begins with complex initial stages, where amorphous and liquid-like precursors play pivotal roles before crystalline growth occurs. Both classical and non-classical nucleation and growth theories, introduced in previous chapters, offer explanations, each with their own strengths and limitations, for the complex intermediate phases observed in experimental research. Analytical techniques play a critical role in detecting and characterizing precursor phases, offering valuable insights into nucleation and growth mechanisms across various temporal and spatial scales. In conclusion, molecular dynamics and modelling provide in-depth perspectives on these phases, allowing for a closer examination of their nucleation and growth mechanisms at the molecular level, and revealing the intricate processes that govern their behaviour.

amorphous↗

A direct transcription-based multiple shooting formulation for dynamic optimization

The growing need for fast and efficient solution techniques for solving dynamic optimization problems is driven by a broad spectrum of applications in scheduling and control. We suggest a novel framework for dynamic optimization that utilizes a multiple shooting “backbone” with discrete rather than continuous subproblems, thereby eliminating need for repeated time-integration. A Lagrangian relaxation (LR)-based decomposition scheme is proposed, which dualizes the state continuity requirements between subproblems and enables parallel solution of the problem. We demonstrate the applicability of our method on two case studies: the Van der Pol oscillator and a batch reactor.

42 ENGINEERING↗

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗

Tailoring Lifetimes and Properties of Carbodiimide-Fueled Covalently Cross-linked Polymer Networks

The mechanical properties of nonequilibrium polymer hydrogels obtained from the transient cross-linking of polymer chains by a chemical fuel were investigated. Aqueous polymers featuring pendant carboxylic acids were treated with a carbodiimide to give anhydride-cross-linked gels. The anhydrides spontaneously hydrolyze back to the polymer solution, and the cycle can be repeated multiple times. Oscillatory rheology was employed to study the effects of temperature, fuel concentration, chain length, and polymer composition on the storage and loss moduli of the polymeric materials as well as the time taken for the polymers to undergo decross-linking. Regardless of the temperature used, at constant carbodiimide concentration, degelation times are more sensitive to experimental temperature than are the peak storage moduli. Decross-linking times decrease with increasing temperature. As carbodiimide concentration decreases, there is a decrease in moduli and decross-linking times. Within the scope of materials studied, the polymer structure was found to have a relatively small impact on the transient properties of gel networks compared to the fuel concentration and temperature. Furthermore, these findings facilitate the design of tunable on-demand networks and gels.

36 MATERIALS SCIENCE↗