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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Utility-Scale Solar, 2021 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2021 Edition” provides an overview of key trends in the U.S. market, with a focus on 2020. Highlights of this year’s update include: A record of nearly 9.6 GWAC of new utility-scale PV capacity came online in 2020, bringing cumulative installed capacity to more than 38.7 GWAC across 43 states. 89% of all new utility-scale PV capacity added in 2020 uses single-axis tracking. Median installed project costs declined to $\$$1.4/WAC (or $\$$1.1/WDC) in 2020. Project-level capacity factors vary widely, from 9% to 36% (on an AC basis), with a sample median of 24%. The report explores drivers of this variation. Utility-scale PV’s LCOE fell to $\$$34/MWh in 2020 ($\$$28/MWh if factoring in the federal investment tax credit, or ITC). PPA prices have largely followed the decline in solar’s LCOE over time, but have stagnated more recently. Prices from a sample of recent contracts average just above $\$$20/MWh (levelized). In 2020, solar’s average market value (defined in the report to include only energy and capacity value) exceeded average wholesale prices in 12 of the 17 balancing authorities analyzed (including 4 of the 7 independent system operators across the United States). Adding battery storage is one way to increase the value of solar. Our public data file tracks metadata for more than 150 PV+battery hybrid projects that are already online or that have secured offtake arrangements. At the end of 2020, there were at least 460 GW of utility-scale solar power capacity within the interconnection queues across the nation, 160 GW of which include batteries. For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗
DOE OSTI2021

Tuning successive linear programming to solve AC optimal power flow problem for large networks

Successive linear programming (SLP) is a practical approach for solving large-scale nonlinear optimization problems. Alternating current optimal power flow (ACOPF) is no exception, particularly the large size of real-world networks. However, in order to achieve tractability, it is essential to tune the SLP algorithm presented in the literature. This paper presents a modified SLP algorithm to solve the ACOPF problem, specified by the U.S. Department of Energy’s (DOE) Grid Optimization (GO) Competition Challenge 1, within strict time limits. The algorithm first finds a near-optimal solution for the relaxed problem (i.e., Stage 1). Then, it finds a feasible solution in the proximity of the near-optimal solution (i.e., Stage 2 and Stage 3). The numerical experiments on test cases ranging from 500-bus to 30,000-bus systems show that the algorithm is tractable. Here the results show that our proposed algorithm is tractable and can solve more than 80% of test cases faster than the well-known Interior Point Method while significantly reduce the number of iterations required to solve ACOPF. The number of iterations is considered an important factor in the examination of tractability which can drastically reduce the computational time required within each iteration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Powering Negative-Emissions Technologies with Marine Renewable Energy

The majority of modeled emissions scenarios that meet the climate change goals outlined in the 2015 Paris Agreement require not only significant reductions in anthropogenic carbon dioxide emissions, but also removal of carbon dioxide from the atmosphere ("negative emissions"). To date, research and development efforts have largely focused on land-based negative emissions technologies, such as bioenergy with carbon capture and storage. However, ocean-based technologies also have significant potential, and avoid competition for land use and fresh water with agriculture and human development. Marine carbon dioxide removal approaches include biological solutions that seek to increase carbon fixation through photosynthesis and chemical solutions that aim to increase the absorption of carbon dioxide in seawater. Most of these approaches have significant power requirements, especially when implemented at the scale necessary to meet negative-emissions goals. Marine renewable energy is uniquely poised to power marine carbon dioxide removal given its ability to provide carbon-free power at sea. In this work, the power requirements of marine carbon dioxide removal systems and the potential of marine renewable energy to power them are investigated, culminating in recommendations for future research and development to enable such developments.

marine renewable energy, carbon dioxide, climate c↗
DOE OSTI2020

A zeroth-order active-space frozen-orbital embedding scheme for multireference calculations

Multireference computations of large-scale chemical systems are typically limited by the computational cost of quantum chemistry methods. In this work, we develop a zeroth-order active space embedding theory [ASET(0)], a simple and automatic approach for embedding any multireference dynamical correlation method based on a frozen-orbital treatment of the environment. ASET(0) is combined with the second-order multireference driven similarity renormalization group and tested on several benchmark problems, including the excitation energy of 1-octene and bond-breaking in ethane and pentyldiazene. Finally, we apply ASET(0) to study the singlet–triplet gap of p-benzyne and 9,10-anthracyne diradicals adsorbed on a NaCl surface. Our results show that despite its simplicity, ASET(0) is a powerful and sufficiently accurate embedding scheme applicable when the coupling between the fragment and the environment is in the weak to medium regime.

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

Hybrid gausslet/Gaussian basis sets

We introduce hybrid gausslet/Gaussian basis sets, where a standard Gaussian basis is added to a gausslet basis in order to increase accuracy near the nuclei while keeping the spacing of the grid of gausslets relatively large. The Gaussians are orthogonalized to the gausslets, which are already orthonormal, and approximations are introduced to maintain the diagonal property of the two electron part of the Hamiltonian so that it continues to scale as the second power of the number of basis functions rather than the fourth. We introduce several corrections to the Hamiltonian designed to enforce certain exact properties, such as the values of certain two-electron integrals. We also introduce a simple universal energy correction that compensates for the incompleteness of the basis stemming from the electron–electron cusps based on the measured double occupancy of each basis function. We perform a number of Hartree Fock and full configuration interaction (full-CI) test calculations on two electron systems and Hartree Fock on a ten-atom hydrogen chain to benchmark these techniques. The inclusion of the cusp correction allows us to obtain complete basis set full-CI results for the two electron cases at the level of several micro-Hartrees, and we see similar apparent accuracy for Hartree Fock on the ten-atom hydrogen chain.

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

Probabilistic Neural Computing with Stochastic Devices

Abstract The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.

Misra, Shashank↗
DOE OSTI2024

Development and Experimental Validation of a Heat Transfer Model for Spilled Molten Salt Pools

A spill of radionuclide-bearing molten salt is one of the major postulated events that needs to be analyzed for liquid fluorine salt-cooled high-temperature reactor (FHR) or molten salt reactor licensing purposes. In this postulated event, radioactive source term materials (RSTMs) in the molten salt are discharged from the reactor vessel to the reactor building. The release of RSTMs from the spilled salt pool to the gas space in the reactor building is expected to be controlled by the cooling behavior of the spilled salt, including the growth and shrinkage of the solid crust on the surface of the spilled salt pool. This paper presents a simulation model for spilled salt pool heat transfer and validation efforts. The validation data come from two molten salt spill tests that were performed recently: the PELE2 test by the Rapid Experimental Laboratory of Kairos Power LLC (KP) and the Argonne salt cooling test conducted by Argonne National Laboratory. The former was a large-scale test involving kilograms of molten spilled FLiNaK salt, and the latter was a relatively smaller-scale test targeting various processes associated with a salt spill event. Both tests generated valuable data sets that can be used to assess salt cooling and validate evaluation models. This paper provides a new one-dimensional model that can simulate the cooling process of a spilled salt pool as well as the thermal responses of heat structures, such as the stainless steel liner and the concrete below the salt. The model has been implemented as part of KP-SAM code, which is a branch of the systems code SAM specific to KP FHR. In conclusion, the simulation results of the model are compared with the data of the PELE2 and Argonne tests, and reasonable agreements are observed between the model and test data.

heat transfer model↗
DOE OSTI2023

Fractal dimensions of jammed packings with power-law particle size distributions in two and three dimensions

Static structure factors are computed for large-scale, mechanically stable, jammed packings of frictionless spheres (three dimensions) and disks (two dimensions) with broad, power-law size dispersity characterized by the exponent -β. Here, the static structure factor exhibits diverging power-law behavior for small wave numbers, allowing us to identify a structural fractal dimension d f . In three dimensions, d f ≈ 2.0 for 2.5≤ β ≤ 3.8, such that each of the structure factors can be collapsed onto a universal curve. In two dimensions, we instead find 1.0≲ d f ≲1.34 for 2.1≤ β ≤ 2.9. Furthermore, we show that the fractal behavior persists when rattler particles are removed, indicating that the long-wavelength structural properties of the packings are controlled by the large particle backbone conferring mechanical rigidity to the system. A numerical scheme for computing structure factors for triclinic unit cells is presented and employed to analyze the jammed packings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Driving Zn-MnO2 grid-scale batteries: A roadmap to cost-effective energy storage

Highlights Zn-MnO 2 batteries promise safe, reliable energy storage, and this roadmap outlines a combination of manufacturing strategies and technical innovations that could make this goal achievable. Approaches such as improved efficiency of manufacturing and increasing active material utilization will be important to getting costs as low as $100/kWh, but key materials innovations that facilitate the full 2-electron capacity utilization of MnO 2 , the use of high energy density 3D electrodes, and the promise of a separator-free battery with greater than 2V potential offer a route to batteries at $50/kWh or less. Abstract Large-scale energy storage is certain to play a significant, enabling role in the evolution of the emerging electrical grid. Battery-based storage, while not a dominant form of storage today, has opportunity to expand its utility through safe, reliable, and cost-effective technologies. Here, secondary Zn–MnO 2 batteries are highlighted as a promising extension of ubiquitous primary alkaline batteries, offering a safe, environmentally friendly chemistry in a scalable and practical energy dense technology. Importantly, there is a very realistic pathway to also making such batteries cost-effective at price points of $50/kWh or lower. By examining manufacturing examples at the Zn–MnO 2 battery manufacturer Urban Electric Power, a roadmap has been created to realize such low-cost systems. By focusing on manufacturing optimization through reduced materials waste, scalable manufacturing, and effective materials selection, costs can be significantly reduced. Ultimately, though, coupling these approaches with emerging research and development advances to enable full capacity active materials utilization and battery voltages greater than 2V are likely needed to drive costs below a target of $50/kWh. Reaching this commercially important goal, especially with a chemistry that is safe, well-known, and reliably effective stands to inject Zn–MnO 2 batteries in the storage landscape at a critical time in energy storage development and deployment. Graphical abstract

Spoerke, Erik D. (ORCID:0000000322862560)↗
DOE OSTI2021

Supporting Co-Design of Extreme-Scale Systems with In Situ Visual Analysis of Event-Driven Simulations (Final Report)

Extreme-scale systems for compute- and data-centric science are pushing the boundaries of system architectures in order to achieve ambitious goals in terms of computing capability, resilience, and power efficiency. Future systems necessitate emerging designs that incorporate new technologies, system organizations, and computer science techniques, and these changes are not yet well understood. Discrete-event-driven simulation of computing system architectures and subsystems has emerged as a productive and cost-effective means to evaluating potential designs, along with capabilities for executing these simulations of extreme-scale systems. However, techniques for analyzing the behavior of these simulations have not kept pace with our ability to perform the simulations, preventing us from extracting the most value from this promising approach. The objective of this project is to support the codesign of extreme-scale system architectures for compute and data-centric science through research and development of novel methods for analysis and visualization of large-scale event-driven simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modernizing the Nuclear Industry and New Ways of Working

Nuclear energy is recognized as the most feasible energy source towards achieving nation-wide net-zero goals (COP 208, MIT). Additionally, the demand for reliable, consistent energy supply is soaring with the construction of energy hungry data centers and AI tools. This means the construction of new nuclear and the continuation of current nuclear are a high national priority. However, this leaves the nuclear industry with a workforce challenge. The rising demand for nuclear power is threatened by an underpopulated workforce. There exist a few contributors to the decreased workforce such as skewed workforce demographics resulting in large-scale workforce retirement. Also, some plants have observed difficulty hiring and retaining skill sets that typically comprise the nuclear power workforce – specifically skilled craftsman. Those who are graduating with desirable skills are seeking employment in sectors that are more modern and culturally more aligned with younger generational values. Lastly, within the nuclear sector, skilled employees will be a competitive commodity as advanced plants come online offering work environments that incorporate modern technologies, skill sets, and opportunities for career advancement. These factors emphasize the need for legacy plants to modernize with a focus on the workplace environment and culture that meets younger generations’ skills, cultural expectations, and desire for advancement opportunities. Nuclear plants are actively engaged in deploying advancements that improve the management of systems, structures, and components as well as process improvements and technologies that can reduce the costs of operation and maintenance. However, the advancements must also be analyzed, reviewed and deployed in a manner that considers the impact on how people within the organization collaborate, communicate, make decisions, and solve problems. Neglecting to consider the cultural impact of modernization risks poor adoption, unrealized opportunities, or insignificant change toward helping attract new employees. The traditional way of working is not necessarily the way new generations want to engage with their employer. For instance, in a survey performed by North American Young Generation in Nuclear the top three reasons for younger nuclear employees to seek other job opportunities was seeking better work-life balance, a lack of advancement opportunities, and work culture and leadership style differences (Smyth et al. 2022). Integrated Operations for Nuclear (ION) is an approach for the nuclear industry to create long-term strategic modernization plans and analyze each advancement for the impact on people and processes and measure how those impacts flow up to support high-level, long-term plant goals and requirements. One principle of ION is to replace the current labor-centric operation style in legacy nuclear plants with data-centric collaborative operation styles. This paper will explain how transitioning operations to leverage centralized skills and responsibility, multi-skilled teams, and collaborative decision making can flatten the hierarchical structure in plants, enable greater individual efficacy while also offering more experiences and advancement opportunities to staff. Adopting the ION way of working shifts the industry mind-set towards more networked, collaborative, and modern way of working that will attract skilled, next-generation workers to operate the legacy nuclear fleet.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A New Family of Proton‐Conducting Electrolytes for Reversible Solid Oxide Cells: BaHf x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ

Abstract Reversible solid oxide cells based on ceramic proton conductors have potential to be the most efficient system for large‐scale energy storage. The performance and long‐term durability of these systems, however, are often limited by the ionic conductivity or stability of the proton‐conducting electrolyte. Here new family of solid oxide electrolytes, BaHf x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ (BHCYYb), which demonstrate a superior ionic conductivity to stability trade‐off than the state‐of‐the‐art proton conductors, BaZr x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ (BZCYYb), at similar Zr/Hf concentrations, as confirmed by thermogravimetric analysis, Raman, and X‐ray diffraction analysis of samples over 500 h of testing are reported. The increase in performance is revealed through thermodynamic arguments and first‐principle calculations. In addition, lab scale full cells are fabricated, demonstrating high peak power densities of 1.1, 1.4, and 1.6 W cm −2 at 600, 650, and 700 °C, respectively. Round‐trip efficiencies for steam electrolysis at 1 A cm −2 are 78%, 72%, and 62% at 700, 650, and 600 °C, respectively. Finally, CO 2 H 2 O electrolysis is carried out for over 700 h with no degradation.

Murphy, Ryan↗
DOE OSTI2022

Unraveling small-scale defects in irradiated ThO 2 using kinetic Monte Carlo simulations

Point defects and their clusters generated through irradiation can have significant impact on the physical and mechanical properties of materials. However, direct experimental visualization of these small-scale defects using high-resolution scanning transmission electron microscopy remains a challenging task. In this study, using thorium dioxide (ThO 2 ) with the fluorite structure as a model system, we demonstrate the use of ab initio basin-hopping simulations in synergy with object kinetic Monte Carlo simulations as a powerful tool for identifying small defect complexes in irradiated materials. In addition to providing quantitative insights into defect evolution in ThO 2 under irradiation, our study reveals an unexpected role of bound anti-Schottky defect clusters in mediating defect transport. Remarkably, despite their poor thermal stability against dissociation at high temperatures, the transient formation of bound anti-Schottky defects under irradiation and their subsequent migration provide the dominant mechanism for the growth of large interstitial loops that have been experimentally observed in ThO 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2020

High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440

Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in Pseudomonas putida KT2440 grown on various carbon sources. The results from the two LC-SRM platforms are highly correlated. In addition, the response curve study of 248 peptides demonstrates that microflow LC-SRM has comparable sensitivity for the majority of detected peptides and better mass spectrometry signal and chromatography stability than nanoflow LC-SRM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Fully variational incremental CASSCF

We report the complete-active-space self-consistent field (CASSCF) method is a canonical electronic structure theory that holds a central place in conceptualizing and practicing first principles simulations. For application to realistic molecules, however, the CASSCF must be approximated to circumvent its exponentially scaling computational costs. Applying the many-body expansion - also known as the method of increments - to CASSCF (iCASSCF) has been shown to produce a polynomially scaling method that retains much of the accuracy of the parent theory and is capable of treating full valence active spaces. Due to an approximation made in the orbital gradient, the orbital parameters of the original iCASSCF formulation could not be variationally optimized, which limited the accuracy of its nuclear gradient. Herein, a variational iCASSCF is introduced and implemented, where all parameters are fully optimized during energy minimization. This method is able to recover electronic correlations from the full valence space in large systems, produce accurate gradients, and optimize stable geometries as well as transition states. Demonstrations on challenging test cases, such as the oxoMn(salen)Cl complex with 84 electrons in 84 orbitals and the automerization of cyclobutadiene, show that the fully variational iCASSCF is a powerful tool for describing challenging molecular chemistries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Enhancing Time Synchronization in Smart Grid With White Rabbit: Theory, Architecture, and Challenges

The smart grid aims to provide economically efficient and sustainable power to consumers with high quality and security. However, the increasing integration of distributed renewable energy sources presents challenges for smart grid protection and control systems. Here, to enhance smart grid operations, this article introduces White Rabbit (WR) as a more precise and accurate time synchronization technique. To explore White Rabbit’s potential applications in the smart grid, this study first explains existing time synchronization techniques and their limitations, followed by an analysis of the role and importance of time synchronization in smart grids. A comprehensive survey is then conducted, covering the theories, principles, implementations, performances, and existing application cases of White Rabbit. The findings suggest that White Rabbit is a promising technique for smart grid deployment. However, several challenges remain on the path to large-scale implementation. These challenges are analyzed in detail, highlighting key areas for future research.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗