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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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The nine papers in this special section on power system planning and operation towards a low-cost economy cover the following topics: power system planning models; power system operation methods and market behavior analysis; and risk assessment and emission management.
BACKGROUND: Enzymatic plant biomass degradation by fungi is a highly complex process and one of the leading challenges in developing a biobased economy. Some industrial fungi (e.g. Aspergillus niger) have a long history of use with respect to plant biomass degradation and for that reason have become 'model' species for this topic. A. niger is a major industrial enzyme producer that has a broad ability to degrade plant based polysaccharides. A. niger wild-type, the (hemi-)cellulolytic regulator (xlnR) and xylulokinase (xkiA1) mutant strains were grown on a monocot (corn stover, CS) and dicot (soybean hulls, SBH) substrate. The xkiA1 mutant is unable to utilize the pentoses D-xylose and L-arabinose and the polysaccharide xylan, and was previously shown to accumulate inducers for the (hemi-)cellulolytic transcriptional activator XlnR and the arabinanolytic transcriptional activator AraR in the presence of pentoses, resulting in overexpression of their target genes. The xlnR mutant has reduced growth on xylan and down-regulation of its target genes. The mutants therefore have a similar phenotype on xylan, but an opposite transcriptional effect. D-xylose and L-arabinose are the most abundant monosaccharides after D-glucose in nearly all plant-derived biomass materials. In this study we evaluated the effect of the xlnR and xkiA1 mutation during growth on two pentose-rich substrates by transcriptome analysis. RESULTS: Particular attention was given to CAZymes, metabolic pathways and transcription factors related to the plant biomass degradation. Genes coding for the main enzymes involved in plant biomass degradation were down-regulated at the beginning of the growth on CS and SBH. However, at a later time point, significant differences were found in the expression profiles of both mutants on CS compared to SBH. CONCLUSION: This study demonstrates the high complexity of the plant biomass degradation process by fungi, by showing that mutant strains with fairly straightforward phenotypes on pure mono- and polysaccharides, have much less clear-cut phenotypes and transcriptomes on crude plant biomass.
The second National Renewable Energy Laboratory (NREL) Wind Energy Systems Engineering Workshop was held in Broomfield, Colorado, from January 29 to February 1, 2013. The event included a day-and-a-half workshop exploring a wide variety of topics related to system modeling and design of wind turbines and plants. Following the workshop, 2 days of tutorials were held at NREL, showcasing software developed at Sandia National Laboratories, the National Aeronautics and Space Administration's Glenn Laboratories, and NREL. This document provides a brief summary of the various workshop activities and includes a review of the content and evaluation results from attendees.
The Alpine Treeline Warming Experiment (ATWE) was established in 2008-2009 to experimentally test how climate change would affect species ranges at and beyond the cold edge limits of forest growth. One key finding was that warming can hinder upslope tree migration by increasing seedling water stress and reducing seedling survival. We found evidence for more conservative traits in seedlings from high elevation tree populations, which may further slow tree expansion into the current alpine. In the alpine, warming may only increase productivity if summer rain increases, as dominant alpine life forms were negatively affected by heating unless water was added. The ATWE experimental design and focus on dynamic vegetation were novel, and the setting at the mountain treeline transition was important to understanding effects of climate variability and change on water resources, carbon cycling and biodiversity. We ended climate manipulations and removed associated infrastructure from the field in 2016 following 8 years of data collection. To date, 5 MS theses and 18 peer-reviewed papers have been published or accepted (including one that yielded the cover image in Global Change Biology, left), and 1 PhD thesis and at least 5 papers are in review or active development. Numerous past seasonal and long-term field staff have gone on to graduate school, and subsequently to research positions as university faculty or agency scientists. PI Kueppers continues collecting data on soil microclimate and seedling survival for long-lived tree species to fill a critical gap in knowledge regarding the seedling-to-sapling transition across species and elevations. Primary project data is housed on the project’s virtual server, organized according to topic. Published data and model code is available either through the project website or via other permanent archives. Our results are influencing understanding and modeling of climate and genetic constraints on dynamic vegetation change across the landscape, particularly in the mountains of the Western U.S.
The Forward Physics Facility (FPF) is a proposal to create a cavern with the space and infrastructure to support a suite of far-forward experiments at the Large Hadron Collider during the High Luminosity era. Located along the beam collision axis and shielded from the interaction point by at least 100 m of concrete and rock, the FPF will house experiments that will detect particles outside the acceptance of the existing large LHC experiments and will observe rare and exotic processes in an extremely low-background environment. In this work, we summarize the current status of plans for the FPF, including recent progress in civil engineering in identifying promising sites for the FPF; the FPF experiments currently envisioned to realize the FPF’s physics potential; and the many Standard Model and new physics topics that will be advanced by the FPF, including searches for long-lived particles, probes of dark matter and dark sectors, high-statistics studies of TeV neutrinos of all three flavors, aspects of perturbative and non-perturbative QCD, and high-energy astroparticle physics.
The following topics are addressed: the model and the method; equation of state of neutron matter, role of three-neutron force; symmetry energy; Λ-hypernuclei; Λ-neutron matter; and neutron star structure. In summary, quantum Monte Carlo methods are useful to study nuclear systems in a coherent framework; the three-neutron force is the bridge between E sym and neutron star structure; and neutron star observations are becoming competitive with experiments. Λ-nucleon data are very limited, but ΛNN is very important. The role of Λ in neutron stars is far from understood; more ΛN data are needed. The author's conclusion: We cannot conclude anything with present models.
This is an overview of recent publications on the prospects of searching for nonminimal Lorentz-violating effects in atomic spectroscopy experiments. The article discusses the differences in the signals for Lorentz violation in the presence of minimal and nonminimal operators and what systems are more sensitive to certain types of Lorentz-violating operators.
The Dagstuhl Seminar 24512 on "Quantum Software Engineering" was held from December 15 to 20, 2024. It brought together 26 participants from industry and academia from 13 different countries, including senior and junior researchers as well as practitioners in the field of Quantum Software Engineering. The aim of the seminar was to advance software engineering methods and tools for the engineering of hybrid quantum systems by promoting personal interaction and open discussion among researchers who are already working in this emerging area of knowledge. The first day of the seminar was devoted to the topic "When software engineering meets quantum mechanics", while the second day focused on "Quantum software engineering and its challenges." During both days, 16 invited presentations were given. The rest of the seminar was organized into three working groups to address the topics "Quantum Software Design, Modelling and Architecturing", "Adaptive Hybrid Quantum Systems", and "Quantum Software Quality Assurance". The seminar was a very fruitful experience for all participants both in terms of scientific outcomes and in terms of the personal relationships that were generated to jointly address future experiences.
The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.
The statistical analysis of networks is a popular research topic with ever widening applications. Exponential random graph models (ERGMs), which specify a model through interpretable, global network features, are common for this purpose. In this study we introduce a new class of models for network analysis, called local structure graph models (LSGMs). In contrast to an ERGM, a LSGM specifies a network model through local features and allows for an interpretable and controllable local dependence structure. In particular, LSGMs are formulated by a set of full conditional distributions for each network edge, e.g., the probability of edge presence/absence, depending on neighborhoods of other edges. Additional model features are introduced to aid in specification and to help alleviate a common issue (occurring also with ERGMs) of model degeneracy. Finally, the proposed models are demonstrated on a network of tornadoes in Arkansas where a LSGM is shown to perform significantly better than a model without local dependence.
Euclid is a European Space Agency medium-class mission selected for launch in 2020 within the cosmic vision 2015–2025 program. The main goal of Euclid is to understand the origin of the accelerated expansion of the universe. Euclid will explore the expansion history of the universe and the evolution of cosmic structures by measuring shapes and red-shifts of galaxies as well as the distribution of clusters of galaxies over a large fraction of the sky. Although the main driver for Euclid is the nature of dark energy, Euclid science covers a vast range of topics, from cosmology to galaxy evolution to planetary research. In this review we focus on cosmology and fundamental physics, with a strong emphasis on science beyond the current standard models. We discuss five broad topics: dark energy and modified gravity, dark matter, initial conditions, basic assumptions and questions of methodology in the data analysis. This review has been planned and carried out within Euclid’s Theory Working Group and is meant to provide a guide to the scientific themes that will underlie the activity of the group during the preparation of the Euclid mission.
The modeling of corrosion to understand and predict corrosion behavior is a topical issue. Here, a 3D phase field model is developed to simulate the pit morphology, primarily focusing on crystallographic pits. A crystallographic function is employed to incorporate different corrosion rates for different crystallographic planes. The model is benchmarked and validated against an analytical solution for a simple case. 3D crystallographic pits in a single crystal are simulated and the effect of substrate orientation on the pit morphology is studied. The crystallographic pit morphology changes significantly with the substrate orientation and these morphologies have a symmetry consistent with the substrate orientation. This first 3D phase field model of crystallographic pits will help in predicting the intricate shapes of pits, thereby, pushing the frontiers of pitting corrosion modeling.
Our society is transitioning fast into the digital age, spurred by development of cheap and new sensing technology, breakthroughs in computing, and development of efficient algorithms for optimization. This transition is also visible in the field of wastewater treatment and is driving new model developments, especially by exploiting the large data sets available with many utilities. Not surprisingly, WRRmod2021 had featured a strong session on ‘data-driven models and digitalization’ focused on this hot topic. At the same time, engineering practice calls for more robust models for performance evaluation and optimization of both conventional facilities and innovative processes. As a result, the WRRmod2021 program also exhibited sessions on modelling of new process units (e.g., aerobic granular sludge), modelling of the nitrogen cycle, and integrated/plant-wide modelling.
This talk will focus on recent and planned modeling improvements to the NREL System Advisor ModelTM (SAM), including PV hourly clipping correction, expanded geographic scope of PVWatts including Ukraine, additional options for hybrids, and enhanced PySAM and GUI interoperability. Additional topics will also include: bifacial modeling features including irradiance on the ground for agrivoltaics, PV uncertainty simulation features, and integrations with other NREL and industry tools.
Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.
Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.
Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision makers. With the recent surge in variable renewable energy (VRE) generators - primarily wind and solar photovoltaics - the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. To assess current best practices, share methods and data, and identify future research needs for VRE representation in capacity expansion models, four capacity expansion modeling teams from the Electric Power Research Institute, the U.S. Energy Information Administration, the U.S. Environmental Protection Agency, and the National Renewable Energy Laboratory conducted two workshops of VRE modeling for national-scale capacity expansion models. The workshops covered a wide range of VRE topics, including transmission and VRE resource data, VRE capacity value, dispatch and operational modeling, distributed generation, and temporal and spatial resolution. The objectives of the workshops were both to better understand these topics and to improve the representation of VRE across the suite of models. Given these goals, each team incorporated model updates and performed additional analyses between the first and second workshops. This report summarizes the analyses and model 'experiments' that were conducted as part of these workshops as well as the various methods for treating VRE among the four modeling teams. The report also reviews the findings and learnings from the two workshops. We emphasize the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making.
Lithium ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack.