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A series of quantization identities are established for static vortex solutions governed by the Born–Infeld type actions. These identities are of a universal nature which are indifferent to the details of the models and provide refined descriptions of divergent energetic quantities.
The carbonyl sulfide budget in the atmosphere is examined, and the effects of stratospheric sulfate aerosol particles, formed in part from atmospheric carbonyl sulfate, on global climate are considered. From tropospheric measurements of carbon disulfide and the rate constant for the conversion of carbon disulfide to carbonyl sulfide, it is estimated that five Tg of carbonyl sulfide/year could be generated from carbon disulfide in the atmosphere. Direct sources of OCS include the refining and combustion of fossil fuels (1 Tg/year), natural and agricultural fires (0.2 to 0.3 Tg/year), and soils (0.5 Tg/year), yielding a total influx of from 1 to 10 Tg/year, up to 50% of which may be anthropogenic. Considerations of carbonyl sulfide sinks and concentrations indicate an atmospheric lifetime of one year, with OCS the major atmospheric sulfur compound. It is estimated that a ten-fold increase in atmospheric carbonyl sulfide would cause an optical depth perturbation comparable to that of a modest volcanic eruption, leading to an average global surface temperature decrease of 0.1 K, in addition to a possible greenhouse effect.
ABSTRACT Temperature has a primary influence on phytoplankton physiology and ecology. We grew 12 strains of Gephyrocapsa huxleyi isolated from different‐temperature regions for ~45 generations (2 months) and characterised acclimated thermal response curves across a temperature range. Even with similar temperature optima and overlapping cell size, strain growth rates varied between 0.45 and 1 day −1 . Thermal niche widths varied from 16.7°C to 24.8°C, suggesting that strains use distinct thermal response mechanisms. We investigated the implications of this thermal intraspecific diversity using an ocean ecosystem simulation resolving phytoplankton thermal phenotypes. Model analogues of thermal ‘generalists’ and ‘specialists’ resulted in a distinctive global biogeography of thermal niche widths with a nonlinear latitudinal pattern. We leveraged model output to predict ranges of the 12 lab‐reared strains and demonstrated how this approach could broadly refine geographic range predictions. Our combination of observations and modelled biogeography highlights the capacity of diverse groups to survive temperature shifts.
Given here is a panel report on the goals and objectives, requirements and recommendations for the investigation of plate motion and deformation. The goals are to refine our knowledge of plate motions, study regional and local deformation, and contribute to the solution of important societal problems. The requirements include basic space-positioning measurements, the use of global and regional data sets obtained with space-based techniques, topographic and geoid data to help characterize the internal processes that shape the planet, gravity data to study the density structure at depth and help determine the driving mechanisms for plate tectonics, and satellite images to map lithology, structure and morphology. The most important recommendation of the panel is for the implementation of a world-wide space-geodetic fiducial network to provide a systematic and uniform measure of global strain.
A thermodynamically-based work potential theory for modeling progressive damage and failure in fiber-reinforced laminates is presented. The current, multiple-internal state variable (ISV) formulation, enhanced Schapery theory (EST), utilizes separate ISVs for modeling the effects of damage and failure. Damage is considered to be the effect of any structural changes in a material that manifest as pre-peak non-linearity in the stress versus strain response. Conversely, failure is taken to be the effect of the evolution of any mechanisms that results in post-peak strain softening. It is assumed that matrix microdamage is the dominant damage mechanism in continuous fiber-reinforced polymer matrix laminates, and its evolution is controlled with a single ISV. Three additional ISVs are introduced to account for failure due to mode I transverse cracking, mode II transverse cracking, and mode I axial failure. Typically, failure evolution (i.e., post-peak strain softening) results in pathologically mesh dependent solutions within a finite element method (FEM) setting. Therefore, consistent character element lengths are introduced into the formulation of the evolution of the three failure ISVs. Using the stationarity of the total work potential with respect to each ISV, a set of thermodynamically consistent evolution equations for the ISVs is derived. The theory is implemented into commercial FEM software. Objectivity of total energy dissipated during the failure process, with regards to refinements in the FEM mesh, is demonstrated. The model is also verified against experimental results from two laminated, T800/3900-2 panels containing a central notch and different fiber-orientation stacking sequences. Global load versus displacement, global load versus local strain gage data, and macroscopic failure paths obtained from the models are compared to the experiments.
A thermodynamically-based work potential theory for modeling progressive damage and failure in fiber-reinforced laminates is presented. The current, multiple-internal state variable (ISV) formulation, enhanced Schapery theory (EST), utilizes separate ISVs for modeling the effects of damage and failure. Damage is considered to be the effect of any structural changes in a material that manifest as pre-peak non-linearity in the stress versus strain response. Conversely, failure is taken to be the effect of the evolution of any mechanisms that results in post-peak strain softening. It is assumed that matrix microdamage is the dominant damage mechanism in continuous fiber-reinforced polymer matrix laminates, and its evolution is controlled with a single ISV. Three additional ISVs are introduced to account for failure due to mode I transverse cracking, mode II transverse cracking, and mode I axial failure. Typically, failure evolution (i.e., post-peak strain softening) results in pathologically mesh dependent solutions within a finite element method (FEM) setting. Therefore, consistent character element lengths are introduced into the formulation of the evolution of the three failure ISVs. Using the stationarity of the total work potential with respect to each ISV, a set of thermodynamically consistent evolution equations for the ISVs is derived. The theory is implemented into commercial FEM software. Objectivity of total energy dissipated during the failure process, with regards to refinements in the FEM mesh, is demonstrated. The model is also verified against experimental results from two laminated, T800/3900-2 panels containing a central notch and different fiber-orientation stacking sequences. Global load versus displacement, global load versus local strain gage data, and macroscopic failure paths obtained from the models are compared to the experiments.
The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC).
The current efforts to assess and improve the Reynolds-averaged Navier-Stokes (RANS)-coupled transition models in the NASA FUN3D and OVERFLOW codes are summarized in this study. The first AIAA Transition Modeling Workshop and the NATO AVT-313 Transition Workshop both emphasized the need for code verification for transport equations based transition models as a top priority. We discuss the methods used for the model verification, the resulting grid families, the flow solutions, and other supporting information collected with at least two established NASA flow solvers, namely, FUN3D and OVERFLOW. These results, which will be uploaded onto the NASA Turbulence Modeling Resource, should assist other members of the computational fluid dynamics (CFD) community in verifying their own implementations of various transition models, such as the Langtry-Menter (LM2009) model, the one-equation γ model, and Coder’s amplification factor transport (AFT) model. Grid convergence is assessed using both global and local flow metrics of interest such as lift and drag as well as local skin-friction coefficients. We also explore the anisotropic unstructured metric-based adaptive mesh refinement library known as refine with the NASA FUN3D solver to determine if this capability can achieve the same accuracy as handcrafted structured grids with a significantly smaller node count and to learn the characteristics of the resulting grid distribution, especially in the vicinity of the transition zone.
The current efforts to assess and improve the Reynolds-averaged Navier-Stokes (RANS)-coupled transition models in the NASA FUN3D and OVERFLOW codes are summarized in this study. The first AIAA Transition Modeling Workshop and the NATO AVT-313 Transition Workshop both emphasized the need for code verification for transport equations based transition models as a top priority. We discuss the methods used for the model verification, the resulting grid families, the flow solutions, and other supporting information collected with at least two established NASA flow solvers, namely, FUN3D and OVERFLOW. These results, which will be uploaded onto the NASA Turbulence Modeling Resource, should assist other members of the computational fluid dynamics (CFD) community in verifying their own implementations of various transition models, such as the Langtry-Menter (LM2009) model, the one-equation γ model, and Coder’s amplification factor transport (AFT) model. Grid convergence is assessed using both global and local flow metrics of interest such as lift and drag as well as local skin-friction coefficients. We also explore the anisotropic unstructured metric-based adaptive mesh refinement library known as refine with the NASA FUN3D solver to determine if this capability can achieve the same accuracy as handcrafted structured grids with a significantly smaller node count and to learn the characteristics of the resulting grid distribution, especially in the vicinity of the transition zone.
The NASA Soil Moisture Active Passive (SMAP) mission has been successfully operated for almost three years. The SMAP freeze/thaw algorithm is based on a seasonal threshold approach. It’s important to have a stable and self-consistent freeze and thaw reference that can be applied for multi-year dataset. The three-year long radiometer datasets allow us to reassess the criteria of reference setup and evaluate its stability. In this paper, we first refined the freeze reference requirements and compare three different methods of setting up the thaw reference to minimize the false flags. The original freeze/thaw products is in the polar grid and only cover the region north of 45° N latitude. The limitation is due to lack of enough freezing days in the lower latitude, where the freezing reference cannot be generated. To extend the freeze/thaw product to global region, we combine the single channel algorithm in the lower latitude and southern atmosphere. The global results have been validated through WMO air temperature.
Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. Furthermore, this opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass toward a broader consideration of how to reduce carbon dioxide to fuels and chemical products.
We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.
Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. This opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass towards a broader consideration of how to reduce carbon dioxide to fuels and chemical products.
Here, we present a new data set of annual historical (1750–2014) anthropogenic chemically reactive gases (CO, CH 4 , NH 3 , NO x , SO 2 , NMVOCs), carbonaceous aerosols (black carbon – BC, and organic carbon – OC), and CO 2 developed with the Community Emissions Data System (CEDS). We improve upon existing inventories with a more consistent and reproducible methodology applied to all emission species, updated emission factors, and recent estimates through 2014. The data system relies on existing energy consumption data sets and regional and country-specific inventories to produce trends over recent decades. All emission species are consistently estimated using the same activity data over all time periods. Emissions are provided on an annual basis at the level of country and sector and gridded with monthly seasonality. These estimates are comparable to, but generally slightly higher than, existing global inventories. Emissions over the most recent years are more uncertain, particularly in low- and middle-income regions where country-specific emission inventories are less available. Future work will involve refining and updating these emission estimates, estimating emissions' uncertainty, and publication of the system as open-source software.
We present a survey of physical and computational techniques that have the potential to con- tribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allow more complete representations of climate features at the global scale. At the same time, part- nerships with computer science teams have focused on taking advantage of evolving computer architectures, such as many-core processors and GPUs, so that these approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.
NREL Marine Energy One-Pager: A resource designed for use during NREL campus visits - especially with high-profile guests - as well as at events in Washington, D.C., when engaging with the new administration, and for displays at WPTO or lab booths. The U.S. holds vast untapped marine energy resources - wave, tidal, river, and ocean currents - that can strengthen grid resilience, support coastal communities, and advance energy independence. NREL leads innovation in this space through advanced modeling tools, patented technologies, and world-class testing facilities. NREL's simulation platforms, like OpenFAST and SAM, help reduce development time and risk. Patented devices such as PKelp and FlexWEC demonstrate flexible, resilient approaches to energy capture. At Flatirons Campus, NREL offers motion simulation, structural testing, wave tanks, and megawatt-scale microgrid emulation via the ARIES platform. These capabilities help developers refine and validate devices before in-water trials. With upcoming open-water testing at PacWave and strong partnerships, NREL is accelerating the path to commercialization - positioning the U.S. as a global leader in marine energy innovation.
Algorithm synthesis transforms a formal specification into an efficient algorithm to solve a problem. Algorithm synthesis in Specware combines the formal specification of a problem with a high-level algorithm strategy. To derive an efficient algorithm, a developer must define operators that refine the algorithm by combining the generic operators in the algorithm with the details of the problem specification. This derivation requires skill and a deep understanding of the problem and the algorithmic strategy. In this paper we introduce two tactics to ease this process. The tactics serve a similar purpose to tactics used for determining indefinite integrals in calculus, that is suggesting possible ways to attack the problem.
The global effort to curb carbon emissions is accelerating demand for clean energy technologies and the materials they rely on. Demand for these materials will only continue to grow, especially as some nations aim to achieve net zero emissions by 2050. While some major materials like steel, copper, and aluminum are already powering the fossil fuel economy, others are more minor materials with potential supply risks. These risks could jeopardize the ability to reduce greenhouse gas emissions within the desirable timeframe to avoid significant climate change. In some cases, it may be necessary to take action to improve the resilience of material supply chains and mitigate supply risks. Understanding the importance of individual materials to clean energy and the supply risks associated with them is necessary to identify which materials may serve as potential roadblocks to a clean energy future. The U.S. Department of Energy (DOE) issued a series of 13 supply chain deep dive assessment reports on various energy technologies in 2022 in response to President Biden’s Executive Order on America’s Supply Chains (E.O. 14017). These reports emphasized that supply chain bottlenecks can occur at any stage of the value chain from mining and refining to component and even sub-system manufacturing. The bottlenecks are a combination of factors such as material availability, equipment availability, work force availability and quality, logistics, regulatory framework, and market conditions. These bottlenecks were worsened during the global Covid-19 pandemic. Its lingering impacts have hindered capacity expansion for material supply chains and prevented product lead-time recovery. One approach to reduce supply chain risks for the United States is to have a strong domestic manufacturing sector with a diverse set of producers. Boosting responsible domestic production would require leveraging the latest science not only in material extraction but also in developing substitutes, recycling, reuse, and remanufacturing. This report is an updated analysis of previous Critical Materials Strategy (CMS) reports published by the DOE in 2010, 2011, and 2019 based on national and global priorities, technology advancement, and technology adoption trends. Like the CMS reports, this analysis presents the results of a formal material criticality assessment to identify which materials are critical to the continued deployment of clean energy technologies globally. The analysis in this report leveraged the DOE supply chain deep dive assessments to develop the initial list of materials to evaluate. This DOE Critical Materials Assessment (CMA) is conducted independently of criticality assessments performed by other U.S. government agencies, such as that conducted by the U.S. Geological Survey (USGS). This analysis complements the USGS critical minerals determination in three aspects. First, the DOE assessment is performed from a global perspective, while the USGS analysis focusses on the importance of minerals to the U.S. economy. Second, this report focuses on the importance of materials to clean energy technologies, rather than to the economy in general. Lastly, this study is forward looking to 2035 based on clean energy deployment scenarios, whereas the USGS assessment is retrospective. Materials evaluated in this report that do not appear in the USGS Critical Minerals List include copper, uranium, electrical steel, and SiC. A draft version of this report received ~80 public comments related to supporting data and methodological improvement. Those comments have been incorporated as much as possible where appropriate. Highlights of findings from this 2023 CMA include: Rare earth materials (neodymium, praseodymium, dysprosium, and terbium) used in magnets in electric vehicle (EV) motors and wind turbine generators continue to be critical. While dysprosium (Dy) and terbium (Tb) are both heavy rare earth elements that serve the same function in magnets, the criticality of Tb is slightly lower than that for Dy in the short term due to the widespread use of Dy in high-grade magnets and Tb’s present role as a substitute. Similarly, praseodymium (Pr) is critical in the medium term but only near critical in the short term because it is more substitutable in magnets than neodymium (Nd); Materials used in batteries for EVs and stationary storage are now considered to be critical. While cobalt (Co) was found to be critical in this and previous reports, lithium (Li) becomes critical in the medium term due to its broader use in various battery chemistries and the rampant growth of the EV industry. Natural graphite is a new addition in this assessment and is also found to be critical; Platinum group metals used in hydrogen electrolyzers, such as platinum (Pr) and iridium (Ir), are critical due to an increased focus on hydrogen technologies to achieve net zero carbon emissions, while those used in catalytic converters, such as rhodium (Rh) and palladium (Pd), were screened out due to the decreased importance of catalytic converters in the medium term; Gallium (Ga) continues to be critical due to its use in light-emitting diodes (LEDs). In addition, the use of Ga has increased in magnet manufacturing and in semiconductor in forms such as gallium arsenide (GaAs) or gallium nitride (GaN); Major materials like Aluminum (Al), copper (Cu), nickel (Ni), and silicon (Si) move from noncritical in the short term to near critical in the medium term due to their importance in electrification; Electrical steel is near critical due to its use in transformers for the grid and electric motors in EVs.