Forecast-based stochastic optimization for a load powered by wave energy
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Dillon, Trent (ORCID:0000000201036449)↗
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Results for “Comparative framework”
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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Economic Incentives for Agrivoltaics Systems with Commodity Crops in the Midwestern United States
Declining costs of photovoltaic (PV) technology and rising market and policy incentives are leading to the growing deployment of PV on cropland in the US Midwest, leading to concerns about the displacement of food and feed crop production. Agrivoltaic (AV) technology enables the dual use of land by co-locating PV energy and crop production, potentially reducing land-use competition with crop production. We develop a benefit-cost analysis framework to compare the net economic returns from AV to those with stand-alone PV and crop production on a representative field and show conditions under which AV can be more profitable for both a solar developer and a farmer. We integrate it with a crop and solar energy model to simulate the performance of various field designs and space and height configurations in AV systems to accommodate soybean production with conventional farm equipment under representative conditions in the US Midwest. We find that an AV system with soybean production is less profitable than PV alone for a solar developer due to the high capital costs of raising panel height, and less profitable for a farmer than leasing land for PV due to its adverse effects of shading on crop yield. We discuss the changes in technology and market prices of solar energy and soybeans that are necessary to make the AV system profitable for solar developers and farmers. We show that AV can worsen rather than mitigate the conflict between food crops and solar energy production in the Midwest.
14 SOLAR ENERGY↗
Load-Shifting Strategies for Cost-Effective Emission Reductions at Wastewater Facilities
Significant hourly variation in the carbon intensity of electricity supplied to wastewater facilities introduces an opportunity to lower emissions by shifting the timing of their energy demand. This shift could be accomplished by storing wastewater, biogas from sludge digestion, or electricity from on-site biogas generation. However, the life cycle emissions and cost implications of these options are not clear. Here, we present a multiobjective optimization framework for comparing cost- and emission-minimizing load-shifting strategies at a California case study facility with a relatively low carbon intensity grid and high spread in peak and off-peak electricity prices. We evaluate cost and emission trade-offs from the optimal flexible operation of both existing infrastructure and optimally sized energy flexibility upgrades. We estimate energy-related emission reductions of up to 9.0% with flexible operation of existing infrastructure and up to 16.8% with optimally sized storage upgrades. Only a fraction of these potential savings are realized under actual industrial energy tariffs and the EPA’s recommended social cost of carbon. Energy flexibility may hold promise as a short-term emission-saving solution for the wastewater sector, but the extent of savings is heavily dependent on the cost of carbon, electricity tariffs, and emission intensity of the regional electricity grid.
climate↗
Structural Evolution of Layered Hybrid Lead Iodide Perovskites in Colloidal Dispersions
Controlling the structure of layered hybrid metal halide perovskites, such as the Ruddlesden-Popper (R-P) phases, is challenging because of their tendency to form mixtures of varying composition. Colloidal growth techniques, such as antisolvent precipitation, forms colloidal dispersions with properties that match bulk layered R-P phases, but controlling the composition of these particles remains challenging. Here, we explore the microstructure of particles of R-P phases of methylammonium lead iodide prepared by antisolvent precipitation from ternary mixtures of alkylammonium cations, where one cation can form perovskite phases (CH 3 NH 3 + ) and the other two promote layered structures as spacers (e.g. C 4 H 9 NH 3 + and C 12 H 25 NH 3 + ). We determine that alkylammonium spacers pack with constant methylene density in the R-P interlayer, and exclude interlayer solvent in dispersed colloids, regardless of length or branching. Using this result, we illustrate how the competition between cations that act as spacers between layers, or as grain- terminating ligands, determines the colloidal microstructure of layered R-P crystallites in solution. Optical measurements reveal that quantum well dimensions can be tuned by engineering the ternary cation composition. Transmission synchrotron wide-angle X-ray scattering and small angle neutron scattering reveal changes in the structure of colloids in solvent and after deposition into thin films. In particular, we find that spacers can alloy between R-P layers if they share common steric arrangements, but tend to segregate into polydisperse R-P phases if they do not mix. In conclusion, this study provides a framework to compare the microstructure of colloidal layered perovskites and suggests clear avenues to control phase and colloidal morphology.
14 SOLAR ENERGY↗
Rate capability and Ragone plots for phase change thermal energy storage
Phase change materials can improve the efficiency of energy systems by time shifting or reducing peak thermal loads. The value of a phase change material is defined by its energy and power density—the total available storage capacity and the speed at which it can be accessed. These are influenced by material properties but cannot be defined with these properties alone. In this work, we show the close link between energy and power density by developing thermal rate capability and Ragone plots, a framework widely used to describe the trade-off between energy and power in electrochemical storage systems (that is, batteries). Our results elucidate how material properties, geometry and operating conditions influence the performance of phase change thermal storage. This research sets a clear framework for comparing thermal storage materials and devices and can be used by researchers and designers to increase clean energy use with storage.
25 ENERGY STORAGE↗
Life history strategies of soil bacterial communities across global terrestrial biomes
The life history strategies of soil microbes determine their metabolic potential and their response to environmental changes. Yet these strategies remain poorly understood. Here we use shotgun metagenomes from terrestrial biomes to characterize overarching covariations of the genomic traits that capture dominant life history strategies in bacterial communities. The emerging patterns show a triangle of life history strategies shaped by two trait dimensions, supporting previous theoretical and isolate-based studies. The first dimension ranges from streamlined genomes with simple metabolisms to larger genomes and expanded metabolic capacities. As metabolic capacities expand, bacterial communities increasingly differentiate along a second dimension that reflects a trade-off between increasing capacities for environmental responsiveness or for nutrient recycling. Random forest analyses show that soil pH, C:N ratio and precipitation patterns together drive the dominant life history strategy of soil bacterial communities and their biogeographic distribution. Finally, our findings provide a trait-based framework to compare life history strategies of soil bacteria.
59 BASIC BIOLOGICAL SCIENCES↗
A compendium of bacterial and archaeal single-cell amplified genomes from oxygen deficient marine waters
Oxygen-deficient marine waters referred to as oxygen minimum zones (OMZs) or anoxic marine zones (AMZs) are common oceanographic features. They host both cosmopolitan and endemic microorganisms adapted to low oxygen conditions. Microbial metabolic interactions within OMZs and AMZs drive coupled biogeochemical cycles resulting in nitrogen loss and climate active trace gas production and consumption. Global warming is causing oxygen-deficient waters to expand and intensify. Therefore, studies focused on microbial communities inhabiting oxygen-deficient regions are necessary to both monitor and model the impacts of climate change on marine ecosystem functions and services. Here we present a compendium of 5,129 single-cell amplified genomes (SAGs) from marine environments encompassing representative OMZ and AMZ geochemical profiles. Of these, 3,570 SAGs have been sequenced to different levels of completion, providing a strain-resolved perspective on the genomic content and potential metabolic interactions within OMZ and AMZ microbiomes. Hierarchical clustering confirmed that samples from similar oxygen concentrations and geographic regions also had analogous taxonomic compositions, providing a coherent framework for comparative community analysis.
59 BASIC BIOLOGICAL SCIENCES↗
A fully Lagrangian, non-parametric bias model for dark matter halos
Here, we present a non-parametric Lagrangian biasing model and fit the ratio of the halo and mass densities at the field level using the mass-weighted halo field in the AbacusSummit simulations at z=0.5. Unlike the perturbative halo bias model that has been widely used in interpreting the observed large-scale structure traced by galaxies, we find a non-negative halo-to-mass ratio that increases monotonically with the linear overdensity δ 1 in the initial Lagrangian space. The bias expansion, however, does not guarantee non-negativity of the halo counts, and may lead to rising halo number counts at negative overdensities. The shape of the halo-to-mass ratio is unlikely to be described by a polynomial function of δ 1 and other quantities. Especially for massive halos with 6×10 12 h -1 M ⊙ , the halo-to-mass ratio starts soaring up at δ 1 >0, substantially different from the predictions of the bias expansion. We show that for the halo masses we consider (M>3×10 11 h -1 M ⊙ ) a non-parametric halo-to-mass ratio as a function of δ 1 and its local derivative ∇^2δ 1 can recover the halo power spectra to sub-percent level accuracy for wavenumbers k=0.01-0.1 h Mpc -1 given a proper smoothing scale to filter the initial density field, even though we do not fit the power spectrum directly. However, there is mild dependence of the recovery of the halo power spectrum on the smoothing scale and other input parameters. At k<0.01 h Mpc -1 and for massive halos with M>6x10 12 h -1 M ⊙ , our non-parametric model leads to a few percent overestimation of the halo power spectrum, indicating the need for larger or multiple smoothing scales. The halo-to-mass ratios obtained qualitatively agree with intuitions from extended Press-Schechter theory. We compare our framework to the bias expansion and discuss possible extensions.
79 ASTRONOMY AND ASTROPHYSICS↗
Spot-based measurement of the brighter-fatter effect on a Roman Space Telescope H4RG detector and comparison with flat-field data
Here, we present the measurement and characterization of the brighter-fatter effect (BFE) on a NASA Roman Space Telescope development Teledyne H4RG-10 near-infrared detector using laboratory measurements with projected point sources. After correcting for other interpixel non-linearity effects such as classical non-linearity and inter-pixel capacitance, we quantify the magnitude of the BFE by calculating the fractional area change per electron of charge contrast. We also introduce a mathematical framework to compare our results with the BFE measured on similar devices using autocorrelations from flat-field images. We find an agreement of 18 ± 5% between the two methods. We identify potential sources of discrepancy and discuss future investigations to characterize and address them.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Philympics 2021: Prophage Predictions Perplex Programs
Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f 1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.
Roach, Michael J.↗
Probing quarkyonic matter in neutron stars with the Bayesian nuclear-physics multimessenger astrophysics framework
The interiors of neutron stars contain matter at the highest densities realized in our Universe. Interestingly, theoretical studies of dense matter, in combination with the existence of two-solar-mass neutron stars, indicate that the speed of sound $c_s$ has to increase to values well above the conformal limit ($c_s^2$ = 1/3) before decreasing again at higher densities. Further, the decrease could be explained by either a strong first-order phase transition or a crossover transition from hadronic to quark matter. The latter scenario leads to a pronounced peak in the speed of sound, reaching values above the conformal limit, naturally explaining the inferred behavior. In this work, we use the nuclear-physics multimessenger astrophysics (NMMA) framework to compare predictions of the quarkyonic matter model with astrophysical observations of neutron stars, with the goal of constraining model parameters. Assuming quarkyonic matter to be realized within neutron stars, we find that there can be a significant amount of quarks inside the cores of neutron stars with masses in the two-solar-mass range, amounting to up to ≈0.13$M$ ⊙ , contributing ≈ 5.9% of the total mass. Furthermore, for the quarkyonic matter model investigated here, the radius of a 1.4$M$ ⊙ neutron star would be $13.44_{–1.54}^{+1.69}(13. 54_{–1.04}^{+1.02})$ km, at 95% credibility, without (with) the inclusion of AT2017gfo.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing
Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.
99 GENERAL AND MISCELLANEOUS↗
Hardware-in-the-Loop Evaluation for Potential High Limit Estimation-Based PV Plant Active Control
This paper validates the efficacy of an artificial intelligence (AI)-based photovoltaic (PV) plant control and optimization approach in enabling PV plants as accountable grid reliability service providers. The validation is performed in a realistic laboratory controller-hardware-in-the-loop environment, leveraging accurate PV plant modeling and standard industrial communication protocols. Through simulations that account for diverse weather conditions and active control scenarios, the results highlight the superior performance of the AI-based solution in comparison to a state-of-the-art reference-control grouping-based approach. Such a finding contributes to mitigating the risk of overcurtailment and uninstructed deviations of active PV plant controls, and offers practical guidance for its field deployment. Furthermore, it establishes a standardized testing framework for comparing various PV active control strategies.
hardware-in-the-loop↗
Endogenizing Probabilistic Resource Adequacy Risks in Deterministic Capacity Expansion Models
In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. The endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.
capacity expansion modeling↗
Structural organization of the spongy mesophyll
Summary Many plant leaves have two layers of photosynthetic tissue: the palisade and spongy mesophyll. Whereas palisade mesophyll consists of tightly packed columnar cells, the structure of spongy mesophyll is not well characterized and often treated as a random assemblage of irregularly shaped cells. Using micro‐computed tomography imaging, topological analysis, and a comparative physiological framework, we examined the structure of the spongy mesophyll in 40 species from 30 genera with laminar leaves and reticulate venation. A spectrum of spongy mesophyll diversity encompassed two dominant phenotypes: first, an ordered, honeycomblike tissue structure that emerged from the spatial coordination of multilobed cells, conforming to the physical principles of Euler’s law; and second, a less‐ordered, isotropic network of cells. Phenotypic variation was associated with transitions in cell size, cell packing density, mesophyll surface‐area‐to‐volume ratio, vein density, and maximum photosynthetic rate. These results show that simple principles may govern the organization and scaling of the spongy mesophyll in many plants and demonstrate the presence of structural patterns associated with leaf function. This improved understanding of mesophyll anatomy provides new opportunities for spatially explicit analyses of leaf development, physiology, and biomechanics.
3D↗
The cosmic muon and detector simulation framework of the extreme energy events (EEE) experiment
Abstract This paper describes the simulation framework of the extreme energy events (EEE) experiment. EEE is a network of cosmic muon trackers, each made of three multi-gap resistive plate chambers (MRPC), able to precisely measure the absolute muon crossing time and the muon integrated angular flux at the ground level. The response of a single MRPC and the combination of three chambers have been implemented in a GEANT4-based framework (GEMC) to study the telescope response. The detector geometry, as well as details about the surrounding materials and the location of the telescopes have been included in the simulations in order to realistically reproduce the experimental set-up of each telescope. A model based on the latest parametrization of the cosmic muon flux has been used to generate single muon events. After validating the framework by comparing simulations to selected EEE telescope data, it has been used to determine detector parameters not accessible by analysing experimental data only, such as detection efficiency, angular and spatial resolution.
Abbrescia, M.↗
Groundwater and Surface Water Flow (GSFLOW) model files to explore bedrock circulation depth and porosity in Copper Creek, Colorado
This data package contains integrated hydrological model input and output files for Copper Creek, Colorado (24 km2), a tributary of the East River located in the headwaters of the Upper Colorado River Basin. The model code is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution and a daily timestep. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model contains 12 model layers and extends 400 m below land surface. The original Copper Creek model was modified to contain geologic layers representing saprolite, shallow bedrock, and deep bedrock. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. For the shallow case, median flow depths occur in the shallow saprolite at depths <8 m, while the deep case promotes a median groundwater flow depth of 100 m. With this modeling framework we compare streamflow response to a plausible worst-case drought lasting up to five years. Streamflow metrics of analysis include average streamflow, fraction of stream network that is dry, no-flow duration, average groundwater flow to streams and time to recovery following the drought. Results and implications are presented in a paper submitted to Geophysical Research Letters titled, "The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought" by Rosemary WH. Carroll, Andrew H. Manning and Kenneth H Williams. A Readme.txt file provides instructions on how to download all model files and execute each model scenario. In addition to the GSFLOW output/prms/copper_drought.csv file containing daily basin water stores and fluxes (refer to GSFLOW manual) and the output/prms/copper_drought_statvar.dat file with output defined in the gsflow3.control file (refer to GSFLOW Manual), output files also include spatially distributed daily values of total evapotranspiration, canopy evaporation, precipitation, snowfall, infiltration, snow water equivalent, potential evapotranspiration, recharge, sublimation, soil moisture, contributing interflow, water table elevations, changes in groundwater storage, groundwater evapotranspiration, interbasin groundwater flow (limited to the alluvium below the stream outlet), and surface-groundwater exchanges within the river system.
54 ENVIRONMENTAL SCIENCES↗
Study of Hydrogen Fuel Cell Technology for Freight Rail Propulsion and Review of Relevant Industry Standards.
Alternatives to conventional diesel electric propulsion are currently of interest to rail operators. In the U.S., smaller railroads have implemented natural gas and other railroads are exploring hydrogen technology as a cleaner alternative to diesel. Diesel, battery, hydrogen fuel cell, or track electrification all have trade-offs for operations, economics, safety, and public acceptability. A framework to compare different technologies for specific applications is useful to optimize the desired results. Standards from the Association of American Railroads (AAR) and other industry best practices were reviewed for applicability with hydrogen fuel cell technology. Some technical gaps relate to the physical properties of hydrogen, such as embrittlement of metals, invisible flames, and low liquid temperatures. A reassessment of material selection, leak/flame detection, and thermal insulation methods is required. Hydrogen is less dense and diffuses more easily than natural gas, and liquid hydrogen is colder than liquefied natural gas. Different densities between natural gas and hydrogen require modifications to tank designs and flow rates. Leaked hydrogen will rise rather than pool on the ground like diesel, requiring a modification to the location of hydrogen tanks on rolling stock. Finally, the vibration and shock experienced in the rail environment is higher than light-duty vehicles and stationary applications for which current fuel cell technology has been developed, requiring a modification in tank design requirements and testing.
08 HYDROGEN↗