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The salt-tolerant capability of the candidate bioenergy crop prairie cordgrass greatly surpasses that of previously characterized prairie grass species and most other plants. To understand the mechanism of inherited salt tolerance, we compared phenotypic and genetic qualities in half-sib families of prairie cordgrass after salt treatment. Each family was treated with a 400 mM NaCl solution or a water control and then measured for various health phenotypes. Phenotypes associated with salt tolerance were shown to be moderately heritable between parent and offspring. RNA-seq analysis revealed differential regulation in unique pathways including metabolism, signaling, photosynthesis, and the circadian rhythm. The studies herein suggest that alternative regulation of the photosynthetic pathway could confer increased salt resistance in halophytes and can be monitored phenotypically or genetically in breeding programs. The improvement of salt-tolerant traits in prairie cordgrass would increase its potential to be grown as a bioenergy crop on lands that are not suitable for the growth of food crops.

Designing future capacity mixes with adequate flexibility requires capturing operating constraints through an embedded unit commitment approximation. Despite significant recent improvements, such simulations still require significant computation times. Here we propose a method, based on clustering units, for approximate unit commitment with dramatic improvements in solution time. This method speeds computation by aggregating similar but non-identical units. This replaces large numbers of binary commitment variables with fewer integers while still capturing individual unit decisions and constraints. We demonstrate the trade-off between accuracy and run-time for different levels of aggregation. A numeric example using an ERCOT-based 205-unit system illustrates that careful aggregation introduces errors of 0.05%-0.9% across several metrics while providing several orders of magnitude faster solution times (400 ×) compared to traditional binary formulations. Further aggregation increases errors slightly ( ~ 2×) with further speedup (2000 ×). We also compare other simplifications that can provide an additional order of magnitude speed-up for some problems.

Abstract More than half the energy to run a data center can be consumed by vapor-compression equipment that cools the center. To reduce consumption and recycle otherwise wasted thermal energy, this paper proposes an alternative cooling architecture that is heat driven and leads to a more efficient data center in terms of power usage effectiveness (PUE). The primary thermal source is waste heat produced by CPUs on each server blade. The main challenge is capturing enough of this high-temperature heat to energize an absorption unit. The goal is to capture a high fraction of dissipated thermal power by using a heat capture scheme with water as the heat transfer fluid. To determine if the CPU temperature range and amount of heat are sufficient for chiller operation, we use server software, validation thermocouples, and chip specifications. We compare these results to required values from a simulator tool specific to our chiller model. One challenge is to simultaneously cool the data center and generate enough exergy to drive the cooling process, regardless of the thermal output of the data center equipment. We can address this by adding phase change latent heat storage to consistently deliver the required heat flow and, if necessary, a solar heat source. Even with zero solar contribution, the results show that the number of CPUs we have is sufficient and our PUE indicates a very efficient data center. Adding solar contribution, the steady-state model proposed leads to a potentially realizable PUE value of less than one.

SrTi0.3Fe0.7−xCoxO3−δoxygen electrodes provide a unique combination of low polarization resistance and stability useful for solid oxide electrochemical cells.

Abstract The objective of this paper is to investigate the effects of newly observed hurricane turbulence models on offshore wind turbines by considering unsteady aerodynamic forces on the tower and wind-wave-soil-structure interaction. The specific goals were analyzing the tower and blade structural buffeting responses, the low cycle fatigue during different hurricane categories, and extreme value of the short term responses. To achieve these goals, first, the recent observations on hurricane turbulence models were discussed. Then a new formulation for addressing unsteady wind forces on the tower was introduced and NREL-FAST package was modified with new formulation. Results showed that recently observed turbulence models resulted in larger structural responses and low cycle fatigue damage than existing models. In addition, extreme value analysis of the short term results showed that the IEC 61400-3 recommendation for wind turbine class I was conservative for designing the tower for wind turbine class S subjected to hurricane; however, for designing the blade, IEC 61400-3 recommendations for class I underestimated the responses.

This paper considers the effect of several key parameters of low carbon energy technologies on the cost of abatement. A methodology for determining the minimum level of performance required for a parameter to have a statistically significant impact on CO2 abatement cost is developed and used to evaluate the impact of eight key parameters of low carbon energy supply technologies on the cost of CO2 abatement. The capital cost of nuclear technology is found to have the greatest impact of the parameters studied. The cost of biomass and CCS technologies also have impacts, while their efficiencies have little, if any. Sensitivity analysis of the results with respect to population, GDP, and CO2 emission constraint show that the minimum performance level and impact of nuclear technologies is consistent across the socioeconomic scenarios studied, while the other technology parameters show different performance under higher population, lower GDP scenarios. Solar technology was found to have a small impact, and then only at very low costs. These results indicate that the cost of nuclear is the single most important driver of abatement cost, and that trading efficiency for cost may make biomass and CCS technologies more competitive.

Biomass fractionation technologies are down-selected and economic feasibility of the selected technologies are assessed to produce cellulose and hemicelluloses for chemical catalytic upgrading.

Polar bears (Ursus maritimus) and brown bears (Ursus arctos) are sister species possessing distinct physiological and behavioural adaptations that evolved over the last 500,000 years. However, comparative and population genomics analyses have revealed that several extant and extinct brown bear populations have relatively recent polar bear ancestry, probably as the result of geographically localized instances of gene flow from polar bears into brown bears. Here, we generate and analyse an approximate 20X paleogenome from an approximately 100,000-year-old polar bear that reveals a massive prehistoric admixture event, which is evident in the genomes of all living brown bears. This ancient admixture event was not visible from genomic data derived from living polar bears. Like more recent events, this massive admixture event mainly involved unidirectional gene flow from polar bears into brown bears and occurred as climate changes caused overlap in the ranges of the two species. These findings highlight the complex reticulate paths that evolution can take within a regime of radically shifting climate.