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Abstract. The impact of multiphase reactions involving nitrogen dioxide (NO2) and aromatic compounds was simulated in this study. A mechanism (CAPRAM 2.4, MODAC Mechanism) was applied for the aqueous phase reactions, whereas RACM was applied for the gas phase chemistry. Liquid droplets were considered as monodispersed with a mean radius of 0.1 µm and a liquid content (LC) of 50 µg m-3. The multiphase mechanism has been further extended to the chemistry of aromatics, i.e. reactions involving benzene, toluene, xylene, phenol and cresol have been added. In addition, reaction of NO2 with dissociated hydroxyl substituted aromatic compounds has also been implemented. These reactions proceed through charge exchange leading to nitrite ions and therefore to nitrous acid formation. The strength of this source was explored under urban polluted conditions. It was shown that it may increase gas phase HONO levels under some conditions and that the extent of this effect is strongly pH dependent. Especially under moderate acidic conditions (i.e. pH above 4) this source may represent more than 75% of the total HONO/NO2 - production rate, but this contribution drops down close to zero in acidic droplets (as those often encountered in urban environments).

Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.

A specific role for the HTGR in a national energy strategy is examined. The issue is addressed in two ways. First, the role of the HTGR-GT Binary cycle plant is examined in a national energy strategy based on symbiosis between fast breeder and advanced converter reactors utilizing the thorium U233 fuel cycle. Second, the advantages of the HTGR-GT dry-cooled plant operating in arid regions is examined and compared with a dry-cooled LWR. An event tree analysis of potential benefits is applied.

Coupling between the UEDGE (edge fluid model), GINGRED (grid generation) and CAKE (equilibrium reconstruction) codes opens the door for automated interpretative scrape-off-layer (SOL) analysis over entire discharges, providing information that is essential in efforts to couple the SOL to core transport codes. In this work, we utilize new developments in the autoUEDGE code (Izacard et al. 2018) to investigate the behavior of the DIII-D SOL during the temporal evolution of an edge-localized mode (ELM) cycle. Modeled temperature and density profiles in UEDGE are automatically matched to experimental measurements by iteratively and self-consistently adjusting transport coefficient profiles in the plasma edge. This analysis is completed over multiple ELM cycles of a well-diagnosed discharge with long (∼100ms) inter-ELM periods. Directly after the ELM crash, a short period of high-density, low-temperature conditions is observed in Langmuir probe measurements at the outer divertor. This regime is associated with enhanced Dαemission and incident particle flux, suggesting that the divertor enters a period of high recycling after an ELM crash. After about ∼25ms, divertor conditions return to their pre-ELM conditions and remain there for several tens of milliseconds. Using the autoUEDGE code, the SOL is modeled as a function of ELM cycle using upstream profiles as input. The 2D modeling successfully reproduces both divertor Thomson scattering measurements and the experimentally observed divertor dynamics. Though the recycling is kept fixed throughout the modeling, changes in particle fluxes are consistent with local experimental recycling changes induced by ELMs. Agreement between modeling and observation suggests a strong link between upstream profiles and the high-recycling divertor conditions directly following large type-I ELMs.

AbstractThe aim of this work is to characterize a 5MWe Fresnel solar power plant at different areas of Algeria Sahara. These areas have been chosen for comparison by shifting the plant in different locations; namely HassiR’mel, Tamanrasset, Beni-Abbes, and El Oued. Theoretical modeling has allowed the determination of mirror optimum number to avoid shadowing effects, blocking and cosine effect. Mirror optimum number was estimated at 40 mirrors. Furthermore, a simulation was carried out on a time scale, regarding insolation, “field” and “receiver” systems. The simulation results are depicted on annual basis. Efficiency, annual energy generation and the cost per kWh are evaluated. Indeed, the calculation of efficiency differs from a site to another with values of 15.7%, 11.8%, 11.6% and 10.4%, respectively for Tamanrasset, El Oued, Beni-Abbes, HassiR’mel. Thermal performances, technical and economic results have been also evaluated for the said sites.

SignificanceMarginal emissions of CO2from the electricity sector are critical for evaluating many climate policies. We provide estimates of marginal CO2emissions for electricity use in the United States that vary by region, hour of day, and year to year. Despite a decrease in average emissions over the last decade, marginal emissions have increased. We apply our estimates to an analysis of the Biden administration’s target of having electric vehicles make up 50% of new vehicle purchases by 2030. We find that, without significant and concurrent changes to the electricity sector far more substantial than those over the last decade, the increase in electricity emissions is likely to offset more than half the emission reductions from having fewer gasoline-powered vehicles.