• Energy Research
• 2021-2025close
• 7. Clean energy close
• 12. Responsible consumption close
• US close

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.

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.

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.

<abstract> <p>Fish is a key component of Nigeria's protein supply, making up about 40% of the nation's protein intake and considerably aiding in the achievement of the second Sustainable Development Goal of feeding the expanding population. Despite its importance, Nigeria's fish production and supply cannot keep up with demand. While total fish output has increased from 1,073,059 tonnes in 2014 to 1,169,000 tonnes in 2018 and is expected to reach 1,275,000 tonnes by 2030, there is a great supply gap. Fish production not only affects food security but also the national economy and employment. Notwithstanding, the fisheries sub-sector suffers several difficulties, such as poor management, a deficient fisheries policy, overfishing, diminishing catch, and a lack of technical know-how among fish growers and fishermen. Thus, exploring untapped aquaculture potential and managing small-scale fisheries effectively are necessary to close the gap between the demand for and supply of fish. The fish output situation can be improved by enforcing fisheries policy and regulations, increasing investments in ethical fisheries and aquaculture, and providing sufficient training for fish farmers and fisherfolk. To reduce waste associated with the limited number of fish now produced, post-harvest losses must also be addressed. By solving these issues and putting in place the necessary actions, Nigeria can increase its fish production, strengthen its food security, and accomplish the sustainable development goals in its evolving blue economy.</p> </abstract>

As is the case with several other mechanical power takeoffs (PTOs), the mechanical-motion-rectifier-based PTO consists of components, such as one-way clutches, gears, a ball screw, mechanical couplings, and a generator. Equivalent circuit models have been created in this article to describe the dry frictions, viscous damping, and mechanical compliances in these components, so the nonideal efficiency and nonlinear force of the PTO can be predicted in electrical simulations by integrating these subcircuit models. The circuit model is simplified, and its parameters are categorized as dc and ac unknowns. The dc and ac tests on the PTO are performed sequentially to extract two sets of parameters through linear regression or nonlinear curve fitting. Then, the model is validated through its prediction capability over 25 test conditions on input forces, output voltages, and efficiencies, with correlation coefficients of 0.9, 0.98, and 0.981, respectively.

Abstract Hydraulic ram pump (HRP), also known as hydram, lifts water without using external power input. Its low performance combined with affordability of fuels has put this otherwise longstanding technology in the backburner of science and research for a long time, yielding to electric or fuel powered pumps. However, growing concerns about the impacts of fossil fuel use on the environment as well as the rising price of electricity has generated a renewed interest in such technology. The ram pump's operation in remote areas where power grid is not available adds research value on the technology. In this project, a novel approach, i.e., adding thermal energy to the flow to assist the water hammer pressure was modeled. Computational fluid dynamics (CFD) simulation was conducted using ansys. The results were validated experimentally in a 32 mm (27 mm internal diameter) drive pipe and a supply head of 2.18 m ram pump. The Analytical approach was more conservative. The results between simulation and experiment were fairly consistent, with only 6.99% error for pressure, and 10.16% for flowrate. The results show that pressure increased from 183.33 kPa to 342.32 kPa when thermally assisted to reach 150 °C. The experimental discharge flow increased from 11.72 l/min to 16.41 l/min for the corresponding temperature, a 42.01% increase.

Accurate estimation of battery state of charge (SOC) is of great significance to improve battery management and service life. An unscented Kalman filter (UKF) method is used to increase the accuracy of SOC estimation in this paper. Firstly, a battery model that the parameters are identified by using the least squares algorithm is established, which is foundation of the two-order RC equivalent circuit model. Secondly, SOC is estimated by UKF. In order to validate the method, experiments have been carried out under different operating conditions for LiFePO4 batteries. The obtained results are compared with that of the extended Kalman filter. Finally, the comparison shows that the UKF method provides better accuracy in the battery SOC estimation. Its estimation error is less than 2%, which is better than EKF algorithm. An effective method is provided for state estimation for battery management system.

Conductive polymers are widely used as active and auxiliary materials for organic photovoltaic cells due to their easily tunable properties, high electronic conductivity, and light absorption. Several conductive polymers show the cathodic photogalvanic effect in pristine state. Recently, photoelectrochemical oxygen reduction has been demonstrated for nickel complexes of Salen-type ligands. Herein, we report an unexpected inversion of the photogalvanic effect caused by doping of the NiSalen polymers with anionic porphyrins. The observed effect was studied by means of UV-Vis spectroscopy, cyclic voltammetry and chopped light chronoamperometry. While pristine NiSalens exhibit cathodic photopolarization, doping with porphyrins inverts the polarization. As a result, photoelectrochemical oxidation of the ascorbate proceeds smoothly on the NiSalen electrode doped with zinc porphyrins. The highest photocurrents were observed on NiSalen polymer with o-phenylene imine bridge, doped with anionic zinc porphyrin. Assuming this, porphyrin serves both as a catalytic center for the oxidation of ascorbate and an internal electron donor, facilitating the photoinduced charge transport and anodic depolarization.