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A prototype air cycle cooling system, designed for forced cooling of HPOF pipe-type cable, was built under an EPRI contract. Its main components are a turbine and compressor operated on a single high speed shaft and a compact cable. oil to air heat exchanger. Air cooling is supplemented by a spray water system. Tests were conducted on the prototype unit at the EPRI Waltz Mill Cable Test Facility to evaluate its performance and operating characteristics. This paper briefly describes this equipment and presents the results of the test program.

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.

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.

Biodiesel is a fuel generally consisting of a mixture of fatty acid methyl esters (FAMEs) which is used in alternative or in combination with petroleum diesel for its environmental benefits. Biodiesel is conveniently manufactured from vegetable oils by transesterification of triglycerides with methanol. However, the process brings about the concurrent formation of glycerol, which may become an oversupplied chemical if biodiesel production keeps growing. A novel biodiesel-like material (abbreviated as DMC-BioD) was developed by reacting soybean oil with dimethyl carbonate (DMC), which avoided the co-production of glycerol. The main difference between DMC-BioD and biodiesel produced from vegetable oil and methanol (MeOH-biodiesel) was the presence of fatty acid glycerol carbonate monoesters (FAGCs) in addition to FAMEs. In the following study, details regarding synthesis and composition of DMC-BioD are provided along with physical properties relevant for its use as a fuel. In addition, the production of potential pyrogenic contaminants was investigated by analytical pyrolysis and compared with those from MeOH-biodiesel, and the model compounds tristearin, triolein, trilinolein and oleic acid glycerol carbonate ester (OAGC). The presence of FAGCs influenced both fuel and flow properties, while the distribution of main pyrogenic compounds, including polycyclic aromatic hydrocarbons (PAHs), was little affected. Benefits and drawbacks of DMC as a candidate transmethylating reagent for producing biofuel from renewable resources and alternative co-products (glycerol carbonate and glycerol dicarbonate) are discussed.

This research examines the economic implications of different designs for a national low carbon fuel standard (NLCFS) for the road transportation sector. A NLCFS based on the average Carbon Intensity (CI) of all fuels sold generates an incentive for fuel suppliers to reduce the measured CI of their fuels. The economic impacts are determined by the availability of low carbon fuels, estimates of which can vary widely. Also important are the compliance path, reference level CI, and the design of the credit system, particularly the opportunities for trading and banking. To quantitatively examine the implications of a NLCFS, we created the Transportation Regulation and Credit Trading (TRACT) Model. With TRACT, we model a NLCFS credit trading system among profit maximizing fuel suppliers for light- and heavy-duty vehicle fuel use for the United States from 2012 to 2030. We find that credit trading across gasoline and diesel fuel markets can lower the average costs of carbon reductions by an insignificant amount to 98% depending on forecasts of biofuel supplies and carbon intensities. Adding banking of credits on top of trading can further lower the average cost of carbon reductions by 5%–9% and greatly reduce year-to-year fluctuations in credit prices.