1,565 research outputs found

    Community Heat Pump Systems Utilizing Oil-Free Compressor Technology

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    The goal to decarbonize buildings is quickly driving growth in the adoption of heat pumps to replace fossil fuel-based heating equipment. The trend is significantly driven by the integration of renewables in the electric grid, also replacing fossil fuel-based sources, to in-turn drive decarbonization. The impact of this change is greater if also changing out end-use fossil fuel-based heating equipment to electric-driven. With the trend to heat pumps, there is a critical choice on both the scale and corresponding heat source. The heat pumps can be implemented with ambient air as the heat source or with other higher temperature/efficiency sources such as geothermal, process or district cooling heat recovery. Nevertheless, these sources are not always available in close proximity to the heat load. This raises the opportunity for larger-scale heating systems, serving multiple loads and with the corresponding opportunity to integrate one or more potential higher-temperature heat recovery heat sources. A related critical factor with the growth of heat pumps is resiliency. The term has historically mainly been associated with critical facilities and the ability to withstand critical events. Now it’s evolving because of that same integration of renewables into the power grid and their inconsistent availability. Now resiliency has more to do with preparing for this periodic unavailability – Ensuring that demand is met when supply is not necessarily available. This paper will present a concept for a community heating and cooling system utilizing oil-free turbo compressor technology, to address the electrification of heating while also taking advantage of multiple higher-temperature heat sources. The technology and heat sources ensure the most efficient system possible, resulting in minimum operating costs and maximum decarbonization, while the community configuration and multiple sources ensure resiliency, consistently meeting the demand requirements

    Magnetic local time‐resolved examination of radiation belt dynamics during high speed solar wind speedtTriggered substorm clusters

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    Particle observations from low Earth orbiting satellites are used to undertake superposed epoch analysis around clusters of substorms, in order to investigate radiation belt dynamical responses to mild geomagnetic disturbances. Medium energy electrons and protons have drift periods long enough to discriminate between processes occurring at different MLT, such as magnetopause shadowing, plasma wave activity, and substorm injections. Analysis shows that magnetopause shadowing produces clear loss in proton and electron populations over a wide range of L‐shells, initially on the dayside, which interact with nightside substorm‐generated flux enhancements following charge‐dependent drift directions. Inner magnetospheric injections recently identified as an important source of 10's to 100's keV electrons at low L (L<3), occurring during similar solar wind‐driving conditions as recurrent substorms, show similar but more enhanced geomagnetic AU‐index signatures. Two‐fold increases in substorm occurrence at the time of the sudden particle enhancements at low L shells (SPELLS), suggests a common linkage

    The Importance of Heat Flux in Quasi-Parallel Collisionless Shocks

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    Collisionless plasma shocks are a common feature of many space and astrophysical systems and are sources of high-energy particles and non-thermal emission, channeling as much as 20\% of the shock's energy into non-thermal particles. The generation and acceleration of these non-thermal particles have been extensively studied, however, how these particles feed back on the shock hydrodynamics has not been fully treated. This work presents the results of self-consistent hybrid particle-in-cell simulations that show the effect of self-generated non-thermal particle populations on the nature of collisionless, quasi-parallel shocks. They contribute to a significant heat flux density upstream of the shock. Non-thermal particles downstream of the shock leak into the upstream region, taking energy away from the shock. This increases the compression ratio, slows the shock down, and flattens the non-thermal population's spectral index for lower Mach number shocks. We incorporate this into a revised theory for the Rankine-Hugoniot jump conditions that include this effect and it shows excellent agreement with simulations. The results have the potential to explain discrepancies between predictions and observations in a wide range of systems, such as inaccuracies of predictions of arrival times of coronal mass ejections and the conflicting radio and x-ray observations of intracluster shocks. These effects will likely need to be included in fluid modeling to accurately predict shock evolution.Comment: 7 pages, 3 figures, a lot of appendi

    Modeling Radiation Belt Electrons With Information Theory Informed Neural Networks

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    An empirical model of radiation belt relativistic electrons (ÎŒ = 560–875 MeV G−1 and I = 0.088–0.14 RE G0.5) with average energy ∌1.3 MeV is developed. The model inputs solar wind parameters (velocity, density, interplanetary magnetic field (IMF) |B|, Bz, and By), magnetospheric state parameters (SYM-H and AL), and L*. The model outputs the radiation belt electron phase space density (PSD). The model is operational from L* = 3 to 6.5. The model is constructed with neural networks assisted by information theory. Information theory is used to select the most effective and relevant solar wind and magnetospheric input parameters plus their lag times based on their information transfer to the PSD. Based on the test set, the model prediction efficiency (PE) increases with increasing L*, ranging from −0.043 at L* = 3 to 0.76 at L* = 6.5. The model PE is near 0 at L* = 3–4 because at this L* range, the solar wind and magnetospheric parameters transfer little information to the PSD. Using solar wind observations at L1 and magnetospheric index (AL and SYM-H) models solely driven by solar wind, the radiation belt model can be used to forecast PSD 30–60 min ahead. This baseline model can potentially complement a class of empirical models that input data from low earth orbit (LEO)
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