The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples.
Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The engineering limits of plasma facing components (PFCs) constrain the allowable operational space of tokamaks. Poorly managed heat fluxes that push the PFCs beyond their limits not only degrade core plasma performance via elevated impurities, but can also result in PFC failure due to thermal stresses or melting. Simple axisymmetric assumptions fail to capture the complex interaction between 3D PFC geometry and 2D or 3D plasmas. This results in fusion systems that must either operate with increased risk or reduce PFC loads, potentially through lower core plasma performance, to maintain a nominal safety factor. High precision 3D heat flux predictions are necessary to accurately ascertain the state of a PFC given the evolution of the magnetic equilibrium. A new code, the Heat flux Engineering Analysis Toolkit (HEAT), has been developed to provide high precision 3D predictions and analysis for PFCs. HEAT couples many otherwise disparate computational tools together into a single open source python package. Magnetic equilibrium, engineering CAD, finite volume solvers, scrape off layer plasma physics, visualization, high performace computing, and more, are connected in a single web-based user interface. Linux users may use HEAT without any software prerequisites via an appImage. This manuscript introduces HEAT, discusses the software architecture, presents first HEAT results, and outlines physics modules in development.
China is the world's largest carbon emitter and suffers from severe air pollution. About one million deaths in China were attributable to air pollution in 2017. Alternative energy vehicles (AEVs), e.g. electric, hydrogen fuel cell, and natural gas vehicles, can help achieve both carbon emission mitigation and air quality improvement. However, climate, air quality and health co-benefit of AEVs powered by deeply decarbonized electricity generation remain poorly quantified. Here, we conduct a quantitative integrated assessment of the air quality, health, carbon emission mitigation and economic benefits of AEV deployment as the electricity grid decarbonizes in China. We find population-weighted annual PM2.5 and summer O3 concentration can decrease as large as 5.7μgm−3 and 4.9ppb. Annual avoided premature mortalities and years of life lost resulting from improved ambient air pollution can be as large as ~329,000 persons and ~1,611,000 years. We thus show that maximizing climate, air quality and health benefits of AEV deployment in China requires rapid decarbonization of the power system.
