Cosmological Particle Production in Quantum Gravity

Quantum theory of a test field on a quantum cosmological spacetime may be viewed as a theory of the test field on an emergent classical background. In such a case, the resulting dressed metric for the field propagation is a function of the quantum fluctuations of the original geometry. When the backreaction is negligible, massive modes can experience an anisotropic Bianchi type I background. The field modes propagating on such a quantum-gravity-induced spacetime can then unveil interesting phenomenological consequences of the super-Planckian scales, such as gravitational particle production. The aim of this paper is to address the issue of gravitational particle production associated with the massive modes in such an anisotropic dressed spacetime. By imposing a suitable adiabatic condition on the vacuum state and computing the energy density of the created particles, the significance of the particle production on the dynamics of the universe in Planck era is discussed.

A Study on the Prediction of the Temperature and Mass of Hydrogen Gas inside a Tank during Fast Filling Process

The hydrogen compression cycle system recycles hydrogen compressed by a compressor at high pressure and stores it in a high-pressure container. Thermal stress is generated due to increase in the pressure and temperature of hydrogen in the hydrogen storage tank during the fast filing process. For the sake of safety, it is of great practical significance to predict and control the temperature change in the tank. The hydrogen charging process in the storage tank of the hydrogen charging station was studied by experimentation and simulation. In this paper, a Computational Fluid Dynamics (CFD) model for non-adiabatic real filling of a 50 MPa hydrogen cylinder was presented. In addition, a shear stress transport (k-ω) model and real gas model were used in order to account for thermo-fluid dynamics during the filling of hydrogen storage tanks (50 MPa, 343 L). Compared to the simulation results with the experimental data carried out under the same conditions, the temperatures calculated from the simulated non-adiabatic condition results were lower (by 5.3%) than those from the theoretical adiabatic condition calculation. The theoretical calculation was based on the experimentally measured pressure value. The calculated simulation mass was 8.23% higher than the theoretical result. The results of this study will be very useful in future hydrogen energy research and hydrogen charging station developments.

A theoretical analysis of temperature rise of hydrogen in high-pressure storage cylinder during fast filling process

During the fast filing process, thermal stress is generated due to the increase in the pressure and temperature of hydrogen in the hydrogen storage tank. For its safety purpose, it is necessary to predict and control the temperature change in the tank. The aim of this study is quantitative analysis of the final temperature and the mass of the hydrogen in the tank through experimental and theoretical methods. In this paper; Theoretical model for adiabatic and non-adiabatic real filling processes of high pressure hydrogen cylinder has been proposed. The cycle of filling process from the initial vacuum state is called the “First cycle.” After the first cycle is completed, there is a certain residual pressure in the tank. Then the second filling process called “Second cycle” begins. The final temperature in fast filling of hydrogen storage cylinders depends on targeted pressure, initial pressure and temperature, and mass filling rate. The final temperature of hydrogen in the tank was calculated from the real gas equation of state, mass and energy conservation equations. As a result of the analysis, based on the first cycle analysis of high pressure tank, the final temperatures were calculated to be 442.11 K for the adiabatic filling process, and 422.37 K for the non-adiabatic process. Based on the second cycle analysis of high pressure tank, the final temperature were obtained as 397.12 K and 380.8 K for the adiabatic and non-adiabatic processes, respectively. The temperatures calculated from the theoretical non-adiabatic condition were lower than those from the adiabatic condition by 5%. The results of this study can provide a reference basis in terms of how to control the temperature in the actual hydrogen storage tank during the fast filling process and how to improve safety.

Adiabatic Quantum Computing and Quantum Annealing

Adiabatic quantum computing (AQC) is a model of computation that uses quantum mechanical processes operating under adiabatic conditions. As a form of universal quantum computation, AQC employs the principles of superposition, tunneling, and entanglement that manifest in quantum physical systems. The AQC model of quantum computing is distinguished by the use of dynamical evolution that is slow with respect to the time and energy scales of the underlying physical systems. This adiabatic condition enforces the promise that the quantum computational state will remain well-defined and controllable thus enabling the development of new algorithmic approaches. Several notable algorithms developed within the AQC model include methods for solving unstructured search and combinatorial optimization problems. In an idealized setting, the asymptotic complexity analyses of these algorithms indicate computational speed-ups may be possible relative to state-of-the-art conventional methods. However, the presence of non-ideal conditions, including non-adiabatic dynamics, residual thermal excitations, and physical noise complicate the assessment of the potential computational performance. A relaxation of the adiabatic condition is captured in the complementary computational heuristic of quantum annealing, which accommodates physical systems operating at finite temperature and in open environments. While quantum annealing (QA) provides a more accurate model for the behavior of actual quantum physical systems, the possibility of non-adiabatic effects obscures a clear separation with conventional computing complexity. A series of technological advances in the control of quantum physical systems have enabled experimental AQC and QA. Prominent examples include demonstrations using superconducting electronics, which encode quantum information in the magnetic flux induced by a weak current operating at cryogenic temperatures. A family of devices developed specifically for unconstrained optimization problems has been applied to solve problems in specific domains including logistics, finance, material science, machine learning, and numerical analysis. An accompanying infrastructure has also developed to support these experimental demonstrations and to enable access of a broader community of users. Although AQC is most commonly applied in superconducting technologies, alternative approaches include optically trapped neutral atoms and ion-trap systems. The significant progress in the understanding of AQC has revealed several open topics that continue to motivate research into this model of quantum computation. Foremost is the development of methods for fault-tolerant operation that will ensure the scalability of AQC for solving large-scale problems. In addition, unequivocal experimental demonstrations that differentiate the computational power of AQC and its variants from conventional computing approaches are needed. This will also require advances in the fabrication and control of quantum physical systems under the adiabatic restrictions.