GPU accelerated numerical investigation of the spherical stability of an acoustic cavitation bubble excited by dual-frequency.
Ontology highlight
ABSTRACT:
SUBMITTER: Klapcsik K
PROVIDER: S-EPMC8350425 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The dataset described in this paper is related to the paper [1], in which the exponential growth rate values of a spherically oscillating gas bubble modelled by the Keller-Miksis equation are recorded. As the bubble is excited by dual-frequency, the employed parameters are the pressure amplitudes and frequencies corresponding to the first and second harmonic components, respectively, the phase shift between harmonic components, and the equilibrium bubble radius. At each parameter combinations the exponential growth rate values corresponding to mode 2 up to mode 6, and the maximum bubble radius are stored. The huge amount of numerical data are generated that are stored in a public repository [2]. The present paper describes the generated data, the applied numerical model and the implementation details of the program code used to generate the data on graphics processing units (GPUs).
| S-EPMC8762084 | biostudies-literature
Project description:Sonoporation is the membrane disruption generated by ultrasound and has been exploited as a non-viral strategy for drug and gene delivery. Acoustic cavitation of microbubbles has been recognized to play an important role in sonoporation. However, due to the lack of adequate techniques for precise control of cavitation activities and real-time assessment of the resulting sub-micron process of sonoporation, limited knowledge has been available regarding the detail processes and correlation of cavitation with membrane disruption at the single cell level. In the current study, we developed a combined approach including optical, acoustical, and electrophysiological techniques to enable synchronized manipulation, imaging, and measurement of cavitation of single bubbles and the resulting cell membrane disruption in real-time. Using a self-focused femtosecond laser and high frequency ultrasound (7.44MHz) pulses, a single microbubble was generated and positioned at a desired distance from the membrane of a Xenopus oocyte. Cavitation of the bubble was achieved by applying a low frequency (1.5MHz) ultrasound pulse (duration 13.3 or 40?s) to induce bubble collapse. Disruption of the cell membrane was assessed by the increase in the transmembrane current (TMC) of the cell under voltage clamp. Simultaneous high-speed bright field imaging of cavitation and measurements of the TMC were obtained to correlate the ultrasound-generated bubble activities with the cell membrane poration. The change in membrane permeability was directly associated with the formation of a sub-micrometer pore from a local membrane rupture generated by bubble collapse or bubble compression depending on ultrasound amplitude and duration. The impact of the bubble collapse on membrane permeation decreased rapidly with increasing distance (D) between the bubble (diameter d) and the cell membrane. The effective range of cavitation impact on membrane poration was determined to be D/d=0.75. The maximum mean radius of the pores was estimated from the measured TMC to be 0.106±0.032?m (n=70) for acoustic pressure of 1.5MPa (duration 13.3?s), and increased to 0.171±0.030?m (n=125) for acoustic pressure of 1.7MPa and to 0.182±0.052?m (n=112) for a pulse duration of 40?s (1.5MPa). These results from controlled cell membrane permeation by cavitation of single bubbles revealed insights and key factors affecting sonoporation at the single cell level.
| S-EPMC3258473 | biostudies-literature
Project description:We have demonstrated experimentally the existence of a giant frequency down-conversion of the translational oscillatory motion of individual submillimeter acoustic bubbles in water in the presence of a high frequency (500?kHz) ultrasonic standing wave. The frequency of the translational oscillations (~170?Hz) is more than three orders of magnitude smaller than that of the driving acoustic wave. We elucidate the mechanism of this very slow oscillation with an analytical model leading to an equation of translational motion of a bubble taking the form of Mathieu's equation. This equation illuminates the origin of the giant down conversion in frequency as arising from an unstable equilibrium. We also show that bubbles that form chains along the direction of the acoustic standing wave due to radiation interaction forces exhibit also translation oscillations that form a spectral band. This band extends approximately from 130?Hz up to nearly 370?Hz, a frequency range that is still at least three orders of magnitude lower than the frequency of the driving acoustic wave.
| S-EPMC5114654 | biostudies-literature
Project description:The miniaturization boiling (micro-bubble emission boiling [MEB]) phenomenon, with a high heat removal capacity that contributes considerably to the cooling of the divertor of the nuclear fusion reactor, was discovered in the early 1980s. Extensive research on MEB has been performed since its discovery. However, the progress of the application has been delayed because the generation mechanism of MEB remains unclear. Reasons for this lack of clarity include the complexity of the phenomenon itself and the high-speed phase change phenomenon in which boiling and condensation are rapidly generated. In addition, a more advanced thermal technique is required to realize the MEB phenomenon at the laboratory scale. To the authors' knowledge, few studies have discussed the rush mechanism of subcooled liquid to the heating surface, which is critical to elucidating the mechanism behind MEB. This study used photographic images to verify that the cavitation phenomenon spreads to the inside of the superheated liquid on the heating surface and thus clarify the mechanism of MEB.
| S-EPMC5024302 | biostudies-literature
Project description:The acoustic cavitation bubble as an open energetic system is the seat of conversion of various forms of energy accompanying the bubble oscillation. The energy conversion would explain specific dynamical, thermal and kinetical behaviors. In the present paper, the energy balance related to a stable bubble irradiated by dual-frequency field is simulated numerically and interpreted in accordance with the phenomena occurring inside it. The study particularly focuses on the comparison of the energetic behavior of high-energy stable cavitation with bubbles that are non-active in sonochemistry, submitted to couples of 35, 140, 300 and 515 kHz. The simulation results revealed that pressure forces work is the major energetic input during the bubble oscillation lifetime, while the main energetic loss comes from heat transfer by diffusion and enthalpy loss accompanying water condensation. Besides, high rates of condensation of water molecules and low amounts of accumulated energy inside the bubble volume were identified as the key factors preventing the achievement of the sonochemical activity threshold.
| S-EPMC7872890 | biostudies-literature
Project description:The soot suppression by acoustic oscillations for acetylene diffusion flames was investigated combining numerical and experimental studies. The combustion and soot formation were predicted by the finite-rate detailed chemistry model and modified Moss-Brookes model, respectively, while the turbulence was predicted by the detached eddy simulation (DES) with a low Reynolds number correction. Experimental results showed that the soot rate almost decreased linearly with the amplitude of acoustic oscillation, and the pinch-off of the flame occurred at a large acoustic oscillation. Numerical results showed that the flame structure was well predicted, while the soot rate was over-predicted at large acoustic oscillations; the consumption of O2 increased obviously with the acoustic oscillation. The soot suppression was mainly caused by the decrease of the surface growth rate when the air was pushed toward the flame.
| S-EPMC7513364 | biostudies-literature
Project description:Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
| S-EPMC4772124 | biostudies-literature
Project description:MotivationMathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code.ResultsWe present a Python package, cuda-sim, that provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler-Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code. For running a large number of simulations in parallel, up to 360-fold decrease in simulation runtime is attained when compared to single CPU implementations.Availabilityhttp://cuda-sim.sourceforge.net/
| S-EPMC3051321 | biostudies-literature
Project description:Structural evolution from monomer to fibril of amyloid ? peptide is related to pathogenic mechanism of Alzheimer disease, and its acceleration is a long-running problem in drug development. This study reveals that ultrasonic cavitation bubbles behave as catalysts for nucleation of the peptide: The nucleation reaction is highly dependent on frequency and pressure of acoustic wave, and we discover an optimum acoustical condition, at which the reaction-rate constant for nucleation is increased by three-orders-of magnitudes. A theoretical model is proposed for explaining highly frequency and pressure dependent nucleation reaction, where monomers are captured on the bubble surface during its growth and highly condensed by subsequent bubble collapse, so that they are transiently exposed to high temperatures. Thus, the dual effects of local condensation and local heating contribute to dramatically enhance the nucleation reaction. Our model consistently reproduces the frequency and pressure dependences, supporting its essential applicability.
| S-EPMC4766559 | biostudies-literature
Project description:GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabolomics. However, this elucidation process has been hampered by complex experimental data and the inability of instruments to completely separate different compounds. Fortunately, with current high-resolution mass spectrometry, one feasible strategy is to define this problem as extending a scaffold database with sidechains of different probabilities to match the high-resolution mass obtained from a high-resolution mass spectrum. By introducing a dynamic programming (DP) algorithm, it is possible to solve this NP-complete problem in pseudo-polynomial time. However, the running time of the DP algorithm grows by orders of magnitude as the number of mass decimal digits increases, thus limiting the boost in structural prediction capabilities. By harnessing the heavily parallel architecture of modern GPUs, we designed a "compute unified device architecture" (CUDA)-based GPU-accelerated mixture elucidator (G.A.M.E.) that considerably improves the performance of the DP, allowing up to five decimal digits for input mass data. As exemplified by four testing datasets with verified constitutions from natural products, G.A.M.E. allows for efficient and automatic structural elucidation of unknown mixtures for practical procedures. Graphical abstract .
| S-EPMC5602814 | biostudies-other