"Cirurgia ortognática e produção da fala."

Niemeyer, Trixy Cristina

doi:10.11606/d.61.2004.tde-05052006-163707

Cited by 5 publications

(8 citation statements)

References 16 publications

(40 reference statements)

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“…The voice changes are possible because surgical procedures that alter the oral cavity, such as orthognathic surgery, may also affect the tongue's position or function [25] . A speech therapy evaluation can detect oral myofunctional changes that could compromise the orthodontic and orthognathic surgery results [26] .…”

Section: Discussionmentioning

confidence: 99%

Knowledge of Internet-using patients about the perioperative period of orthognathic surgery

Barros

Sousa

Turrini

2013

JNEP

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Background: Identify the knowledge and information needs of Internet-using patients undergoing orthognathic surgery by blogs and virtual communities.Methods: Exploratory descriptive study that obtained the data from virtual communities and blogs of Portuguese language not administrated by surgeons or treatment clinics. Thematic content analysis was used to identify the comments of interest and it was identified 257 posts that fit the study's objective. Results:The data were separated in two groups: pre and post-operatory period. The most common topics were related to recovery, paresthesia, costs, edema, fear, and outcome. Other problems, such as pain, eating, anesthesia, regret, mouth opening, kissing, doubt, laser therapy, voice changes, procedures, counsel, difficulty breathing, acne, orthodontics, relief, faith, physical therapy, medical licensure, hair loss, and acupuncture, were also discussed. Conclusions:Based on this analysis, we concluded that principal information needs of internet-using patients are related to the recovery of the masticatory functions and reducing the edema and paresthesia. A good professional-patient bond can help patients to clarify their doubts and avoid the use of Internet to solve their postoperative difficulties.

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Section: Discussionmentioning

confidence: 99%

Knowledge of Internet-using patients about the perioperative period of orthognathic surgery

Barros

Sousa

Turrini

2013

JNEP

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“…Further, we converted all reversible reactions in the reaction mechanisms used here into two irreversible reactions for each in order to avoid the computation of equilibrium constants, as described in Appendix A. We developed an additional Python tool implementing this procedure that is also available online [54]. We paired VODE with CHEMKIN-III [41] to evaluate the chemical kinetics and species thermodynamic properties.…”

Section: Resultsmentioning

confidence: 99%

“…We wrote a Python tool implementing the above conversion for Chemkin-format reaction mechanisms. This software is freely available online [54].…”

Section: Appendix a Irreversible Reaction Mechanism Convertermentioning

confidence: 99%

Accelerating moderately stiff chemical kinetics in reactive-flow simulations using GPUs

Niemeyer

Sung

2014

Journal of Computational Physics

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The chemical kinetics ODEs arising from operator-split reactive-flow simulations were solved on GPUs using explicit integration algorithms. Nonstiff chemical kinetics of a hydrogen oxidation mechanism (9 species and 38 irreversible reactions) were computed using the explicit fifth-order Runge-Kutta-Cash-Karp method, and the GPU-accelerated version performed faster than single-and six-core CPU versions by factors of 126 and 25, respectively, for 524,288 ODEs. Moderately stiff kinetics, represented with mechanisms for hydrogen/carbon-monoxide (13 species and 54 irreversible reactions) and methane (53 species and 634 irreversible reactions) oxidation, were computed using the stabilized explicit second-order Runge-Kutta-Chebyshev (RKC) algorithm. The GPU-based RKC implementation demonstrated an increase in performance of nearly 59 and 10 times, for problem sizes consisting of 262,144 ODEs and larger, than the single-and six-core CPU-based RKC algorithms using the hydrogen/carbon-monoxide mechanism. With the methane mechanism, RKC-GPU performed more than 65 and 11 times faster, for problem sizes consisting of 131,072 ODEs and larger, than the single-and six-core RKC-CPU versions, and up to 57 times faster than the six-core CPU-based implicit VODE algorithm on 65,536 ODEs. In the presence of more severe stiffness, such as ethylene oxidation (111 species and 1566 irreversible reactions), RKC-GPU performed more than 17 times faster than RKC-CPU on six cores for 32,768 ODEs and larger, and at best 4.5 times faster than VODE on six CPU cores for 65,536 ODEs. With a larger time step size, RKC-GPU performed at best 2.5 times slower than six-core VODE for 8192 ODEs and larger. Therefore, the need for developing new strategies for integrating stiff chemistry on GPUs was discussed. models, are the primary bottleneck preventing most industrial and academic researchers from performing and using such studies. Reactive-flow simulations considering detailed chemistry in particular pose prohibitive computational demands due to (1) chemical stiffness, caused by rapidly depleting species and/or fast reversible reactions, and (2) the large and ever-increasing size of detailed reaction mechanisms. While reaction mechanisms for fuels relevant to hypersonic engines, such as hydrogen or ethylene, may contain 10-70 species [1, 2], a recent surrogate mechanism for gasoline consists of about 1550 species and 6000 reactions [3]; a surrogate mechanism for biodiesel contains almost 3300 species and over 10,000 reactions [4]. Strategies for incorporating such large, realistic reaction mechanisms in reactive-flow simulations are beyond the scope of this paper; for example, Lu and Law [5] recently reviewed strategies for mechanism reduction.Even compact mechanisms pose challenges due to stiffness. In the presence of stiffness, explicit integration algorithms generally require time step sizes on the same order as the fastest chemical time scales, which can be many orders of magnitude smaller than the flow time scale [5]. Due to the resulting computat...

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“…These include the three-stage/fifth-order implicit Runge-Kutta algorithm [31] (Radau-IIA), the fourth-order exponential Rosenbrock-like method of Hochbruck et al [36] (exp4), and the newer fourth-order exponential Rosenbrock method [44] (exprb43). For the exponential methods, we used the method of rational approximants [46] paired with the Carathédothy-Fejér method [47,48] to approximate the action of the matrix exponential on a vector, as suggested by Bisetti [37]. This technique relied on the external FFTW3 library [49,50].…”

Section: Integration Techniquesmentioning

confidence: 99%

An investigation of GPU-based stiff chemical kinetics integration methods

Curtis

Niemeyer

Sung

2017

Combustion and Flame

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A fifth-order implicit Runge-Kutta method and two fourth-order exponential integration methods equipped with Krylov subspace approximations were implemented for the GPU and paired with the analytical chemical kinetic Jacobian software pyJac. The performance of each algorithm was evaluated by integrating thermochemical state data sampled from stochastic partially stirred reactor simulations and compared with the commonly used CPU-based implicit integrator CVODE. We estimated that the implicit Runge-Kutta method running on a single Tesla C2075 GPU is equivalent to CVODE running on 12-38 Intel Xeon E5-4640 v2 CPU cores for integration of a single global integration time step of 10 −6 s with hydrogen and methane kinetic models. In the stiffest case studied-the methane model with a global integration time step of 10 −4 s-thread divergence and higher memory traffic significantly decreased GPU performance to the equivalent of CVODE running on approximately three CPU cores. The exponential integration algorithms performed more slowly than the implicit integrators on both the CPU and GPU. Thread divergence and memory traffic were identified as the main limiters of GPU integrator performance, and techniques to mitigate these issues were discussed. Use of a finite-difference Jacobian on the GPU-in place of the analytical Jacobian provided by pyJac-greatly decreased integrator performance due to thread divergence, resulting in maximum slowdowns of 7.11-240.96 ×; in comparison, the corresponding slowdowns on the CPU were just 1.39-2.61 ×, underscoring the importance of use of an analytical Jacobian for efficient GPU integration. Finally, future research directions for working towards enabling realistic chemistry in reactive-flow simulations via GPU/SIMT accelerated stiff chemical kinetics integration were identified.

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"Cirurgia ortognática e produção da fala."

Cited by 5 publications

References 16 publications

Knowledge of Internet-using patients about the perioperative period of orthognathic surgery

Knowledge of Internet-using patients about the perioperative period of orthognathic surgery

Accelerating moderately stiff chemical kinetics in reactive-flow simulations using GPUs

An investigation of GPU-based stiff chemical kinetics integration methods

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