Piezoelectric sensing is of increasing interest for high-temperature applications in aerospace, automotive, power plants and material processing due to its low cost, compact sensor size and simple signal conditioning, in comparison with other high-temperature sensing techniques. This paper presented an overview of high-temperature piezoelectric sensing techniques. Firstly, different types of high-temperature piezoelectric single crystals, electrode materials, and their pros and cons are discussed. Secondly, recent work on high-temperature piezoelectric sensors including accelerometer, surface acoustic wave sensor, ultrasound transducer, acoustic emission sensor, gas sensor, and pressure sensor for temperatures up to 1,250 °C were reviewed. Finally, discussions of existing challenges and future work for high-temperature piezoelectric sensing are presented.
JSC Engineering, Technology, and Science (JETS): Jacobs Technology and HX5, LLC An overview of the capabilities of the CHarring Ablator Response (CHAR) code is presented. CHAR is a one-, two-, and three-dimensional unstructured continuous Galerkin finite-element heat conduction and ablation solver with both direct and inverse modes. Additionally, CHAR includes a coupled linear thermoelastic solver for determination of internal stresses induced from the temperature field and surface loading. Background on the development process, governing equations, material models, discretization techniques, and numerical methods is provided. Special focus is put on the available boundary conditions including thermochemical ablation and contact interfaces, and example simulations are included. Finally, a discussion of ongoing development efforts is presented. Nomenclature α thermal diffusivity ( m 2 /sec) or coefficient of thermal expansion (K −1 ) or absorptivity α t , β t , γ t temporal finite-difference weights (sec −1 ) β extent of reaction also referred to as degree of char for volume fraction in virgin material γ P parameters for defining gas flow contact model stability ( m 2 /sec) and (sec) n unit normal vector κ permeability (m 2 ) λ Lamé's first parameter (Pa) or blowing reduction parameter R residual µ dynamic viscosity (Pa · sec) or Lamé's second parameters (Pa) ν Poisson's ratio Ω domain volume (m 3 ) φ porosity * Applied Aeroscience and CFD Branch 1 of 37 American Institute of Aeronautics and Astronautics Downloaded by MONASH UNIVERSITY on June 23, 2016 | http://arc.aiaa.org | This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AIAA Aviation ψ basis function ρ density ( kg /m 3 ) ρ e u e C H heat transfer film coefficient ( kg /m 2 ·sec) σ Stefan-Boltzmann constant ( W /m 2 ·K 4 ) or stress (Pa) σ P gas flow contact model stability parameter ( m /sec) and ( sec /m) σ T thermal contact model stability parameter ( W /m 2 ·K) τ shear stress (Pa) α coefficient of thermal expansion tensor (K −1 ) σ shear stress tensor (Pa) ε mechanical strain tensor C rotation matrix to shift coordinate reference framẽ K stiffness tensor (Pa) κ permeability tensor (m 2 ) k thermal conductivity tensor ( W /m·K) ε emissivity A element face area (m 2 ) b Klinkenberg parameter (Pa) B non-dimensional mass flux C specific heat ( J /kg·K) C H convective heat transfer Stanton number DE discretization error E activation energy ( J /kg) or Young's modulus (Pa) e o total internal energy ( J /kg) H convective heat transfer coefficient ( Wpre-exponential factor (sec −1 ) or roughness height (m) or thermal conductivity ( W /m·K) k + non-dimensional roughness height m reaction order N number of nodes nc number of components P pressure (Pa) p temporal order of accuracy q spatial order of accuracyfunction y effective mass fractions in solid DoF acronym for degree of freedom PDE acronym for partial differential equation TC abbreviation for thermocouple TPS acronym for thermal protection...
The risk of food limitation and, ultimately, starvation dates back to the dawn of heterotrophy in animals, yet starvation remains a major factor in the regulation of modern animal populations. Researchers studying starvation more than a century ago suggested that animals subjected to sublethal periods of food limitation are somehow more tolerant of subsequent starvation events. This possibility has received little attention over the past decades, yet it is highly relevant to modern science for two reasons. First, animals in natural populations are likely to be exposed to bouts of food limitation once or more before they face prolonged starvation, during which the risk of mortality becomes imminent. Second, our current approach to studying starvation physiology in the laboratory focuses on nourished animals with no previous exposure to nutritional stress. We examined the relationship between previous exposure to food limitation and potentially adaptive physiological responses to starvation in adult rats and found several significant differences. On two occasions, rats were fasted until they lost 20% of their body mass maintained lower body temperatures, and had presumably lower energy requirements when subjected to prolonged starvation than their naive cohort that never experienced food limitation. These rats that were trained in starvation also had lower plasma glucose set -points and reduced their reliance on endogenous lipid oxidation. These findings underscore (1) the need for biologists to revisit the classic hypothesis that animals can become habituated to starvation, using a modern set of research tools; and (2) the need to design controlled experiments of starvation physiology that more closely resemble the dynamic nature of food availability.
A hybrid large-eddy simulation/Reynolds-averaged Navier-Stokes turbulence model is used to simulate the Mach 6 flow around a scaled model similar to NASA's Orion multipurpose crew vehicle. The results for surface pressure and heat transfer are compared with experimental data from previous base flow experiments conducted at the CalspanUniversity at Buffalo Research Center. Using the highest Reynolds number test case (11 × 10 6 based on capsule diameter), different numerical aspects of the hybrid approach are addressed, such as use of a low-dissipation scheme, a modification to the eddy-viscosity blending function, time-averaging results, filtering computational results, and sensitivity to grid resolution. In addition, results are compared with Reynolds-Averaged Navier-Stokes using Menter's two-equation baseline model and to detached-eddy simulation predictions. By introducing a new modification to the blending from Reynolds-averaged Navier-Stokes to large-eddy simulation within boundary layers, very good agreement with the experiment is obtained in regions where the boundary-layer grid spacing is too coarse for large-eddy simulation. The findings show that the high-fidelity schemes produce results that agree much better with the experimental data than Reynolds-averaged Navier-Stokes methods, which tend to underpredict base pressures and overpredict heat fluxes. The overall accuracy of each scheme is evaluated using a normalized root mean square error in different regions of the flow, and the analysis shows that, in separated regions, the integrated error is over 120% using a Reynolds-averaged Navier-Stokes model, and 30-50% using the higher fidelity schemes. Additionally, the hybrid methodology presented is further validated by considering a lower Reynolds number (6 × 10 6 ), where the flow is nominally transitional, and very accurate heat transfer predictions are also obtained. NomenclatureC M = hybrid large-eddy simulation/Reynolds-averaged Navier-Stokes model constant, 0.06 D = capsule diameter, m d = distance from the nearest wall, m gl out = blending correction function k, k R = turbulence, resolved kinetic energy, m 2 ∕s 2 l inn , l out = inner, outer boundary-layer length scale, m M ∞ = freestream Mach number Re D = Reynolds number based on capsule diameter S = vorticity tensor, 1∕s T wall = wall temperature, K T ∞ = freestream temperature, K t = time, s U ∞ = freestream velocity, m∕s u i = velocity component, m∕ŝũ = resolvable-scale Favre-averaged velocity component, m∕s u = Favre-averaged velocity component, m∕s Γ = large-eddy simulation/Reynolds-averaged NavierStokes blending function Δ = cell volume, m 3 Δ max = maximum grid spacing over the three coordinate directions, m ϵ = turbulent kinetic energy dissipation rate, m 2 ∕s 3 κ = von Kármán constant λ = length scale ratio for blending function μ μ T= molecular, turbulent viscosity, Pa · s ν = kinematic viscosity, m 2 ∕s ν T = kinematic eddy viscosity, m 2 ∕s ν T;SGS = subgrid kinematic eddy viscosity, m 2 ∕s ρ = density, kg∕m 3 ρ ∞ = freestream density...
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