Exponential suppression of thermal conductance using coherent transport and heterostructures

We consider coherent thermal conductance through multilayer photonic crystal heterostructures, consisting of a series of cascaded non-identical photonic crystals. We show that thermal conductance can be suppressed exponentially with the number of cascaded crystals, due to the mismatch between photonic bands of all crystals in the heterostructure.Comment: 15 pages, 4 figure

Heat Transfer in High Temperature Multilayer Insulation

High temperature multilayer insulations have been investigated as an effective component of thermal-protection systems for atmospheric re-entry of reusable launch vehicles. Heat transfer in multilayer insulations consisting of thin, gold-coated, ceramic reflective foils and Saffil(TradeMark) fibrous insulation spacers was studied both numerically and experimentally. A finite volume numerical thermal model using combined conduction (gaseous and solid) and radiation in porous media was developed. A two-flux model with anisotropic scattering was used for radiation heat transfer in the fibrous insulation spacers between the reflective foils. The thermal model was validated by comparison with effective thermal conductivity measurements in an apparatus based on ASTM standard C201. Measurements were performed at environmental pressures in the range from 1x10(exp -4) to 760 torr over the temperature range from 300 to 1300 K. Four multilayer samples with nominal densities of 48 kg/cu m were tested. The first sample was 13.3 mm thick and had four evenly spaced reflective foils. The other three samples were 26.6 mm thick and utilized either one, two, or four reflective foils, located near the hot boundary with nominal foil spacing of 1.7 mm. The validated thermal model was then used to study relevant design parameters, such as reflective foil spacing and location in the stack-up and coating of one or both sides of foils

Multipole expansion for H I intensity mapping experiments: simulations and modelling

We present a framework and an open-source python toolkit to analyse the 2-point statistics of 3D fluctuations in the context of HI intensity maps using the multipole expansion formalism. We include simulations of the cosmological HI signal using N-body and log-normal methods, foregrounds and their removal, as well as instrumental effects. Using these simulations and analytical modelling, we investigate the impact of foreground cleaning and the instrumental beam on the power spectrum multipoles as well as on the Fourier space clustering wedges. We find that both the instrumental beam and the foreground removal can produce a quadrupole (and a hexadecapole) signal, and demonstrate the importance of controlling and accurately modelling these effects for precision radio cosmology. We conclude that these effects can be modelled with reasonable accuracy using our multipole expansion technique. We also perform an MCMC analysis to showcase the effect of foreground cleaning on the estimation of the HI abundance and bias parameters. The accompanying python toolkit is available at https://github.com/IntensityTools/MultipoleExpansion, and includes an interactive suite of examples to aid new users.Comment: 21 pages, 14 figure

Combined Heat Transfer in High-Porosity High-Temperature Fibrous Insulations: Theory and Experimental Validation

Combined radiation and conduction heat transfer through various high-temperature, high-porosity, unbonded (loose) fibrous insulations was modeled based on first principles. The diffusion approximation was used for modeling the radiation component of heat transfer in the optically thick insulations. The relevant parameters needed for the heat transfer model were derived from experimental data. Semi-empirical formulations were used to model the solid conduction contribution of heat transfer in fibrous insulations with the relevant parameters inferred from thermal conductivity measurements at cryogenic temperatures in a vacuum. The specific extinction coefficient for radiation heat transfer was obtained from high-temperature steady-state thermal measurements with large temperature gradients maintained across the sample thickness in a vacuum. Standard gas conduction modeling was used in the heat transfer formulation. This heat transfer modeling methodology was applied to silica, two types of alumina, and a zirconia-based fibrous insulation, and to a variation of opacified fibrous insulation (OFI). OFI is a class of insulations manufactured by embedding efficient ceramic opacifiers in various unbonded fibrous insulations to significantly attenuate the radiation component of heat transfer. The heat transfer modeling methodology was validated by comparison with more rigorous analytical solutions and with standard thermal conductivity measurements. The validated heat transfer model is applicable to various densities of these high-porosity insulations as long as the fiber properties are the same (index of refraction, size distribution, orientation, and length). Furthermore, the heat transfer data for these insulations can be obtained at any static pressure in any working gas environment without the need to perform tests in various gases at various pressures

Study of fuel systems for LH2-fueled subsonic transport aircraft, volume 1

Several engine concepts examined to determine a preferred design which most effectively exploits the characteristics of hydrogen fuel in aircraft tanks received major emphasis. Many candidate designs of tank structure and cryogenic insulation systems were evaluated. Designs of all major elements of the aircraft fuel system including pumps, lines, valves, regulators, and heat exchangers received attention. Selected designs of boost pumps to be mounted in the LH2 tanks, and of a high pressure pump to be mounted on the engine were defined. A final design of LH2-fueled transport aircraft was established which incorporates a preferred design of fuel system. That aircraft was then compared with a conventionally fueled counterpart designed to equivalent technology standards

Study of fuel systems for LH2-fueled subsonic transport aircraft, volume 2

For abstract, see N78-31085

Ultrafiltration for acute decompensated cardiac failure: A systematic review and meta-analysis

Background Ultrafiltration is a method used to achieve diuresis in acute decompensated heart failure (ADHF) when there is diuretic resistance, but its efficacy in other settings is unclear. We therefore conducted a systematic review and meta-analysis to evaluate the use of ultrafiltration in ADHF. Methods We searched MEDLINE and EMBASE for studies that evaluated outcomes following filtration compared to diuretic therapy in ADHF. The outcomes of interest were body weight change, change in renal function, length of stay, frequency of rehospitalization, mortality and dependence on dialysis. We performed random effects meta-analyses to pool studies that evaluated the desired outcomes and assessed statistical heterogeneity using the I2 statistic. Results A total of 10 trials with 857 participants (mean age 68 years, 71% male) compared filtration to usual diuretic care in ADHF. Nine studies evaluated weight change following filtration and the pooled results suggest a decline in mean body weight − 1.8; 95% CI, − 4.68 to 0.97 kg. Pooled results showed no difference between the filtration and diuretic group in change in creatinine or estimated glomerular filtration rate. The pooled results suggest longer hospital stay with filtration (mean difference, 3.70; 95% CI, − 3.39 to 10.80 days) and a reduction in heart failure hospitalization (RR, 0.71; 95% CI, 0.51–1.00) and all-cause rehospitalization (RR, 0.89; 95% CI, 0.43–1.86) compared to the diuretic group. Filtration was associated with a non-significant greater risk of death compared to diuretic use (RR, 1.08; 95% CI, 0.77–1.52)

FFNSL: feed-forward neural-symbolic learner

Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFNSL), that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data. We demonstrate the generality of FFNSL on four neural-symbolic classification problems, where different pre-trained neural network models and logic-based machine learning systems are integrated to learn interpretable knowledge from sequences of images. We evaluate the robustness of our framework by using images subject to distributional shifts, for which the pre-trained neural networks may predict incorrectly and with high confidence. We analyse the impact that these shifts have on the accuracy of the learned knowledge and run-time performance, comparing FFNSL to tree-based and pure neural approaches. Our experimental results show that FFNSL outperforms the baselines by learning more accurate and interpretable knowledge with fewer examples

The Social Work Online Team Training (SWOTT) toolkit: embedding team-based peer learning in continuous professional development

Continuous professional development (CPD) underpins safe, effec-tive practice by ensuring that social workers acquire and sustain up- to-date knowledge and skills. Additionally, CPD is critical to theoreti-cally rooted, evidence-informed decision-making and intervention. Despite the reported benefits, there are many barriers such as high caseloads and the time required to participate. This paper presents the findings from a proof-of-concept study which piloted a new model for CPD: the Social Work Online Team Training (SWOTT) toolkit. Each themed toolkit incorporates research evidence and/or new theoretical frameworks and is built upon a team-based, peer learning approach. Toolkits have two components: an online module and peer group supervision using a complex case study. The pilot and evaluation integrated two data collection workstreams: a pre- intervention survey and a post-intervention survey; and interviews. Participants reported that the CPD was relevant, accessible, enabling them to refresh knowledge of core theory and acquire new theore-tical and evidence-informed knowledge. The toolkit design facilitated deep learning as participants used the online training to critically discuss the complex case study using peer reflection. Overall, findings demonstrated the value of shared learning experiences through the combined modes of learning (online/in-person) resulting in evi-dence-informed CPD with real-world relevance to practice contexts