Effect of Embedded Length and Bar Diameter of Reinforcement on Bond Strength Behavior of High Strength Concrete Subjected to Elevated Temperatures

In case of accidental fire, sabotages reinforced concrete structures get exposed to elevated temperatures, which results in deterioration of its mechanical strength. The deterioration in concrete is due to, its inhomogeneous volume change of concrete ingredients, generation of vapour pressure and decomposition of cement hydration products. Hence, it is significant to study the bond strength between concrete and reinforcing steel.  In the present investigation a study has been carried out on bond strength between high strength concrete and reinforcing steel subjected to elevated temperatures. In order to find the variation in bond strength, various parameters were considered such as bar diameters, embedded length and different temperature levels with 1 hr retention period.  In this investigation 12 mm, 16 mm and 20 mm diameter with two different embedded lengths 150 mm and 300 mm were adopted.  Specimens were exposed to three different temperature levels 200°C, 400°C, 600°C with retention period of 1hr. The experimental results concludes that, under elevated temperatures, embedded length does not contribute more to change in bond strength, but bar diameter and temperature plays important role in change in bond strength and, which is also associated with statistical analysis. From experimental study an empirical formula is proposed to predict the bond strength by considering elevated temperatures, bar diameter and embedded length. Keywords: Bond strength, Bar diameter, Embedded length, Elevated Temperature

Using System Analysis Modeling Language (SAML) for validating the critical aerospace model.

System Analysis Modeling Language (SAML) is a formal language which helps in expressing and analyzing the qualitative and quantitative aspects of the software as well as hardware models. This can be used in model-based safety analysis (MBSA) which provides the means of identifying, localizing and analyzing hazards in these real-time Safety-Critical Systems. This paper describes the work carried out in the organization to validate the complex and critical Mode-Transition Logic (MTL) in Automated Flight Control System (AFCS) being developed in the organization. The Mode-Transition Logic (MTL) of the AFCS system is re-modeled using SAML and further analyzed with model checkers such as PRISM and NuSMV, for generation of counter-examples. The counter examples helped in mapping the safety scenarios along the AFCS requirements. These counter examples also helped in generating the fault model and analyzing the system logic for fault tolerance. Using NUSMV, MTL the failure scenarios were generated and the allowed transitions were studied. Failure management analysis report is generated and mapped as an artefact for the certification. For the illustration of the proposed approach, a suitable framework viz. Verification Environment for Safety-Critical Systems (VECS) is used to validate the utility of Mode-Transition Logic (MTL) in Automated Flight Control System (AFCS). The critical operations and complex functions were analyzed for contingency situations and provide means in significantly enhancing the safe operation of the Safety-Critical System. The mapping of the model safety using this approach will provide compliance with Civil Aerospace Standard DO-178C and DO-331 using Model-Based Design

Statistical Machine Learning Methodology for Individualized Treatment Rule Estimation in Precision Medicine

Precision medicine aims to deliver optimal, individualized treatments for patients by accounting for their unique characteristics. With a foundation in reinforcement learning, decision theory, and causal inference, the field of precision medicine has seen many advancements in recent years. Significant focus has been placed on creating algorithms to estimate individualized treatment rules (ITRs), which map from patient covariates to the space of available treatments with the goal of maximizing patient outcome. In Chapter 1, we extend ITR estimation methodology in the scenario where variance of the outcome is heterogeneous with respect to treatment and covariates. Accordingly, we propose Stabilized Direct Learning (SD-Learning), which utilizes heteroscedasticity in the error term through a residual reweighting framework that models residual variance via flexible machine learning algorithms such as XGBoost and random forests. We also develop an internal cross-validation scheme which determines the best residual model among competing models. Further, we extend this methodology to multi-arm treatment scenarios. In Chapter 2, we develop ITR estimation methodology for situations where clinical decision-making involves balancing multiple outcomes of interest. Our proposed framework estimates an ITR which maximizes a combination of the multiple clinical outcomes, accounting for the fact that patients may ascribe importance to outcomes differently (utility heterogeneity). This approach employs inverse reinforcement learning (IRL) techniques through an expert-augmentation solution, whereby physicians provide input to guide the utility estimation and ITR learning processes. In Chapter 3, we apply an end-to-end precision medicine workflow to novel data from older adults with Type 1 Diabetes in order to understand the heterogeneous treatment effects of continuous glucose monitoring (CGM) and develop an interpretable ITR to reveal patients for which CGM confers a major safety benefit. The results from this analysis elucidate the demographic and clinical markers which moderate CGM's success, provide the basis for using diagnostic CGM to inform therapeutic CGM decisions, and serve to augment clinical decision-making. Finally, in Chapter 4, as a future research direction, we propose a deep autoencoder framework which simultaneously performs feature selection and ITR optimization, contributing to methodology built for direct consumption of unstructured, high-dimensional data in the precision medicine pipeline.Doctor of Philosoph

Impact of the Flame-Holder Heat-Transfer Characteristics on the Onset of Combustion Instability

In this article, we investigate the impact of heat transfer between the flame and the flame-holder on the dynamic stability characteristics of a 50-kW backward-facing step combustor. We conducted a series of tests where two backward step blocks were used, made of ceramic and stainless steel, whose thermal conductivities are 1.06 and 12 W/m/K, respectively. Stability characteristics of the two flame-holder materials were examined using measurements of the dynamic pressure and flame chemiluminescence over a range of operating conditions. Results show that with the ceramic flameholder, the onset of instability is significantly delayed in time and, for certain operating conditions, disappears altogether, whereas with the higher conductivity material, the combustor becomes increasingly unstable over a range of operating conditions. We explain these trends using the heat flux through the flame-holder and the change in the burning velocity near the step wall. Results suggest a potential approach using low-thermal-conductivity material near the flame-holder as passive dynamics suppression methods.King Abdullah University of Science and Technology (Grant KUS-110-010-01

Comparative Analysis of Phase Noise for different configurations of Bragg lattice for an Atomic Gravimeter with Bose-Einstein Condensate

We perform a comparative study of the phase noise induced in the lasers used for Bragg diffraction in a Bose-Einstein condensate-based quantum gravimeter where the Bragg beams are generated using two different configurations. In one of the configurations, the Bragg beams that form the moving optical lattice are generated using two different acousto-optic modulators. In the second configuration, the Bragg beams are generated using a single acousto-optic modulator carrying two phase-locked frequencies. The second configuration shows a suppression of phase noise by a factor of 4.7 times in the frequency band upto 10 $kHz$, the primary source of noise, which is the background acoustic noise picked up by optical components and the optical table. We report a sensitivity of 99.7 $\mu Gal/\sqrt Hz$ for an interferometric time of 10 $ms$.Comment: 8 pages, 6 figure

Electromagnetically Induced Transparency (EIT) aided cooling of strontium atoms

The presence of ultra-narrow inter-combination spectroscopic lines in alkaline earth elements places them as promising candidates for optical atomic clocks, quantum computation, and for probing fundmental physics. Doppler cooling of these atoms is typically achieved through two subsequent stages: the initial cooling is on the 1s0-1p1 transition followed by cooling using the narrow-line 1s0-3p1 transition. However, due to significantly lower linewidth of the second stage cooling transition, efficient transfer of atoms into the second stage becomes technically challenging. The velocity distribution of the atoms after the first stage of cooling is too broad for atoms to be captured efficiently in the second stage cooling. As a result, the capture efficiency of atoms into the second stage Magneto-Optical Trap is low, even if the linewidth of the second stage cooling laser is artificially broadened.Comment: 7 pages, 3 figure