Microscopic characteristics of biodiesel – Graphene oxide nanoparticle blends and their Utilisation in a compression ignition engine

Use of nano-additives in biofuels is an important research and development topic for achieving optimum engine performance with reduced emissions. In this study, rice bran oil was converted into biodiesel and graphene oxide (GO) nanoparticles were infused into biodiesel-diesel blends. Two blends containing (i) 5% biodiesel, 95% diesel and 30 ppm GO (B5D95GO30) and (ii) 15% biodiesel, 85% diesel and 30 ppm GO (B15D85GO30) were prepared. The fuel properties like heating value, kinematic viscosity, cetane number, etc. of the nanoadditives–biodiesel-diesel blends (NBDB) were measured. Effects of injection timing (IT) on the performance, combustion and emission characteristics were studied. It was observed that both B15D85GO30 and B5D95GO30 blends at IT23° gave up to 13.5% reduction in specific fuel consumption. Compared to diesel, the brake thermal efficiency was increased by 7.62% for B15D85GO30 at IT23° and IT25°. An increase in IT from 23° to 25° deteriorated the indicated thermal efficiency by 6.68% for B15D85GO30. At maximum load condition, the peak heat release rates of NBDB were found to be lower than the pure diesel at both IT. The CO, CO2 & NOx emissions were reduced by 2–8%. The study concluded that B15D85GO30 at IT23° gave optimum results in terms of performance, combustion and emission characteristics

Microstructural Stresses and Strains Associated with Trabecular Bone Microdamage

Bone is a composite material consisting of hydroxyapatite crystals deposited in an oriented manner on a collagen backbone. The arrangement of the mineral and organic phases provides bone tissue with the appropriate strength, stiffness, and fracture resistance properties required to protect vital internal organs and maintain the shape of the body. A remarkable feature of bone is the ability to alter its properties and geometry in response to changes in the mechanical environment. However, in cases of metabolic bone diseases or aging, bone can no longer successfully adapt to its environment, increasing its fragility. To elucidate the mechanisms of bone microdamage, this research project developed a specimen-specific approach that integrated 3D imaging, histological damage labeling, image registration, and image-based finite element analysis to correlate microdamage events with microstructural stresses and strains under compressive loading conditions. By applying this novel method to different ages of bovine and human bone, we have shown that the local mechanical environment at microdamage initiation is altered with age. We have also shown that formation of microdamage is time-dependent and may have implications in age-related microdamage progression with cyclic and/or sustained static loading. The work presented in this dissertation is significant because it improved our understanding of trabecular bone microdamage initiation and unlocked exciting future research directions that may contribute to the development of therapies for fragility diseases such as osteoporosis.Ph.D.Committee Chair: Robert E. Guldberg; Committee Member: Christopher S. Lynch; Committee Member: Jiamin Qu; Committee Member: Karl J. Jepsen; Committee Member: Oskar Skrinja

Current practices in corrosion, surface characterization, and nickel leach testing of cardiovascular metallic implants

In an effort to better understand current test practices and improve nonclinical testing of cardiovascular metallic implants, the Food and Drug Administration (FDA) held a public workshop on Cardiovascular Metallic Implants: corrosion, surface characterization, and nickel leaching. The following topics were discussed: (1) methods used for corrosion assessments, surface characterization techniques, and nickel leach testing of metallic cardiovascular implant devices, (2) the limitations of each of these in vitro tests in predicting in vivo performance, (3) the need, utility, and circumstances when each test should be considered, and (4) the potential testing paradigms, including acceptance criteria for each test. In addition to the above topics, best practices for these various tests were discussed, and knowledge gaps were identified. Prior to the workshop, discussants had the option to provide feedback and information on issues relating to each of the topics via a voluntary preworkshop assignment. During the workshop, the pooled responses were presented and a panel of experts discussed the results. This article summarizes the proceedings of this workshop and background information provided by workshop participants

Boosting MMSE Receivers Using AdaBoost

MIMO systems with several antennas at the transmitter and receiver have the potential to enable high throughputs. One of the challenges in realizing this potential is the design of receivers that scale in terms of computation and performance. Towards this end, in this paper, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in machine learning. We call our receiver boosted MMSE (B-MMSE) receiver and we study its performance via simulations. The channel matrix is estimated from the pilot using the MMSE technique. We find that for a 4 x 4 system with a Rayleigh fading channel, using BPSK at a bit error rate (BER) of 10(-2), our receiver using the estimated channel matrix needs 2 dB less power than the plain MMSE receiver based on the estimated channel matrix. On an average, excluding the training phase, the receiver complexity equals 1.25 linear receivers. Thus the B-MMSE receiver is slightly more complex than the MMSE receiver and substantially less complex than the maximum-likelihood receiver. We also show that the coded BER performance with an outer rate-3/4 Turbo-code has a gain of 5 dB at 10(-4) coded BER when the demodulated output from the proposed method is used in place of plain MMSE detection with MMSE estimated channel

Large-MIMO Receiver Based on Linear Regression of MMSE Residual

Multiple input multiple output (MIMO) systems with large number of antennas have been gaining wide attention as they enable very high throughputs. A major impediment is the complexity at the receiver needed to detect the transmitted data. To this end we propose a new receiver, called LRR (Linear Regression of MMSE Residual), which improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel), to find the linear regression parameters. The proposed receiver is suitable for applications where the channel remains constant for a long period (slowfading channels) and performs quite well: at a bit error rate (BER) of 10-3, the SNR gain over MMSE receiver is about 7 dB for a 16 × 16 system; for a 64 × 64 system the gain is about 8.5 dB. For large coherence time, the complexity order of the LRR receiver is the same as that of the MMSE receiver, and in simulations we find that it needs about 4 times as many floating point operations. We also show that further gain of about 4 dB is obtained by local search around the estimate given by the LRR receiver

Large-MIMO Receiver based on Linear Regression of MMSE Residual

Multiple input multiple output (MIMO) systems with large number of antennas have been gaining wide attention as they enable very high throughputs. A major impediment is the complexity at the receiver needed to detect the transmitted data. To this end we propose a new receiver, called LRR (Linear Regression of MMSE Residual), which improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel), to find the linear regression parameters. The proposed receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs quite well: at a bit error rate (BER) of 10(-3), the SNR gain over MMSE receiver is about 7 dB for a 16 x 16 system; for a 64 x 64 system the gain is about 8.5 dB. For large coherence time, the complexity order of the LRR receiver is the same as that of the MMSE receiver, and in simulations we find that it needs about 4 times as many floating point operations. We also show that further gain of about 4 dB is obtained by local search around the estimate given by the LRR receiver