@article{vinod_gore_liu_fang_2023, title={Experimental characterization of ammonia, methane, and gasoline fuel mixtures in small scale spark ignited engines}, volume={16}, ISSN={["2666-352X"]}, url={https://doi.org/10.1016/j.jaecs.2023.100205}, DOI={10.1016/j.jaecs.2023.100205}, abstractNote={In this study, gaseous anhydrous ammonia is blended with fuels like gasoline and methane and tested in an instrumented, low-technology single cylinder carbureted engine. In-cylinder pressure and emissions are monitored with the various mixtures and their performance is then compared with pure gasoline. With the addition of ammonia, the stability of combustion inside the combustion chamber was affected. But with the addition of a combustion modifier, the overall variability was reduced. At higher substitutions of ammonia, Initial results show an increase in indicated thermal efficiency of the engine. There is also a substantial decrease in the heat release rate (HRR) of the engine when substituting gasoline with ammonia. With the addition of methane, the change in the fuel reactivity helped improve HRR. Increasing ammonia substitution also resulted in an increase in indicated efficiency when compared to pure gasoline by approximately 12% with 50% substitution of ammonia in gasoline. Adding ammonia to the fuel mixtures also showed an initial reduction in unburnt hydrocarbon emission, followed by a sudden increase with further increasing concentration, suggesting incomplete combustion of the fuel mixture. The addition of methane with gasoline also showed a reduction in overall NOx emissions. Furthermore, methane was also tested as the main fuel with ammonia substitution of up to 50%. This ammonia and methane blend also showed comparable results to the gasoline, ammonia, and methane blends tested. From the emissions data, the catalyzing effects of ammonia were also seen with some cases showing varying trends with increasing ammonia substitution. Results from this study can be used to design small-scale engine based power generation systems that need very little modifications to accept ammonia based mixed fuels. Furthermore, this study lays the groundwork for using fuels blends with methane sourced using carbon neutral technologies and ammonia to power engine based systems.}, journal={APPLICATIONS IN ENERGY AND COMBUSTION SCIENCE}, author={Vinod, Kaushik Nonavinakere and Gore, Matt and Liu, Hanzhang and Fang, Tiegang}, year={2023}, month={Dec} }
 @article{liu_rush_baron_2023, title={Rigorous State Evolution Analysis for Approximate Message Passing With Side Information}, volume={69}, ISSN={["1557-9654"]}, url={https://doi.org/10.1109/TIT.2022.3220046}, DOI={10.1109/TIT.2022.3220046}, abstractNote={A common goal in many research areas is to reconstruct an unknown signal $\mathbf {x}$ from noisy linear measurements. Approximate message passing (AMP) is a class of low-complexity algorithms that can be used for efficiently solving such high-dimensional regression tasks. Often, it is the case that side information (SI) is available during reconstruction, for example in online learning applications. For this reason, a novel algorithmic framework that incorporates SI into AMP, referred to as approximate message passing with side information (AMP-SI), has been recently introduced. In this work, we provide rigorous performance guarantees for AMP-SI when there are statistical dependencies between the signal and SI pairs and the entries of the measurement matrix are independent and identically distributed (i.i.d.) Gaussian. We also allow for statistical dependencies within the elements of the signal itself, by considering a flexible AMP-SI framework incorporating both separable and non-separable denoisers. The AMP-SI performance is shown to be provably tracked by a scalar iteration referred to as state evolution (SE). Moreover, we provide numerical examples that demonstrate empirically that the SE can predict the AMP-SI mean square error accurately.}, number={6}, journal={IEEE TRANSACTIONS ON INFORMATION THEORY}, author={Liu, Hangjin and Rush, Cynthia and Baron, Dror}, year={2023}, month={Jun}, pages={3989–4013} }