Process structure-based recurrent neural network modeling for predictive control: A comparative study
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In support of the development of robust combustion models, the present study describes experimental and computational results on the non-premixed counterflow ignition of all four butanol isomers against heated air for pressures of 1–4 atm, pressure-weighted strain rates of 200–400 s -1 , and fuel molar fractions in nitrogen-diluted mixtures of 0.05–0.25. Comparison of the parametric effects of varied pressure, strain rate, and fuel loading among the isomers facilitates a comprehensive evaluation of the effect of varied structural isomerism on transport-affected ignition. The experimental findings are simulated using isomer-specific skeletal mechanisms developed from two comprehensive butanol models available in the literature, and are used to validate and assess the performance of these models. Comparison of the experimental and computational results reveal that while both models largely capture the trends in ignition temperature as functions of pressure-weighted strain rate, fuel loading, and pressure, for all isomers both models over-predict the experimental data to an appreciable extent. In addition, neither model captures the experimentally-observed ignition temperature rankings, with both models predicting a large spread among n-/iso-/sec-butanol which does not appear in the experimental results. Sensitivity and path analyses reveal that the butene isomers play a significant role in determining the ignition temperatures of the butanol isomers in both models, with the relative branching ratios likely accounting for the ignition temperature rankings observed using each model. It is observed that the reactivity of the butene isomers varies appreciably between the two butanol models, which may account for some of the variability in predictions between the two models. Moreover, effects of transport properties and their uncertainties on ignition temperature predictions are discussed.
Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. Here, this study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. In conclusion, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.
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The physicochemical properties of a (nano)mineral are strongly affected by its formation processes, and thus, may indicate the (nano)mineral’s formation environment and mechanism. This correlation, although relevant to a myriad of geological, environmental, and material-science processes, has not yet been fully appreciated and systematically explored. In this study, using the Zn–S system, we demonstrate that biological and abiotic processes at similar experimental conditions can produce distinctive particle size, morphology, and crystal structure in the formed ZnS. Specifically, bacterial sulfate reduction led to the formation of highly-defective nanocrystals of mixed sphalerite and wurtzite in a range of ~4–12 nm. By comparison, the abiotic procedures of titration- or diffusion-controlled precipitation resulted in the formation of polycrystalline aggregates that contained randomly-oriented, ultrafine crystals below ~2–3 nm. The poor crystallinity in the abiogenic samples, regardless of the sulfide addition rates, reveals an overall nucleation-dominated, crystal growth-restricted pathway for the formation of ZnS from low-temperature aqueous solutions. The difficulty in the ZnS crystallization likely stems from the intrinsic surface instability of the ZnS growth units (i.e., in the form of nanoclusters) resulting from the dipole–dipole interactions of the unit with surrounding water molecules. In the biogenic samples, the ZnS crystallinity was significantly improved, indicating that the presence of bacterial metabolites somehow promoted the crystallization process. With evidence for the enlarged {1 1 1} planes in the biogenic nanocrystals, we attribute this enhancement mainly to the selective interaction of the bacterial metabolites with polar faces of the ZnS growth units, which might have effectively screened the dipole moments in the growth units and enabled their crystallographic assembly. By revealing the intrinsic difficulty and specific pathways for the ZnS crystallization, we also present viable explanations for the exclusive occurrence of structural defects in {1 1 1} planes of the biogenic nanocrystals. The findings of our study provide insight into the origin and fate of metal sulfide nanoparticles in the environment, and have implications for biomineralization, bioremediation of metal-contaminated sites, and bacterial production of functional nanomaterials.
The potential safety, productivity, and energy benefits of automated vehicles have driven a surge of research interest in their algorithms. Even within single-lane driving, control engineers now have a profusion of approaches available to them. Algorithm classes include classical controllers, receding horizon controllers, and constrained eco-driving formulae based on Pontryagin’s Minimum Principle. Differing connectivity architectures and collaboration levels further differentiate algorithms from one another. This study evaluated six controllers in two drive cycle-based scenarios using an electric powertrain model for energy analysis. Individual-vehicle and string performance were examined, including string stability and length. Algorithms with greater access to information generally performed best. Although collaboration affected energy use only slightly, it made a greater impact on string length.
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The choice of driving a heat pump with an electrically$-$or a thermally-driven engine is a vexing question complicated by the carbon footprint and environmental impact of using electricity versus natural gas (or waste heat) as the main driver for the respective engines. The amount of useful work generated by these two distinct engines is the focal point of this paper, which addresses a key question: which engine presents a better choice for a given heat pumping application within the constraints of energy and environmental stewardship? Extensive use of energy, exergy, and availability analysis is necessary to quantify the useful work and to examine the issue holistically for both types of engines. The methodology explains why the output of work from these two distinct engines to satisfy a given load is vastly different, a direct consequence of their inherent Irreversibility. Thermodynamic consistency is guaranteed by satisfaction of the First and Second Laws applied to closed systems and their subsystems. The general conclusion is that thermally-driven engines are not industrious converters of heat to mechanical work.
Guidelines from the American Society of Clinical Oncology and Cancer Care Ontario recommend brachytherapy boost for patients with intermediate-risk or high-risk prostate cancer. SABR is an emerging technique for prostate cancer, but its use in high-risk disease is limited. Efficacy, toxic effects, and quality of life (QoL) were compared in patients treated on 2 prospective protocols that used SABR boost or magnetic resonance–guided high-dose-rate brachytherapy (HDR-BT) boost with 6 to 18 months of androgen deprivation therapy (ADT).
The high cooling rates in laser directed energy deposition (DED-LB) of alloys lead to substantial amounts of solute trapping as solute atoms cannot diffuse away from the solid/liquid interface before it advances. In some concentrated alloys, this results in supersaturated solid phases that form nanoscale hierarchical microstructures when the solute atoms precipitate out during reheating from subsequent laser passes. We choose the Iron-Copper (Fe-Cu) binary alloy as model system as it is chemically homogeneous in the liquid phase and has negligible solid solubility at room temperature. Two alloys with nominal compositions in atomic (at.) %, Fe67Cu33 and Fe55Cu45, were manufactured using DED-LB. Scanning transmission electron microscopy (STEM), energy dispersive spectroscopy (EDS) and wavelength dispersive spectroscopy (WDS) were used to characterize the nanostructures and heterogeneous chemical compositions. A non-equilibrium solute partitioning model was used to compute the supersaturated chemistries of the constituent phases and validated with experimentally measured compositions. The measured phase compositions of the two alloys were very similar, at roughly 12 at.% Cu and 4 at.% Fe in the α(bcc)-Fe and ε(fcc)-Cu phases respectively, despite having different processing parameters and mechanical behavior. This indicates that the total thermal history, that depends on both the processing parameters and laser scan pattern, plays a stronger role on the final microstructure evolution than just the initial quantity of trapped solute. In addition, we find that current non-equilibrium solute partitioning models applied on the continuum scale fall short of predicting accurate quantitative phase compositions in concentrated alloys, although the qualitative trends are captured correctly.
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