Surrogate Model Development for Fuels for Advanced Combustion Engines

2011 ◽  
Vol 25 (4) ◽  
pp. 1474-1484 ◽  
Author(s):  
K. Anand ◽  
Y. Ra ◽  
R. D. Reitz ◽  
B. Bunting
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Combustion Engines ◽  
Advanced Combustion

Surrogate Model Development for Naturally Ventilated Office Buildings

2020 ◽  
Author(s):  
Marcelo Salles Olinger ◽  
Ana Paula Melo ◽  
Letícia Oliveira Neves ◽  
Roberto Lamberts
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Office Buildings

scholarly journals Unique structure of active platinum-bismuth site for oxidation of carbon monoxide

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bing Nan ◽  
Qiang Fu ◽  
Jing Yu ◽  
Miao Shu ◽  
Lu-Lu Zhou ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Low Temperature ◽  
Co Oxidation ◽  
Density Functional ◽  
Technology Development ◽  
Co Adsorption ◽  
Combustion Engines ◽  
Advanced Combustion ◽  
Surface Metal ◽  
Bismuth Cluster

AbstractAs the technology development, the future advanced combustion engines must be designed to perform at a low temperature. Thus, it is a great challenge to synthesize high active and stable catalysts to resolve exhaust below 100 °C. Here, we report that bismuth as a dopant is added to form platinum-bismuth cluster on silica for CO oxidation. The highly reducible oxygen species provided by surface metal-oxide (M-O) interface could be activated by CO at low temperature (~50 °C) with a high CO2 production rate of 487 μmolCO2·gPt−1·s−1 at 110 °C. Experiment data combined with density functional calculation (DFT) results demonstrate that Pt cluster with surface Pt−O−Bi structure is the active site for CO oxidation via providing moderate CO adsorption and activating CO molecules with electron transformation between platinum atom and carbon monoxide. These findings provide a unique and general approach towards design of potential excellent performance catalysts for redox reaction.

Simulation of n-Heptane and Surrogate Fuels for Advanced Combustion Engines (FACE) in a Single-Cylinder Compression Ignition Engine

2013 ◽  
Author(s):  
Joseph Taglialegami ◽  
Gregory Bogin ◽  
Eric Osecky ◽  
Anthony M. Dean
Keyword(s):  
Diesel Engine ◽  
Physical Properties ◽  
Heat Release ◽  
Chemical Mechanism ◽  
Computational Time ◽  
Combustion Engines ◽  
Single Cylinder ◽  
Advanced Combustion ◽  
The Face ◽  
Single Chemical

A CFD model of a HATZ diesel engine was developed for the purpose of simulating low temperature combustion (LTC) of surrogate diesel fuels for the Fuels for Advanced Combustion Engines (FACE). Initial validation of the model was performed using n-heptane data from a single cylinder HATZ diesel engine. Simulations were run with both a detailed n-heptane mechanism and several reduced mechanisms to determine the suitability of using a reduced mechanism to capture the main ignition characteristics and emissions. It was found that a 173 species n-heptane mechanism predicts start of combustion (SOC) within 0.5 crank angle degrees of the detailed 561 species mechanism. The 173 species mechanism required 27 hours of computational time to reach the end of the simulation whereas the 561 species detailed mechanism required 41 hours under the same conditions. Two additional reduced mechanisms, containing 85 and 35 species, were provided reasonable accuracy with a computational time of 8 hours and 2 hours, respectively. Due to the varying physical and chemical properties of the FACE surrogates, a sensitivity analysis of the effects of the physical properties was conducted by changing the n-heptane physical properties to those of n-hexadecane while keeping the chemistry the same. As expected, when the fuel properties of n-hexadecane (which is less volatile than n-heptane) were used with the n-heptane kinetics, SOC was delayed and the net heat release rate was reduced. The FACE fuels were developed to fulfill the need for research grade fuels that are able to represent common refinery stream fuels. Since the FACE fuels consist of hundreds of fuel components, it is not feasible to model the actual FACE fuels in a full-scale engine model. An alternative is to develop surrogates consisting of relatively few species for which detailed mechanisms are available. Even then this mechanism would need to be reduced to make the computation practical. For this work, a detailed diesel surrogate mechanism was reduced from 4016 species to 1046 species to match the characteristics for FACE fuels 1, 3, 5, 8, and 9. The surrogates only contain 4–7 species. Using the single chemical mechanism to represent five surrogate FACE fuels, it was found that ∼200°C of air preheat was required to achieve autoignition in the HATZ model compared to the 130°C of air preheat required experimentally. Initial runs have found that there were similar trends in SOC and heat release between the FACE fuel surrogate experiments and simulations for the respective fuels. Future work will require improvements on the single chemical mechanism to represent the five surrogate FACE fuels.

Data-Driven Model Development and Feedback Control Design for PZT Bimorph Actuators

2017 ◽  
Author(s):  
Nikolas Bravo ◽  
Ralph C. Smith ◽  
John Crews
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Micro Air Vehicles ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
High Fidelity ◽  
Actuator Dynamics ◽  
Rate Dependent ◽  
Homogenized Energy Model

In the paper, we discuss the development of a high-fidelity and surrogate model for a PZT bimorph used as an actuator for micro-air vehicles including Robobee. The models must quantify the nonlinear, hysteretic, and rate-dependent behavior inherent to PZT in dynamic operating regimes. The actuator dynamics are initially modeled using the homogenized energy model (HEM) framework. This provides a comprehensive high-fidelity model, which can be inverted and implemented in real time for certain control regimes. To improve efficiency, we additionally discuss the development of data-driven models and focus on the implementation of a surrogate model based on a dynamic mode decomposition (DMD). Finally, we detail the design and implementation of a PI controller on the surrogate and high-fidelity models.

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