Oluwayemisi O. Oluwole

Obtaining accurate solutions using reduced chemical kinetic models: a new model reduction method for models rigorously validated over ranges

Reduced chemical kinetics models are often used to lessen the computational cost of reacting flow simulations. However, because a reduced model is usually validated only for a nominal set of reaction conditions, unknown errors are introduced if the reduced model is used at new reaction conditions. In a previous paper, we introduced a method that, given a reduced model as input, identifies a rigorous range over which the model remains valid. However, this procedure is backwards: in most cases one starts with a known range of reaction conditions and one desires a reduced model that can be used over this range. Here we present the first automatic procedure that constructs a reduced chemistry model that is guaranteed to be valid everywhere in any user-specified range. The rigorousness of the model reduction method enables rigorous statements about the difference between the solution obtained using the reduced model and the solution that would have been obtained using the original full-chemistry model. By appropriate choice of error tolerances, the reduced-model solution can be made arbitrarily close to the full-model solution. This is demonstrated with adaptive chemistry simulations of one- and two-dimensional steady state laminar methane/air flames. As guaranteed by the error control procedure, the solutions of the reduced models are just as accurate as those obtained using the full-chemistry model, but they require significantly less CPU time.

Portal-based knowledge environment for collaborative science.

The Knowledge Environment for Collaborative Science (KnECS) is an open‐source informatics toolkit designed to enable knowledge Grids that interconnect science communities, unique facilities, data, and tools. KnECS features a Web portal with team and data collaboration tools, lightweight federation of data, provenance tracking, and multi‐level support for application integration. We identify the capabilities of KnECS and discuss extensions from the Collaboratory for Multi‐Scale Chemical Sciences (CMCS) which enable diverse combustion science communities to create and share verified, documented data sets and reference data, thereby demonstrating new methods of community interaction and data interoperability required by systems science approaches. Finally, we summarize the challenges we encountered and foresee for knowledge environments. Copyright

An algorithm to estimate aircraft cruise black carbon emissions for use in developing a cruise emissions inventory

To provide accurate input parameters to the large-scale global climate simulation models, an algorithm was developed to estimate the black carbon (BC) mass emission index for engines in the commercial fleet at cruise. Using a high-dimensional model representation (HDMR) global sensitivity analysis, relevant engine specification/operation parameters were ranked, and the most important parameters were selected. Simple algebraic formulas were then constructed based on those important parameters. The algorithm takes the cruise power (alternatively, fuel flow rate), altitude, and Mach number as inputs, and calculates BC emission index for a given engine/airframe combination using the engine property parameters, such as the smoke number, available in the International Civil Aviation Organization (ICAO) engine certification databank. The algorithm can be interfaced with state-of-the-art aircraft emissions inventory development tools, and will greatly improve the global climate simulations that currently use a single fleet average value for all airplanes. Implications An algorithm to estimate the cruise condition black carbon emission index for commercial aircraft engines was developed. Using the ICAO certification data, the algorithm can evaluate the black carbon emission at given cruise altitude and speed.

Updating Our Understanding of JP-10 Decomposition Chemistry: A Detailed JP-10 Combustion Mechanism Constructed Using RMG - an Automatic Reaction Mechanism Generator

We are applying Reaction Mechanism Generator (RMG) to detailed and comprehensive characterization of JP-10 combustion chemistry. JP-10 is a large synthetic fuel with complex high temperature decomposition chemistry for which detailed characterization has been considered intractable; and RMG is a computer program that we developed for automatic construction of predictive kinetic models. In this paper, we present initial results from applying RMG to JP-10 combustion. After considering over 25,000 possible species and more than 1 million possible reactions, we have developed a highly detailed JP-10 combustion mechanism that currently consists of 317 chemical species and 7,715 elementary reactions. Comparisons against existing mechanisms and experimental data in published literature reveal that this RMG-constructed mechanism establishes a new state of the art in JP-10 combustion modeling: ignition delay predictions accurately reproduce experimental observations (to our knowledge, the first detailed mechanism to accomplish this); and our mechanism provides the first detailed insight into the high temperature initial decomposition chemistry of JP-10 in the presence of oxygen, particularly down to C5 hydrocarbons. We are also developing transport property estimates for all species in the mechanism. The final result of this effort will be an experimentally validated comprehensive JP-10 combustion mechanism containing all parameters necessary for application in reacting flow simulations.

Development of the RIOT web service and information technologies to enable mechanism reduction for HCCI simulations

New approaches are being explored to facilitate multidisciplinary collaborative research of Homogeneous Charge Compression Ignition (HCCI) combustion processes. In this paper, collaborative sharing of the Range Identification and Optimization Toolkit (RIOT) and related data and models is discussed. RIOT is a developmental approach to reduce the computational of detailed chemical kinetic mechanisms, enabling their use in modeling kinetically controlled combustion applications such as HCCI. These approaches are being developed and piloted as a part of the Collaboratory for Multiscale Chemical Sciences (CMCS) project. The capabilities of the RIOT code are shared through a portlet in the CMCS portal that allows easy specification and processing of RIOT inputs, remote execution of RIOT, tracking of data pedigree, and translation of RIOT outputs to a table view and to a commonly-used mechanism format.

Dynamic Spatial and Temporal Allocation of Reduced Chemical Kinetics Models in Combustion Computational Fluid Dynamics Simulations

Computational Fluid Dynamics (CFD) numerical simulations used for combustion do not currently have the predictive capability that is typically found for non-reacting flow simulations. This is due to the prohibitively high computational cost incurred when one introduces detailed chemical kinetics. An efficient method – Adaptive Chemistry – has been developed to enable detailed chemical kinetics modeling. Adaptive Chemistry adapts the reaction mechanism used in the CFD to the local reaction conditions. Instead of a single comprehensive reaction mechanism throughout the computation, a dynamic distribution of smaller, locally valid reduced models is used to accurately capture the chemical kinetics at a small fraction of the cost of the traditional “single-mechanism” approach. In this work, we present two new algorithms to facilitate practical application of Adaptive Chemistry to CFD simulations. Simulation results obtained from our implementation are presented and discussed.