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Coble A.R.,CMCL Innovations | Smallbone A.,CMCL Innovations | Bhave A.,CMCL Innovations | Mosbach S.,University of Cambridge | And 3 more authors.
SAE Technical Papers | Year: 2011

Employing detailed chemistry into modern engine simulation technologies has potential to enhance the robustness and predictive power of such tools. Specifically this means significant advancements in the ability to compute the onset of ignition, low and high temperature heat release, local extinction, knocking, exhaust gas emissions formation etc. resulting in a set of tools which can be employed to carry out virtual engineering studies and add additional insight into common IC engine development activities such as computing IMEP, identifying safe/feasible operating ranges, minimizing exhaust gas emissions and optimizing operating strategy. However the adoption of detailed chemistry comes at a greater computational cost, this paper investigates the means to retain computational robustness and ease of use whist reducing computational timescales. This paper focuses upon a PDF (Probability Density Function) based model based on the Stochastic Reactor Model (SRM), which has gained increasing attention from academics and industry for its capabilities to account for in-cylinder processes such as chemical kinetics, fuel injection, turbulent mixing, heat transfer etc. whilst retaining in-cylinder stratification of mixture composition (i.e. fuel equivalence ratio) and temperature. Among the techniques considered here are: a standard KIVA 3V simulation, down-sampling from 3D CFD composition-space to stochastic particles using sequential coupling of KIVA 3V and SRM, the use of detailed chemical kinetics within SRM, chemical mechanism reduction, down-sampling of a chemical mixture space within the SRM, and parallelization of chemistry solution within SRM. The experimental engine setup studied is that used by Cao et. al. [1], employing Premixed Charge Compression Ignition (PCCI), which is a Low Temperature Combustion (LTC) strategy for diesel engines. This paper demonstrates how equivalent results can be achieved with a reduction in computational time from 28 days to 10 minutes. In order to enable engineers to more easily exploit SRM's capabilities in the IC engine development process, it has been coupled with an industry-standard 1D engine cycle simulation tool (Ricardo WAVE) and a working example is presented. Copyright © 2011 SAE International.

Azadi P.,University of Cambridge | Brownbridge G.,University of Cambridge | Mosbach S.,University of Cambridge | Smallbone A.,Cmcl innovations | And 4 more authors.
Applied Energy | Year: 2014

We determine the environmental impact of different biodiesel production strategies from algae feedstock in terms of greenhouse gas (GHG) emissions and non-renewable energy consumption, we then benchmark the results against those of conventional and synthetic diesel obtained from fossil resources. The algae cultivation in open pond raceways and the transesterification process for the conversion of algae oil into biodiesel constitute the common elements among all considered scenarios. Anaerobic digestion and hydrothermal gasification are considered for the conversion of the residues from the wet oil extraction route; while integrated gasification-heat and power generation and gasification-Fischer-Tropsch processes are considered for the conversion of the residues from the dry oil extraction route. The GHG emissions per unit energy of the biodiesel are calculated as follows: 41g e-CO2/MJb for hydrothermal gasification, 86g e-CO2/MJb for anaerobic digestion, 109g e-CO2/MJb for gasification-power generation, and 124g e-CO2/MJb for gasification-Fischer-Tropsch. As expected, non-renewable energy consumptions are closely correlated to the GHG values. Also, using the High Dimensional Model Representation (HDMR) method, a global sensitivity analysis over the entire space of input parameters is performed to rank them with respect to their influence on key sustainability metrics. Considering reasonable ranges over which each parameter can vary, the most influential input parameters for the wet extraction route include extractor energy demand and methane yield generated from anaerobic digestion or hydrothermal gasification of the oil extracted-algae. The dominant process input parameters for the dry extraction route include algae oil content, dryer energy demand, and algae annual productivity. The results imply that algal biodiesel production from a dried feedstock may only prove sustainable if a low carbon solution such as solar drying is implemented to help reducing the water content of the feedstock. © 2013 Elsevier Ltd.

Smallbone A.,Cmcl Innovations | Bhave A.,Cmcl Innovations | Hillman M.,Cmcl Innovations | Saville A.,Caterpillar Inc. | McDavid R.,Caterpillar Inc.
SAE Technical Papers | Year: 2013

This paper builds upon recent publication (SAE Technical Paper 2011-01-1388, 2011, doi:10.4271/2011-01-1388) and outlines the on-going development of an advanced simulator for virtual engine mapping and optimization of engine performance, combustion and emissions characteristics. The model is further advanced through development of new sub-models for turbulent mixing, multiple injection events, variable injection pressures, engine breathing and gas exchange, as well as particulates formation and oxidation. The result is a simulator which offers engine design and performance data typically associated with 1D thermodynamic engine cycle simulations but with the physics-based model robustness usually associated with 3D CFD methods. This combination then enables efficient optimization of engine design with respect to engine performance, combustion characteristics and exhaust gas emissions. As a demonstration, a detailed method to parameterize (calibrate) the advanced PDF-based model is presented followed by application to three case studies: 1) a concept study of a heavy duty diesel engine, examining the impact of increased injection pressure and lower compression ratio to meet engine design constraints and Stage IV/Tier 4 exhaust gas emission limits for both NOx and PM, 2) examining the performance of both the proposed model and 3D-CFD to simulate heat release and exhaust gas emissions in a HSDI diesel engine, 3) performance of the model over a full load-speed map in terms of combustion and NOx emissions. The results demonstrate the robustness of the model compared to experimental observations and equivalent performance compared to more human resource and CPU cost intensive 3D-CFD simulations. Copyright © 2013 SAE International.

Taylor B.,University of Cambridge | Xiao N.,University of Cambridge | Sikorski J.,University of Cambridge | Yong M.,University of Cambridge | And 7 more authors.
Applied Energy | Year: 2013

This paper presents a techno-economic analysis of carbon-negative algal biodiesel production routes that use currently available technologies. The production process includes the following stages: carbon-neutral renewable electricity generation for powering the plant, algal growth in photobioreactors, algae dewatering and lipid extraction, and biofuel conversion and refining. As carbon dioxide is consumed in the algal growth process, side products are not burned (with CO2 release), and the energy supplied to the entire production process is obtained from concentrated solar power, the whole system is assumed carbon footprint negative. Under assumptions related to economics of scale, the techno-economic model is extended to account for varying industrial scales of production. Verified data from a selection of commercially available technologies are used as inputs for the model, and the economic viability of the various production routes is assessed. From the various routes investigated, one scheme involving combined gasification and Fischer-Tropsch of algal solids to produce biodiesel along with conversion of algal lipids into biodiesel through transesterification was found to be promising. Assuming a typical economic scaling factor of 0.8, an algal biodiesel process with an annual production rate of 100Mt/year is identified to achieve a biodiesel price comparable to the current conventional diesel price (approximately £1.39/litre at the pump, or $114/barrel of crude) with a discounted break-even time of 6years. © 2013 Elsevier Ltd.

Brownbridge G.P.E.,University of Cambridge | Smallbone A.,Cmcl Innovations | Phadungsukanan W.,University of Cambridge | Mosbach S.,University of Cambridge | And 2 more authors.
SAE Technical Papers | Year: 2011

This paper describes the development of a novel data model for storing and sharing data obtained from engine experiments, it then outlines a methodology for automatic model development and applies it to a state-of-the-art engine combustion model (including chemical kinetics) to reduce corresponding model parameter uncertainties with respect engine experiments. These challenges are met by adopting the latest developments in the semantic web to create a shared data model resource for the IC engine development community. The relevant data can be extracted and then used to set-up simulations for parameter estimation by passing it to the relevant application models. A methodology for incorporating experimental and model uncertainties into the model optimization procedure is presented. Data from seven operating points have been extracted from the proposed data model and have been incorporated into a state-of-the-art in-cylinder IC engine model through the optimization of model parameters whilst accounting for the model parameter and experimental uncertainties. Copyright © 2011 SAE International.

Smallbone A.,CMCL Innovations | Bhave A.,CMCL Innovations | Coble A.,CMCL Innovations | Mosbach S.,University of Cambridge | And 3 more authors.
SAE Technical Papers | Year: 2011

Regulations on emissions from diesel and gasoline fuelled engines are becoming more stringent in all parts of the world. Hence there is a great deal of interest in developing advanced combustion systems that offer the efficiency of a diesel engine, but with low PM and NOx. One promising approach is that of Partially-Premixed Compression Ignition (PPCI) or Low Temperature Combustion (LTC). Using this approach, PM can be reduced in compression ignition engines by promoting the mixing of fuel and air prior to combustion. This paper describes the application of an advanced combustion simulator for fuels, combustion and emissions to analyze the key processes which occur in PPCI combustion mode. A detailed chemical kinetic model with advanced PM population balance sub-model is employed in a PPCI engine context to examine the impact of ignition resistance on combustion, mixing, ignition and emissions. The ignition and combustion of a diesel-like fuel (n-heptane) and low octane gasoline-like fuel (84PRF) are compared using the model highlighting how the diesel-like fuel ignites at very rich equivalence ratios whereas the gasoline-like fuel ignites on the lean side. Sources of exhaust gas emissions are also identified. For the first time, a computational model is employed to demonstrate the trade-off between low PM emissions and "over-mixing" (sensitivity to cycle-to-cycle variations and combustion instability) for a full range of fuels with increasing ignition resistance. These results are then discussed noting that conventional hydrocarbon fuels which fulfill either a conventional diesel or gasoline standards are not necessarily consistent with those required to run an engine operating at it's optimal point in terms of PM emissions and combustion stability. Copyright © 2011 SAE International.

Coble A.,CMCL Innovations | Smallbone A.,CMCL Innovations | Bhave A.,CMCL Innovations | Watson R.,University of Cambridge | And 2 more authors.
2010 IEEE Education Engineering Conference, EDUCON 2010 | Year: 2010

Use of leading industrial technology in 'remote experiments' and 'virtual laboratories' delivers authentic experiences to engineering students. Both types of learning resources can easily be shared between universities or industrial partners, leading to dramatic reductions in the costs associated with development, construction, operation and maintenance of traditional laboratory set-ups; however, each is characterised by inherent advantages and disadvantages. We compare and contrast remote experiments and virtual labs, using two case studies: 'Cambridge Weblab', a remote experiment built by the Computational Modeling (CoMo) Group at the University of Cambridge and 'SRM web-suite', a virtual lab developed by CMCL innovations. The Cambridge Weblab remote experiment uses a Siemens SIMATIC PS7 industrial interface to control a chemical reactor, yielding authentic experiences of industrial practices for students. A variety of pedagogical approaches employed by institutions using the Weblab are also discussed in this paper. The SRM web-suite uses an advanced engine design tool that simulates fuels, combustion and emissions in conventional and advanced internal combustion engines. The detailed simulations have been precisely tailored for training and educational settings. The web-suite labs provide students and engineering professionals with experience using the latest industry-standard technology, whilst supporting a wide range of educational goals e.g. undergraduate courses in combustion engines or chemical reaction engineering and advanced courses in futuristic fuels or powertrain engineering. We also assess the potential impact of these learning resources within the panEuropean Library of Labs (LiLa) framework. Ultimately, we demonstrate that remote experiments and virtual laboratories are complementary, that there is significant potential for future integration of the two technologies, and that both can benefit from the latest industrial technologies. © 2010 IEEE.

Brownbridge G.,University of Cambridge | Azadi P.,University of Cambridge | Smallbone A.,Cmcl innovations | Bhave A.,Cmcl innovations | And 2 more authors.
Bioresource Technology | Year: 2014

This study presents a techno-economic assessment of algae-derived biodiesel under economic and technical uncertainties associated with the development of algal biorefineries. A global sensitivity analysis was performed using a High Dimensional Model Representation (HDMR) method. It was found that, considering reasonable ranges over which each parameter can vary, the sensitivity of the biodiesel production cost to the key input parameters decreases in the following order: algae oil content > algae annual productivity per unit area > plant production capacity > carbon price increase rate. It was also found that the Return on Investment (ROI) is highly sensitive to the algae oil content, and to a lesser extent to the algae annual productivity, crude oil price and price increase rate, plant production capacity, and carbon price increase rate. For a large scale plant (100,000. tonnes of biodiesel per year) the production cost of biodiesel is likely to be £0.8-1.6 per kg. © 2013 Elsevier Ltd.

Smallbone A.,Cmcl Innovations | Bhave A.,Cmcl Innovations | Man P.,Cmcl Innovations
SAE Technical Papers | Year: 2014

This paper demonstrates how the validation and verification phase of prototype development can be simplified through the application of the Model Development Suite (MoDS) software by integrating advanced statistical and numerical techniques. The authors have developed and present new numerical and software integration methods to support a) automated model parameter estimation (model calibration) with respect to experimental data and, b) automated global sensitivity analysis through using a High Dimensional Model Representation (HDMR). These methods are demonstrated at 1) a component level by performing systematic parameter estimation of various friction models for heavy-duty IC engine applications, 2) at a sub-component level by performing a parameter estimation for an engine performance model, and 3) at a system level for evaluating fuel efficiency losses (and CO2 sources) in a vehicle model over 160 'real-world' and legislated drive cycles. Copyright © 2014 SAE International.

Etheridge J.,CMCL Innovations | Bhave A.,CMCL Innovations | Smallbone A.,CMCL Innovations | Coble A.,CMCL Innovations | And 2 more authors.
SAE Technical Papers | Year: 2011

Regulations concerning emissions from diesel and gasoline fuelled engines are becoming ever more stringent in all parts of the world. Historically these targets have been achieved through on-going technological development using an iterative process of computational modelling, design, build and test. Computational modelling is certainly the cheapest aspect within this process and if employed to meet more of the challenges associated with development, has the potential to significantly reduce developmental cost and time scales. Furthermore, computational models are an effective means to retain and apply often highly focused technical knowledge of complex processes within development teams thus delivering greater insight into processes. As such there is a great deal of interest in advanced simulation technologies; one such technology is srm suite™ which has proven effective in simulating in-cylinder combustion processes to enable engineers to identify optimal injection, valve train and spark timing operating strategy to achieve a particular load-speed point with reduced target emissions. The model accounts for the impact of fuel injection strategies, detailed chemical kinetics, turbulent mixing, and heat losses on the inhomogeneities associated with the in-cylinder composition and temperature, within practical computing time scales. In order to account for the valve train dynamics and engine breathing within the context of engine cycle simulation, the srm suite has been coupled with standard 1D engine cycle simulators and applied to investigate three industry relevant problems (1) investigating cycle-to-cycle variations on emissions in an SI engine, (2) investigating emissions at different injection timings, speeds and loads in a diesel engine operated with pilot injection and high levels of Exhaust Gas Recirculation (EGR), and (3) simulating a dual injection Homogeneous Charge Compression Ignition (HCCI) engine operated with an injection (fuel reformation) during Negative Valve Overlap (NVO). In each context, computational results are compared with experimental observations and conclusions presented. Copyright © 2011 SAE International and Copyright © 2011 SIAT, India.

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