Publications

Advances in Applied Energy
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Abstract
This paper investigates how using heat pumps for domestic heating would impact fuel poverty and social inequality. The analysis integrates a geospatial description of climate observations, gas and electricity infrastructure, energy consumption and fuel poverty from the base world of a Universal Digital Twin based on the World Avatar knowledge graph. Historic temperature data were used to estimate the temporal and geospatial variation of the performance of air source heat pumps in the UK. The corresponding change in gas and electricity consumption that could be achieved using heat pumps instead of gas for domestic heating was estimated. The geospatial impact of the heat pumps was assessed in terms of CO2 savings, and their effect on fuel cost and fuel poverty. Whilst heat pumps would reduce emissions, it is predicted that they would increase fuel costs. It was shown that both local and regional areas of high fuel poverty would experience some of the largest increases in fuel cost. This illustrates the potential for the transition to sustainable heating to exacerbate social inequality. The analysis suggests that existing regional inequalities will increase, and that it comes down to a political choice between investments to support the most effective use of heat pumps, and delayed investments to counter social inequality. The ability of the World Avatar to integrate the models and data necessary to perform this type of holistic analysis provides a means to generate actionable information, for example, to enable local policy interventions to address the tension between social and environmental goals.

Applied Energy
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Abstract
District heating is expected to play an essential role in the cost-effective decarbonisation strategy of many countries. Resource-optimised management of district heating networks depends on a wide range of factors, including demand forecasting, operational flexibility, and increasingly volatile market conditions. However, traditional operations often still rely on static models and rather simple heuristics, while holistic optimisation requires dynamic cross-domain interoperability to allow the consideration of all these factors. This paper demonstrates a proof-of-concept for a knowledge graph based optimisation problem to minimise total heat generation cost for a district heating provider. The optimisation follows a hierarchical approach based on a merit-order principle and is embedded in a model predictive control framework to allow the system to incorporate most recent information and react to disturbances promptly. A detailed sensitivity study is conducted to identify key model parameters and assess the impact of anticipated changes in regulation and market conditions. Simulation-based optimisation is used to determine the short-term heat generation mix based on data-driven gas consumption models and day-ahead forecasts for the network’s energy demand and grid temperatures. Seasonal autoregressive integrated moving average models with exogenous predictor variables are found to be sufficiently accurate and precise. The effectiveness of the approach is demonstrated for a case study of an existing heating network of a midsize town in Germany, where a reduction of approximately 20% and 40% compared to baseline operational data is obtained for operating cost and CO2 emissions, respectively.

Preprint
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Abstract
This paper introduces a dynamic knowledge-graph approach for digital twins and demonstrates how it fulfils many of the technical requirements of the vision to create a National Digital Twin of the UK. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data is connected, portable, discoverable and queryable via a uniform interface. The knowledge graph includes the notions of a ‘base world’ that describes the real world and that is maintained by agents that incorporate real-time data, and of ‘parallel worlds’ that support the intelligent exploration of alternative designs without affecting the base world. Use cases are presented that demonstrate the ability of the dynamic knowledge graph to host geospatial and chemical data, control chemistry experiments, perform cross-domain simulations and perform scenario analysis. The questions of how to make intelligent suggestions for alternative scenarios and how to ensure alignment between the scenarios considered by the knowledge graph and the goals of society are considered. Work to extend the dynamic knowledge graph to develop a digital twin of the UK to support the decarbonisation of the energy system is discussed. Important directions for future research are highlighted.

Energy and Fuels
https://doi.org/10.1021/acs.energyfuels.8b01007

Abstract
This paper presents a methodology that combines physicochemical modeling with advanced statistical analysis algorithms as an efficient workflow, which is then applied to the optimization and design of biomass pyrolysis and gasification processes. The goal was to develop an automated flexible approach for the analyses and optimization of such processes. The approach presented here can also be directly applied to other biomass conversion processes and, in general, to all those processes for which a parametrized model is available. A flexible physicochemical model of the process is initially formulated. Within this model, a hierarchy of sensitive model parameters and input variables (process conditions) is identified, which are then automatically adjusted to calibrate the model and to optimize the process. Through the numerical solution of the underlying mathematical model of the process, we can understand how species concentrations and the thermodynamic conditions within the reactor evolve for the two processes studied. The flexibility offered by the ability to control any model parameter is critical in enabling optimization of both efficiency of the process as well as its emissions. It allows users to design and operate feedstock-flexible pyrolysis and gasification processes, accurately control product characteristics, and minimize the formation of unwanted byproducts (e.g., tar in biomass gasification processes) by exploiting various productivity-enhancing simulation techniques, such as parameter estimation, computational surrogate (reduced order model) generation, uncertainty propagation, and multi-response optimization.

Applied Energy (Volume 106)
https://doi.org/10.1016/j.apenergy.2013.01.065

Abstract
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 100 Mt/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 6 years.

Applied Energy (Volume 113)
https://doi.org/10.1016/j.apenergy.2013.09.027

Abstract
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.

Bioresource Technology (Volume 151)
https://doi.org/10.1016/j.biortech.2013.10.062

Abstract
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.

Sustainable Energy and Fuels (Volume 2)
https://doi.org/10.1039/c8se00061a

Abstract
It is broadly recognised that CO2 capture and storage (CCS) and associated negative emissions technologies (NETs) are vital to meeting the Paris agreement target. The hitherto failure to deploy CCS on the required scale has led to the search for options to improve its economic return. CO2 capture and utilisation (CCU) has been proposed as an opportunity to generate value from waste CO2 emissions and improve the economic viability of CCS, with the suggestion of using curtailed renewable energy as a core component of this strategy. This study sets out to quantify (a) the amount of curtailed renewable energy that is likely to be available in the coming decades, (b) the amount of fossil CO2 emissions which can be avoided by using this curtailed energy to convert CO2 to methanol for use as a transport fuel – power-to-fuel, with the counterfactual of using that curtailed energy to directly remove CO2 from the atmosphere via direct air capture (DAC) and subsequent underground storage, power-to-DAC.

Atmospheric Environment (Volume 235)
https://doi.org/10.1016/j.atmosenv.2020.117642

Abstract
Particulate emissions from on-road motor vehicles are the focus of intensive current research due to the impact of the ambient particulate matter (PM) levels on climate and human health. Constant improvement in engine technology has led to significant decrease in the number and mass of emitted PM, but particular concern is raised nowadays by the ultrafine particles. In this context, there is a critical lack of certification procedures for the measurement of the smallest-size (<23 nm) particulate matter emissions. To support the engine development process as well as future certification procedures, a measurement technology for sub-23 nm particles must be designed. The development of a reliable measurement procedure entails understanding the formation and evolution of particles from the engine to the tailpipe via multiple analytical techniques and theoretical simulations. We present here extensive experimental characterization of ultrafine particles emitted by a gasoline direct injection single-cylinder engine as particle generator. The particles were sampled using a cascade impactor which allows size-separation into 13 different size bins. Chemical characterization of the collected size- selected particles was performed using mass spectrometry, which gives access to detailed molecular information on chemical classes of critical interest such as organosulphates, oxygenated hydrocarbons, nitrogenated hydrocarbons, metals, or polycyclic aromatic hydrocarbons. Additionally, the morphology of the emitted particles was probed with atomic force (AFM) and scanning electron microscopy (SEM). Tip-Enhanced Raman Spectroscopy (TERS) was applied for the first time to sub-10 nm combustion-generated particles to gather information on their nanostructure. The extensive database built from these multiple experimental characterizations has been used as input of a theoretical approach to simulate and validate engine out- emissions. These studies were performed in the framework of the H2020 PEMS4Nano project which aims to the development of a robust, reliable and reproducible measurement technology for particles down to 10 nm for both chassis dyno and real driving emissions (RDE).

Journal of Data-Centric Engineering
https://doi.org/10.1017/dce.2020.4

Abstract
We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of computationally cheaper alternatives to physics-based models. High-dimensional model representation (HDMR) is also briefly discussed and acts as a benchmark model for comparison. We apply the considered methods to a dataset, which was obtained from a compression ignition engine and includes as outputs soot and NOx emissions as functions of 14 engine operating condition variables. We combine a quasi-random global search with a conventional grid-optimization method in order to identify suitable values for several DKL hyperparameters, which include network architecture, kernel, and learning parameters. The performance of DKL, HDMR, plain GPs, and plain DNNs is compared in terms of the root mean squared error (RMSE) of the predictions as well as computational expense of training and evaluation. It is shown that DKL performs best in terms of RMSE in the predictions whilst maintaining the computational cost at a reasonable level, and DKL predictions are in good agreement with the experimental emissions data.

SAE Technical Paper
https://doi.org/10.4271/2018-01-1739

Abstract
Digital engineering workflows, involving physico-chemical simulation and advanced statistical algorithms, offer a robust and cost-effective methodology for model-based internal combustion engine development. In this paper, a modern Tier 4 capable Cat® C4.4 engine is modelled using a digital workflow that combines the probability density function (PDF)-based Stochastic Reactor Model (SRM) Engine Suite with the statistical Model Development Suite (MoDS). In particular, an advanced multi-zonal approach is developed and applied to simulate fuels, in-cylinder combustion and gas phase as well as particulate emissions characteristics, validated against measurements and benchmarked with respect to the predictive power and computational costs of the baseline model. The multizonal SRM characterises the combustion chamber on the basis of different multi-dimensional PDFs dependent upon the bulk or the thermal boundary layer in contact with the cylinder liner. In the boundary layer, turbulent mixing is significantly weaker and heat transfer to the liner alters the combustion process. The integrated digital workflow is applied to perform parameter estimation based on the in-cylinder pressure profiles and engine-out emissions (i.e. NOx, CO, soot and unburnt hydrocarbons; uHCs) measurements. Four DoE (design-of-experiments) datasets are considered, each comprising measurements at a single load-speed point with various other operating conditions, which are then used to assess the capability of the calibrated models in mimicking the impact of the input variable space on the combustion characteristics and emissions. Both model approaches predict in-cylinder pressure profiles, NOx, and soot emissions satisfactorily well across all four datasets. Capturing the physics of emission formation near the cylinder liner enables the multi-zonal SRM approach to provide improved predictions for intermediates, such as CO and uHCs, particularly at low load operating points. Finally, fast-response surrogates are generated using the High Dimensional Model Representation (HDMR) approach, and the associated global sensitivities of combustion metrics and emissions are also investigated.

SAE International Journal of Advances and Current Practices in Mobility
https://doi.org/10.4271/2019-26-0062

Abstract
Model guided application (MGA) combining physico-chemical internal combustion engine simulation with advanced analytics offers a robust framework to develop and test particle number (PN) emissions reduction strategies. The digital engineering workflow presented in this paper integrates the kinetics & SRM Engine Suite with parameter estimation techniques applicable to the simulation of particle formation and dynamics in gasoline direct injection (GDI) spark ignition (SI) engines. The evolution of the particle population characteristics at engine-out and through the sampling system is investigated. The particle population balance model is extended beyond soot to include sulphates and soluble organic fractions (SOF). This particle model is coupled with the gas phase chemistry precursors and is solved using a sectional method. The combustion chamber is divided into a wall zone and a bulk zone and the fuel impingement on the cylinder wall is simulated. The wall zone is responsible for resolving the distribution of equivalence ratios near the wall, a factor that is essential to account for the formation of soot in GDI SI engines. In this work, a stochastic reactor model (SRM) is calibrated to a single-cylinder test engine operated at 12 steady state load-speed operating points. First, the flame propagation model is calibrated using the experimental in-cylinder pressure profiles. Then, the population balance model parameters are calibrated based on the experimental data for particle size distributions from the same operating conditions. Good agreement was obtained for the incylinder pressure profiles and gas phase emissions such as NOx. The MGA also employs a reactor network approach to align with the particle sampling measurements procedure, and the influence of dilution ratios and temperature on the PN measurement is investigated. Lastly, the MGA and the measurements procedure are applied to size-resolved chemical characterisation of the emitted particles.

Advanced Energy (Volume 190)
https://doi.org/10.1016/j.apenergy.2016.12.120

Abstract
Biomass-based power generation combined with CO₂ capture and storage (Biopower CCS) currently represents one of the few practical and economic means of removing large quantities of CO₂ from the atmosphere, and the only approach that involves the generation of electricity at the same time. We present the results of the Techno-Economic Study of Biomass to Power with CO₂ capture (TESBiC) project, that entailed desk-based review and analysis, process engineering, optimisation as well as primary data collection from some of the leading pilot demonstration plants. From the perspective of being able to deploy Biopower CCS by 2050, twenty-eight Biopower CCS technology combinations involving combustion or gasification of biomass (either dedicated or co-fired with coal) together with pre-, oxy- or post-combustion CO₂ capture were identified and assessed. In addition to the capital and operating costs, techno-economic characteristics such as electrical efficiencies (LHV% basis), Levelised Cost of Electricity (LCOE), costs of CO₂ captured and CO₂ avoided were modelled over time assuming technology improvements from today to 2050. Many of the Biopower CCS technologies gave relatively similar techno-economic results when analysed at the same scale, with the plant scale (MW_e) observed to be the principal driver of CAPEX (£/MW_e) and the cofiring % (i.e. the weighted feedstock cost) a key driver of LCOE. The data collected during the TESBiC project also highlighted the lack of financial incentives for generation of electricity with negative CO₂ emissions.

SAE Technical Paper
https://doi.org/10.4271/2014-01-2564

Abstract
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).

Internal Combustion Engines: Performance, Fuel Economy and Emissions (IMechE)
https://doi.org/10.1533/9781782421849.4.137

Abstract
This paper summarises the latest advancements in Computer Aided Engineering (CAE) tools which can simulate the impact of fuels on internal combustion (IC) engine performance. We introduce advanced surrogate models for application to gaseous, conventional gasoline/diesel, low grade gasoline, synthetic and bio- liquid fuels (E0-E100) in single and multi- component blends. In addition, dual-fuel ready surrogates (natural gas with diesel fuel pilot injection) are developed and analysed using shock tube and standardised fuel test data (Cetane and Research Octane Number), these models are then applied directly to simulate knock limits, ignition and heat release in state-of-the-art IC engine applications.

SAE Technical Paper
https://doi.org/10.4271/2013-01-0308

Abstract
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.

SAE Technical Paper
https://doi.org/10.4271/2011-01-0237

Abstract
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.

SAE Technical Paper
https://doi.org/10.4271/2011-01-1388

Abstract
In recent decades, “physics-based” gas-dynamics simulation tools have been employed to reduce development timescales of IC engines by enabling engineers to carry out parametric examinations and optimisation of alternative engine geometry and operating strategy configurations using desktop PCs. However to date, these models have proved inadequate for optimisation of in-cylinder combustion and emissions characteristics thus extending development timescales through additional experimental development efforts.

This research paper describes how a Stochastic Reactor Model (SRM) with reduced chemistry can be employed to successfully determine in-cylinder pressure, heat release and emissions trends from a diesel fuelled engine operated in compression ignition direct injection mode using computations which are completed in 147 seconds per cycle. The model was successfully validated against 46 steady state operating points in terms of in-cylinder pressure and exhaust gas emissions over a three-dimensional matrix comprising ranges of EGR, boost pressure and injection timing. The resulting model was then employed to examine the local in-cylinder temperatures and equivalence ratios and to highlight the main sources of excessive exhaust gas emissions.

With a view of identifying the optimal operating strategy, a parametric sweep comprised of 968 computations were then completed, the results of which were narrowed based on satisfying stable operating limits (i.e. peak pressure, knocking combustion). Excessive exhaust gas emissions were identified to highlight the most suitable regime for minimal emissions. Finally, the potential of this technology is examined by discussing aspects of engine development process which can be accelerated using the tool.

SAE Technical Paper
https://doi.org/10.4271/2011-01-0849

Abstract
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.

SAE Technical Paper
https://doi.org/10.4271/2011-01-1184

Abstract

SAE Technical Paper
https://doi.org/10.4271/2010-01-0152

Abstract
Analyzing the combustion characteristics, engine performance, and emissions pathways of the internal combustion (IC) engine requires management of complex and an increasing quantity of data. With this in mind, effective management to deliver increased knowledge from these data over shorter timescales is a priority for development engineers. This paper describes how this can be achieved by combining conventional engine research methods with the latest developments in process informatics and statistical analysis. Process informatics enables engineers to combine data, instrumental and application models to carry out automated model development including optimization and validation against large data repositories of experimental data. This is complemented with the inclusion of experimental error and model parameter uncertainty, to yield confidence regimes on the final model result, hence the impact of specific shortcomings of the model and/or experimental dataset can be identified in a systematic manner. A methodology for model implementation is described including an extensible data model for storing engine experimental data in a consistent format. Finally, a working example for an application model is presented through the development of a semi-empirical soot model for diesel engines.

SAE Technical Paper
https://doi.org/10.4271/2010-01-0572

Abstract
This research describes the potential to adopt detailed chemical kinetics for practical and potential future fuels using tri-component surrogate mixtures capable of simulating fuel octane “sensitivity” . Since the combustion characteristics of modern fuels are routinely measured using the RON and MON of the fuel, a methodology to generate detailed chemical kinetic mechanisms for these fuels based on these data is presented. Firstly, a novel correlation between various tri-component blends (comprised of i-octane, n-heptane and toluene) and fuel RON and MON was obtained by carrying out standard octane tests. Secondly, a chemical kinetic mechanism for tri-component fuels was validated using a Stochastic Reactor Model (SRM) suite, an in-cylinder engine combustion simulator, and a series of engine experiments conducted in HCCI operating mode. Thirdly, the methodology was applied to predict combustion characteristics of a practical gasoline and fuel blends with ethanol and di-iso-butylene blends using detailed chemical kinetics. Finally, for the first time the application of this technique was demonstrated by employing detailed chemistry in the optimization of two engines and two fuels operating in HCCI mode. Here a parametric study highlighted the adoption of fuels with “sensitivity” could significantly extend the HCCI peak operating IMEP limit by as much as 60%.

SAE International Journal of Engines
https://doi.org/10.4271/2009-01-1102

Abstract
Premixed Charge Compression Ignition (PCCI), a Low Temperature Combustion (LTC) strategy for diesel engines is of increasing interest due to its potential to simultaneously reduce soot and NO_x emissions. However, the influence of mixture preparation on combustion phasing and heat release rate in LTC is not fully understood. In the present study, the influence of injection timing on mixture preparation, combustion and emissions in PCCI mode is investigated by experimental and computational methods. A sequential coupling approach of 3D CFD with a Stochastic Reactor Model (SRM) is used to simulate the PCCI engine. The SRM accounts for detailed chemical kinetics, convective heat transfer and turbulent micro-mixing. In this integrated approach, the temperature-equivalence ratio statistics obtained using KIVA 3V are mapped onto the stochastic particle ensemble used in the SRM. The coupling method proved to be advantageous in terms of computational expense and emission prediction capability, as compared with direct coupling of CFD and chemical kinetics. The results show that the fuel rich pockets in the late injection timing are desirable for triggering auto-ignition and advancing the combustion phasing. Furthermore, the model is utilised to study the impact of combustion chamber design (open bowl, vertical side wall bowl and re-entry bowl) on PCCI combustion and emissions. The piston bowl geometry was observed to influence the in-cylinder mixing and the pollutant formation for the conditions studied.

SAE Technical Paper
https://doi.org/10.4271/2008-01-0021

Abstract
A detailed chemical model was implemented in the KIVA-3V two dimensional CFD code to investigate the effects of the spray cone angle and injection timing on the PCCI combustion process and emissions in an optical research diesel engine. A detailed chemical model for Primary Reference Fuel (PRF) consisting of 157 species and 1552 reactions was used to simulate diesel fuel chemistry. The model validation shows good agreement between the predicted and measured pressure and emissions data in the selected cases with various spray angles and injection timings. If the injection is retarded to -50° ATDC, the spray impingement at the edge of the piston corner with 100° injection angle was shown to enhance the mixing of air and fuel. The minimum fuel loss and more widely distributed fuel vapor contribute to improving combustion efficiency and lowering uHC and CO emissions in the engine idle condition. Finally, the coupling of CFD and multi-zone Stochastic Reactor Model (SRM) was demonstrated to show improvement in CO and uHC emissions prediction.

International Journal of Engine Research (IMechE)
https://doi.org/10.1243%2F14680874JER01806

Abstract
Multiple direct injection (MDI) is a promising strategy to enable fast-response ignition control as well as expansion of the homogeneous charge compression ignition (HCCI) engine operating window, thus realizing substantial reductions of soot and NO_x emissions. The present paper extends a zero-dimensional-probability-density-function-based stochastic reactor model (SRM) for HCCI engines in order to incorporate MDI and an improved turbulent mixing model. For this, a simplistic spray model featuring injection, penetration, and evaporation sub-models is formulated, and mixing is described by the Euclidean minimal spanning tree (EMST) sub-model accounting for localness in composition space. The model is applied to simulate a gasoline HCCI engine, and the in-cylinder pressure predictions for single and dual injection cases show a satisfactory agreement with measurements. From the parametric studies carried out it is demonstrated that, as compared with single injection, the additional second injection contributes to prolonged heat release and consequently helps to prevent knock, thereby extending the operating range on the high load side. Tracking the phase space trajectories of individual stochastic particles provides significant insight into the influence of local charge stratification owing to direct injection on HCCI combustion.

SAE Technical Paper
https://doi.org/10.4271/2007-01-0049

Abstract

SAE Technical Paper
https://doi.org/10.4271/2007-01-1880

Abstract
Two-stage fuel direct injection (DI) has the potential to expand the operating region and control the auto-ignition timing in a Diesel fuelled homogeneous charge compression ignition (HCCI) engine. In this work, to investigate the dual-injection HCCI combustion, a stochastic reactor model, based on a probability density function (PDF) approach, is utilized. A new wall-impingement sub-model is incorporated into the stochastic spray model for direct injection. The model is then validated against measurements for combustion parameters and emissions carried out on a four stroke HCCI engine. The initial results of our numerical simulation reveal that the two-stage injection is capable of triggering the charge ignition on account of locally rich fuel parcels under certain operating conditions, and consequently extending the HCCI operating range. Furthermore, both simulated and experimental results on the effect of second injection timing on combustion indicate that there exists an optimal second injection timing to gain maximum engine output work for a given fuel split ratio.

Combustion and Flame (Volume 144, Issue 3)
https://doi.org/10.1016/j.combustflame.2005.10.015

Abstract
Factors influencing a reliable prediction of CO emissions in a homogeneous charge compression ignition (HCCI) engine are investigated using an improved probability density function (PDF)-based engine cycle model. A previously validated PDF-based stochastic reactor model is utilized to identify critical sources of CO emissions numerically. The full cycle model includes detailed chemical kinetics, accounts for the inhomogeneities in temperature and composition, and has been demonstrated to provide sufficiently reliable predictions of the combustion and engine parameters and emissions.

SAE Technical Paper
https://doi.org/10.4271/2006-01-1362

Abstract
We numerically simulate a Homogeneous Charge Compression Ignition (HCCI) engine fuelled with a blend of ethanol and diethyl ether by means of a stochastic reactor model (SRM). A 1D CFD code is employed to calculate gas flow through the engine, whilst the SRM accounts for combustion and convective heat transfer. The results of our simulations are compared to experimental measurements obtained using a Caterpillar CAT3401 single-cylinder Diesel engine modified for HCCI operation. We consider emissions of CO, CO₂ and unburnt hydrocarbons as functions of the crank angle at 50% heat release. In addition, we establish the dependence of ignition timing, combustion duration, and emissions on the mixture ratio of the two fuel components. Good qualitative agreement is found between our computations and the available experimental data. The performed numerical simulations predict that the addition of diethyl ether to ethanol neither spreads out the combustion nor lowers light-off temperatures significantly, both in accordance with experimental observations.

Combustion and Flame (Volume 147, Issues 1-2)
https://doi.org/10.1016/j.combustflame.2006.07.005

Abstract
We present a new probability density function (PDF)-based computational model to simulate a homogeneous charge compression ignition (HCCI) engine with direct injection (DI) during gas exchange. This stochastic reactor model (SRM) accounts for the engine breathing process in addition to the closed-volume HCCI engine operation. A weighted-particle Monte Carlo method is used to solve the resulting PDF transport equation. While simulating the gas exchange, it is necessary to add a large number of stochastic particles to the ensemble due to the intake air and EGR streams as well as fuel injection, resulting in increased computational expense. Therefore, in this work we apply a down-sampling technique to reduce the number of stochastic particles, while conserving the statistical properties of the ensemble. In this method some of the most important statistical moments (e.g., concentration of the main chemical species and enthalpy) are conserved exactly, while other moments are conserved in a statistical sense. Detailed analysis demonstrates that the statistical error associated with the down-sampling algorithm is more sensitive to the number of particles than to the number of conserved species for the given operating conditions. For a full-cycle simulation this down-sampling procedure was observed to reduce the computational time by a factor of 8 as compared to the simulation without this strategy, while still maintaining the error within an acceptable limit. Following the detailed numerical investigation, the model, intended for volatile fuels only, is applied to simulate a two-stroke, naturally aspirated HCCI engine fueled with isooctane. The in-cylinder pressure and CO emissions predicted by the model agree reasonably well with the measured profiles. In addition, the new model is applied to estimate the influence of engine operating parameters such as the relative air–fuel ratio and early direct injection timing on HCCI combustion and emissions. The qualitative trends observed in the parametric variation study match well with experimental data in literature.

SAE Technical Paper
https://doi.org/10.4271/2005-01-0161

Abstract
We present a computational tool to develop an exhaust gas recirculation (EGR) – air-fuel ratio (AFR) operating range for homogeneous charge compression ignition (HCCI) engines. A single cylinder Ricardo E-6 engine running in HCCI mode, with external EGR is simulated using an improved probability density function (PDF) based engine cycle model. For a base case, the in-cylinder temperature and unburned hydrocarbon emissions predicted by the model show a satisfactory agreement with measurements [Oakley et al., SAE Paper 2001-01-3606]. Furthermore, the model is applied to develop the operating range for various combustion parameters, emissions and engine parameters with respect to the air-fuel ratio and the amount of EGR used. The model predictions agree reasonably well with the experimental results for various parameters over the entire EGR-AFR operating range thus proving the robustness of the PDF based model. The boundaries of the operating range namely, knocking, partial burn, and misfire are reliably predicted by the model. In particular, the model provides a useful insight into the misfire phenomenon by depicting the cyclic variation in the ignition timing and the in-cylinder temperature profiles. Finally, we investigate two control options, namely heating intake charge and trapping residual burned fraction by negative valve overlap. The effect of these two methods on HCCI combustion and CO, HC and NOx emissions is studied.

SIAM Journal on Scientific Computing (Volume 25, Issue 5)
https://doi.org/10.1137/S1064827502411328

Abstract
We investigate the partially stirred reactor (PaSR), which is based on a simplified joint composition probability density function (PDF) transport equation. Analytical solutions for the first four moments of the mass density function (MDF) obtained from the PaSR model are presented. The Monte Carlo particle method with first order time splitting algorithm is implemented to obtain the first four moments of the MDF numerically. The dynamics of the stochastic particle system is determined by inflow-outflow, chemical reaction, and mixing events. Three different inflow-outflow algorithms are investigated: an algorithm based on the inflow-outflow event modeled as a Poisson process, an inflow-outflow algorithm mentioned in the literature, and a novel algorithm derived on the basis of analytical solutions. It is demonstrated that the inflow-outflow algorithm used in the literature can be explained by considering a deterministic waiting time parameter of a corresponding stochastic process, and also forms a specific case of the new algorithm. The number of particles in the ensemble, N, the nondimensional time step, $\Delta t^{*}$ (ratio of the global time step to the characteristic time of an event), and the number of independent simulation trials, L, are the three sources of the numerical error. The split analytical solutions and the numerical experiments suggest that the systematic error converges as $\Delta t^{*}$ and N^-1 . The statistical error scales as L^-1/2 and N^-1/2 . The significance of the numerical parameters and the inflow-outflow algorithms is also studied by applying the PaSR model to a practical case of premixed kerosene and air combustion.