Numerical Investigation of Spray Collapse in GDI with OpenFOAM

During certain operating conditions in spark-ignited direct injection engines (GDI), the injected fuel will be superheated and begin to rapidly vaporize. Fast vaporization can be beneficial for fuel–oxidizer mixing and subsequent combustion, but it poses the risk of spray collapse. In this work, spray collapse is numerically investigated for a single hole and the spray G eight-hole injector of an engine combustion network (ECN). Results from a new OpenFOAM solver are first compared against results of the commercial CONVERGE software for single-hole injectors and validated. The results corroborate the perception that the superheat ratio $R_p$, which is typically used for the classification of flashing regimes, cannot describe spray collapse behavior. Three cases using the eight-hole spray G injector geometry are compared with experimental data. The first case is the standard G2 test case, with iso-octane as an injected fluid, which is only slightly superheated, whereas the two other cases use propane and show spray collapse behavior in the experiment. The numerical results support the assumption that the interaction of shocks due to the underexpanded vapor jet causes spray collapse. Further, the spray structures match well with experimental data, and shock interactions that provide an explanation for the observed phenomenon are discussed

Gradient boosted decision trees for combustion chemistry integration

This study introduces the gradient boosted decision tree (GBDT) as a machine learning approach to circumvent the need for a direct integration of the typically stiff system of ordinary differential equations that govern the temporal evolution of chemically reacting species. Stiffness primarily relates to the chemistry integration and here, hydrogen/air systems are taken to train and test the ensemble learning approach. We use the LightGBM (Light Gradient Boosting Machine) algorithm to train GBDTs on the time series of various self-igniting mixtures from the time of ignition to equilibrium composition. The GBDT model provides reasonable predictions of the species compositions and thermodynamic states at the next time step in an a priori study. A much more challenging a posteriori study shows that the model can reproduce a full time–history profile of the igniting H/air mixtures, as the results agree very well with those obtained from a direct integration of the ODEs. The GBDT model can be deployed as standalone C++ codes and a speed-up by one order of magnitude has been demonstrated. The GBDT approach can thus be considered as an efficient method to represent the chemical kinetics in the simulation of reactive flows. It provides an alternative to deep artificial neural networks (ANNs) that is comparable in accuracy but easier to couple with existing CFD codes

Mixing Time Scale Models for Multiple Mapping Conditioning with Two Reference Variables

A novel multiple mapping conditioning (MMC) approach has been developed for the modelling of turbulent premixed flames including mixture inhomogeneities due to mixture stratification or mixing with the cold surroundings. MMC requires conditioning of a mixing operator on characteristic quantities (reference variables) to ensure localness of mixing in composition space. Previous MMC used the LES-filtered reaction progress variable as reference field. Here, the reference variable space is extended by adding the LES-filtered mixture fraction effectively leading to a double conditioning of the mixing operator. The model is used to predict a turbulent stratified flame and is validated by comparison with experimental data. The introduction of the second reference variable also requires modification of the mixing time scale. Two different mixing time scale models are compared in this work. A novel anisotropic model for stratified combustion leads to somewhat higher levels of fluctuations for the passive scalar when compared with the original model but differences remain small within the flame front. The results show that both models predict flame position and flame structure with good accuracy

Sparse-Lagrangian PDF Modelling of Silica Synthesis from Silane Jets in Vitiated Co-flows with Varying Inflow Conditions

This paper presents a comparison of experimental and numerical results for a series of turbulent reacting jets where silica nanoparticles are formed and grow due to surface growth and agglomeration. We use large-eddy simulation coupled with a multiple mapping conditioning approach for the solution of the transport equation for the joint probability density function of scalar composition and particulate size distribution. The model considers inception based on finite-rate chemistry, volumetric surface growth and agglomeration. The sub-models adopted for these particulate processes are the standard ones used by the community. Validation follows the “paradigm shift” approach where elastic light scattering signals (that depend on particulate number and size), OH- and SiO-LIF signals are computed from the simulation results and compared with “raw signals” from laser diagnostics. The sensitivity towards variable boundary conditions such as co-flow temperature, Reynolds number and precursor doping of the jet is investigated. Agreement between simulation and experiments is very good for a reference case which is used to calibrate the signals. While keeping the model parameters constant, the sensitivity of the particulate size distribution on co-flow temperature is predicted satisfactorily upstream although quantitative differences with the data exist downstream for the lowest coflow temperature case that is considered. When the precursor concentration is varied, the model predicts the correct direction of the change in signal but notable qualitative and quantitative differences with the data are observed. In particular, the measured signals show a highly non-linear variation while the predictions exhibit a square dependence on precursor doping at best. So, while the results for the reference case appear to be very good, shortcomings in the standard submodels are revealed through variation of the boundary conditions. This demonstrates the importance of testing complex nanoparticle synthesis models on a flame series to ensure that the physical trends are correctly accounted for

Simulation model of the military rescue chain in combat scenarios – a conceptional design

The intensity of current military combats far exceeds the ones of recent conflicts. Since medical resources are limited during battle, the available capacities must be used optimally. This requires distributing patients to available resources, such as medical facilities and transporters. Therefore, the effective planning and coordination of a complex, constantly changing logistics network is of utmost importance. Due to limited data, current planning is often based on expert assumptions. To evaluate current and future concepts, we propose constructive simulation to analyse the interplay of assumptions and planning decisions. For this purpose, we study the military rescue chain and review existing optimization approaches. A conceptual simulation model design is presented and an outlook of upcoming research is given

Fully-resolved simulations of coal particle combustion using a detailed multi-step approach for heterogeneous kinetics

Fully-resolved simulations of the heating, ignition, volatile flame combustion and char conversion of single coal particles in convective gas environments are conducted and compared to experimental data (Molina and Shaddix, 2007). This work extends a previous computational study (Tufano et al., 2016) by adding a significant level of model fidelity and generality, in particular with regard to the particle interior description and hetero- geneous kinetics. The model considers the elemental analysis of the given coal and interpolates its properties by linear superposition of a set of reference coals. The improved model description alleviates previously made assumptions of single-step pyrolysis, fixed volatile composition and simplified particle interior properties, and it allows for the consideration of char conversion. The results show that the burning behavior is affected by the oxygen concentration, i.e. for enhanced oxygen levels ignition occurs in a single step, whereas decreasing the oxygen content leads to a two-stage ignition process. Char conversion becomes dominant once the volatiles have been depleted, but also causes noticeable deviations of temperature, released mass, and overall particle con- version during devolatilization already, indicating an overlap of the two stages of coal conversion which are usually considered to be consecutive. The complex pyrolysis model leads to non-monotonous profiles of the combustion quantities which introduce a minor dependency of the ignition delay time $τ_{ign}$ on its definition. Regardless of the chosen extraction method, the simulations capture the measured values of $τ_{ign}$ very well

Multicriteria VMAT optimization

Purpose: To make the planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the planner to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus organ at risk sparing. The selected plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal plan is matched almost exactly with the VMAT merging routine, resulting in a high quality plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria planning aspect, which greatly speeds up planning time and allows the user to select the plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT plan. Finally, the user can explore the tradeoff between delivery time and plan quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial planning systems

Stabilization and humanization of a single-chain Fv antibody fragment specific for human lymphocyte antigen CD19 by designed point mutations and CDR-grafting onto a human framework

A single-chain Fv (scFv) fragment derived from the murine antibody 4G7, specific for human lymphocyte CD19, was engineered for stability and expression in Escherichia coli in view of future use as a therapeutic protein. We compared two orthogonal knowledge-based procedures. In one approach, we designed a mutant with 14 single amino-acid substitutions predicted to correct destabilizing residues in the 4G7-wt sequence to create 4G7-mut. In the second variant, the murine CDRs were grafted to the human acceptor framework huVκ3-huVH3, with 11 additional point mutations introduced to obtain a better match between CDR graft and acceptor framework, to arrive at 4G7-graft. Compared to 4G7-wt, 4G7-mut showed greater thermodynamic stability in guanidinium chloride-induced equilibrium denaturation experiments and somewhat greater stability in human serum. The loop graft maintained the comparatively high stability of the murine loop donor, but did not improve it further. Our analysis indicates that this is due to subtle strain introduced between CDRs and framework, mitigating the otherwise highly favorable properties of the human acceptor framework. This slight strain in the loop graft is also reflected in the binding affinities for CD19 on leukemic cells of 8.4 nM for 4G7-wt, 16.4 nM for 4G7-mut and 30.0 nM for 4G7-graft. This comparison of knowledge-based mutation and loop-grafting-based approaches will be important, when moving molecules forward to therapeutic application

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.Comment: 17 pages, 8 figures, 2 table

Determining ‘Age at Death’ for Forensic Purposes using Human Bone by a Laboratory-based Analytical Method

Determination of age-at-death (AAD) is an important and frequent requirement in contemporary forensic science and in the reconstruction of past populations and societies from their remains. Its estimation is relatively straightforward and accurate (±3 years) for immature skeletons by using morphological features and reference tables within the context of forensic anthropology. However, after skeletal maturity (>35 yrs) estimates become inaccurate, particularly in the legal context. In line with the general migration of all the forensic sciences from reliance upon empirical criteria to those which are more evidence-based, AAD determination should rely more-and-more upon more quantitative methods. We explore here whether well-known changes in the biomechanical properties of bone and the properties of bone matrix, which have been seen to change with age even after skeletal maturity in a traceable manner, can be used to provide a reliable estimate of AAD. This method charts a combination of physical characteristics some of which are measured at a macroscopic level (wet & dry apparent density, porosity, organic/mineral/water fractions, collagen thermal degradation properties, ash content) and others at the microscopic level (Ca/P ratios, osteonal and matrix microhardness, image analysis of sections). This method produced successful age estimates on a cohort of 12 donors of age 53–85 yr (7 male, 5 female), where the age of the individual could be approximated within less than ±1 yr. This represents a vastly improved level of accuracy than currently extant age estimation techniques. It also presents: (1) a greater level of reliability and objectivity as the results are not dependent on the experience and expertise of the observer, as is so often the case in forensic skeletal age estimation methods; (2) it is purely laboratory-based analytical technique which can be carried out by someone with technical skills and not the specialised forensic anthropology experience; (3) it can be applied worldwide following stringent laboratory protocols. As such, this technique contributes significantly to improving age estimation and therefore identification methods for forensic and other purposes