Assessment of Aliphatic Based Soot Inception in Laminar Diffusion Flames

Paper presented at 2018 Canadian Society of Mechanical Engineers International Congress, 27-30 May 2018.Soot models are key components of computation fluid dynamic combustion codes that attempt to prescribe how soot is formed. However, due to the complex nature of soot formation, not all pathways may have been fully characterized. This work investigates numerically the influence that an aliphatic-collision (open-chain hydrocarbon) based soot inception model has on soot formation for coflow ethylene/air and methane/air laminar diffusion flames. In the literature, prediction of the soot volume fraction along the centerline of coflow ethylene flames is lacking in accuracy. Similarly for methane flames, soot formation on the wings are under predicted by many models. A new collision based inception model has been developed for specific aliphatics, and applied using an existing framework for molecular collision, in conjunction with pyrene based inception. The purpose of this model is not to be completely fundamental in nature, but more so a proof of concept in that by using physically realistic values for surface reactivity and collision efficiency, this collision mechanism can account for soot formation deficiencies that exist with just polycyclic aromatic hydrocarbon (PAH) based inception. Using this new model, the peak soot volume fraction along the centerline of an ethylene flame can be increased while the peak soot volume fraction along the wings remains unchanged, showing potential to significantly improve the model’s predicative capability. Applying this model to a methane flame has resulted in an increase in the soot volume fraction in both the centerline and the wings, again improving predictive capability

Effect of an off-peak ground pre-cool control strategy on hybrid ground-source heat pump systems

Hybrid Ground-Source Heat Pump (HGSHP) systems have been introduced as an alternate system configuration to remedy the current financial hurdles associated to the installation of geo-exchange technology. However, there still remains potential for increased economic feasibility with the addition of improved system control. This study introduces an operational strategy referred to as an 'Off-Peak Ground Pre-Cool', employing time-of-use conscious operating logic to facilitate artificial bore-field pre-conditioning. Artificially pre-cooling a system's bore-field during an off-peak operating bracket allows for improved thermal characteristics for the following peak period. With improved bore-field thermal characteristics during peak periods, cooling mode operation can be exploited more efficiently, resulting in a reduction in peak power consumption and operating costs. This study presents a preliminary evaluation of the impact the proposed off-peak ground pre-cool strategy has on the operation of a HGSHP system, simulated for a mid-rise multi-residential facility located in Toronto, Canada. Two analyses are presented simulating the strategy's impact as a function of pre-cool duration and hybrid system proportions. This study explores the potential benefit that a proactive bore-field pre-condition poses for the operation of a HGSHP system, intending to concurrently address improving system economics and aid in the balancing of the electrical grid

Modelling of alternative borehole configurations for geo-exchange

During the operation of a ground source heat pump (GSHP), the ground acts as a heat sink and heat source in cooling and heating modes, respectively. When the heating and cooling loads are extremely unbalanced, ground temperature can slowly migrate up or down in the long term, diminishing the GSHP system's performance, and eventually causing the system to fail. This failure occurs when the ground can no longer accept or provide more heat for a building. Therefore, a method to mitigate thermal imbalance is needed. Previous studies in the literature examine the effects of borehole configurations in geo-exchange. However, no study has been done to analyze the effects of varying borehole lengths in a bore field. The objective of this study is to examine the effects on thermal performance from changing the length of individual boreholes while retaining the same total borehole length. In this paper, the four centre boreholes in a 4x4 borehole system were shortened and the length of the remaining boreholes was recalculated to meet the total required ground loop length. A 20 year operation was simulated for a school building model with centre borehole lengths of 100 m, 80 m, and 50 m and separation distances of 3 m, 4 m, and 6 m, to study the benefits of shortening the centre boreholes. The results demonstrate that by adjusting the length of the centre boreholes, separation can be reduced

Characterization of helical steel pile performance under varying soil conditions

Ground-Source Heat Pumps (GSHPs) are a clean alternative to traditional space heating and cooling technologies. GSHPs take advantage of relatively constant ground temperatures as a medium for heat exchange, in contrast to the use of highly variable air temperatures. Conventional systems use a heat pump paired with a borehole heat exchanger to exchange heat with the ground. Widespread use of these systems has been impeded by high initial costs and low short-term return on investment. Helical steel piles (HSP) are structural elements that are drilled into the soil to provide support to buildings. With only minor modifications, these structures have shown promise as a viable alternative to the use of the conventional borehole heat exchanger. At present, there is little understanding of the functionality and the optimal design of HSPs as heat exchangers under different soil properties such as heterogeneity, porosity and saturation content. Therefore, the focus of this paper is to investigate the performance of HSPs under different heterogeneous soil conditions using numerical analysis. This paper presents the results of a numerical study of HSP performance under varying moisture contents

Performance analysis of a single underground thermal storage borehole using phase change material

Ground source heat pumps (GSHP) are used to provide both heating and cooling to a given system. These heat pumps transfer heat efficiently between the system and the ground. Despite this high efficiency, there has been a low adoption rate for GSHPs owing to limited usage in commercial structures and buildings primarily due to high installation costs, but also due to a lack of drilling space and unbalanced heating/cooling loads. Phase change materials (PCMs) can absorb, store and release large amounts of latent heat over a defined narrow temperature range while the material changes phase or state. The main goal of this paper is to be able to predict numerically the performance of a single borehole with the effect of implementing PCMs. In order to successfully proceed with the discussion, two main objectives for this paper are presented. The first objective is to establish a finite element model of a single borehole with accurate assumptions in order to achieve an accurate prediction over four years of operation for a GSHP. Then, the second objective of the paper is to investigate the effect of using PCM in the borehole of GSHP to help maintain a more stable ground temperature range. Two scenarios of different PCM volumes and melting temperatures are presented. It was found that the performance enhancement due to PCMs reaches up to 35% in monthly average COP. In addition, PCMs show great potential to smooth the ground thermal response

Do Femtonewton Forces Affect Genetic Function? A Review

Protein-Mediated DNA looping is intricately related to gene expression. Therefore any mechanical constraint that disrupts loop formation can play a significant role in gene regulation. Polymer physics models predict that less than a piconewton of force may be sufficient to prevent the formation of DNA loops. Thus, it appears that tension can act as a molecular switch that controls the much larger forces associated with the processive motion of RNA polymerase. Since RNAP can exert forces over 20 pN before it stalls, a ‘substrate tension switch’ could offer a force advantage of two orders of magnitude. Evidence for such a mechanism is seen in recent in vitro micromanipulation experiments. In this article we provide new perspective on existing theory and experimental data on DNA looping in vitro and in vivo . We elaborate on the connection between tension and a variety of other intracellular mechanical constraints including sequence specific curvature and supercoiling. In the process, we emphasize that the richness and versatility of DNA mechanics opens up a whole new paradigm of gene regulation to explore.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41816/1/10867_2005_Article_9002.pd

A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame

Particulate matter (soot) emissions from combustion processes have damaging health and environmental effects. Numerical techniques with varying levels of accuracy and computational time have been developed to model soot formation in flames. High-fidelity soot models come with a significant computational cost and as a result, accurate soot modelling becomes numerically prohibitive for simulations of industrial combustion devices. In the present study, an accurate and computationally inexpensive soot-estimating tool has been developed using a long short-term memory (LSTM) neural network. The LSTM network is used to estimate the soot volume fraction (fv) in a time-varying, laminar, ethylene/air coflow diffusion flame with 20 Hz periodic fluctuation on the fuel velocity and a 50% amplitude of modulation. The LSTM neural network is trained using data from CFD, where the network inputs are gas properties that are known to impact soot formation (such as temperature) and the network output is fv. The LSTM is shown to give accurate estimations of fv, achieving an average error (relative to CFD) in the peak fv of approximately 30% for the training data and 22% for the test data, all in a computational time that is orders-of-magnitude less than that of high-fidelity CFD modelling. The neural network approach shows great potential to be applied in industrial applications because it can accurately estimate the soot characteristics without the need to solve the soot-related terms and equations

A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame

Particulate matter (soot) emissions from combustion processes have damaging health and environmental effects. Numerical techniques with varying levels of accuracy and computational time have been developed to model soot formation in flames. High-fidelity soot models come with a significant computational cost and as a result, accurate soot modelling becomes numerically prohibitive for simulations of industrial combustion devices. In the present study, an accurate and computationally inexpensive soot-estimating tool has been developed using a long short-term memory (LSTM) neural network. The LSTM network is used to estimate the soot volume fraction (fv) in a time-varying, laminar, ethylene/air coflow diffusion flame with 20 Hz periodic fluctuation on the fuel velocity and a 50% amplitude of modulation. The LSTM neural network is trained using data from CFD, where the network inputs are gas properties that are known to impact soot formation (such as temperature) and the network output is fv. The LSTM is shown to give accurate estimations of fv, achieving an average error (relative to CFD) in the peak fv of approximately 30% for the training data and 22% for the test data, all in a computational time that is orders-of-magnitude less than that of high-fidelity CFD modelling. The neural network approach shows great potential to be applied in industrial applications because it can accurately estimate the soot characteristics without the need to solve the soot-related terms and equations