1,165 research outputs found
Addiction in context
The dissertation provides a comprehensive exploration of the interplay between social and cultural factors in substance use, specifically focusing on alcohol use disorder (AUD) and cannabis use disorder (CUD). It begins by introducing the concept of social plasticity, which posits that adolescents' susceptibility to AUD is influenced by their heightened sensitivity to their social environment, but this sensitivity increases the potential for recovery in the transition to adulthood.A series of studies delves into how social cues impact alcohol craving and consumption. One study using functional magnetic resonance imaging (fMRI) investigated social alcohol cue reactivity and its relationship to social drinking behavior, revealing increased craving but no significant change in brain activity in response to alcohol cues. Another fMRI study compared social processes in alcohol cue reactivity between adults and adolescents, showing age-related differences in how social attunement affects drinking behavior. Shifting focus to cannabis, this dissertation discusses how cultural factors, including norms, legal policies, and attitudes, influence cannabis use and processes underlying CUD. The research presented examined various facets of cannabis use, including how cannabinoid concentrations in hair correlate with self-reported use, the effects of cannabis and cigarette co-use on brain reactivity, and cross-cultural differences in CUD between Amsterdam and Texas. Furthermore, the evidence for the relationship between cannabis use, CUD, and mood disorders is reviewed, suggesting a bidirectional relationship, with cannabis use potentially preceding the onset of bipolar disorder and contributing to the development and worse prognosis of mood disorders and mood disorders leading to more cannabis use
CFD Modelling of the Mixture Preparation in a Modern Gasoline Direct Injection Engine and Correlations with Experimental PN Emissions
A detailed 3D CFD analysis of a modern gasoline direct injection (GDI) engine is carried
out to reveal the connections between pre-combustion mixture indicators and PN emissions.
Firstly, a novel calibration methodology is introduced to accurately predict the widely
used characteristics of the high-pressure fuel spray. The methodology utilised the Siemens
STAR-CD 3D CFD software environment and employed a combination of statistical and
optimization methods supported by experimental data. The calibration process identified dominant
factors influencing spray properties and established their optimal levels. The two most
used models for fuel atomisation were investigated. The Kelvin–Helmholtz/Rayleigh–Taylor
(KH–RT) and Reitz–Diwakar (RD) break-up models were calibrated in conjunction with
the Rosin–Rammler (RR) mono-modal droplet size distribution. RD outperformed KH–RT
in terms of prediction when comparing numerical spray tip penetration and droplet size
characteristics to the experimental counterparts. Then, the modelling protocol incorporated
droplet-wall interaction models and a multi-component surrogate fuel blend model. The
comprehensive digital model was validated using published data and applied to a modern
small-capacity GDI engine. The study explored various engine operating conditions and
highlights the contribution of fuel mal-distribution and liquid film retention at spark timing
to Particle Number (PN) emissions. Finally, a novel surrogate model was developed to
predict the engine-out PN. An extensive CFD analysis was conducted considering part-load
operating conditions and variations of engine control variables. The PN surrogate model
was developed using an Elastic Net (EN) regression technique, establishing relationships
between experimental PN emission levels and modelled, pre-combustion, air-fuel mixture
quality indicators. The approach enabled the reliable prediction of engine sooting tendencies
without relying on complex measurements of combustion characteristics. These research
efforts aim to enhance engine efficiency, reduce emissions, and contribute to the development
of a reliable and cost-effective digital toolset for engine development and diagnostics
Numerical simulation of surfactant flooding with relative permeability estimation using inversion method
Surfactant flooding attracts significant interest in the hydrocarbon industry, with a definite
promise to improve oil recovery from depleting oil reserves. In this thesis, surfactant flooding
is the primary area of focus as it has significant potential for integration with other chemical
enhanced oil recovery techniques, including polymer, nanofluid, alkali, and foam. This
combined approach has the potential to reduce interfacial tension to ultralow levels, decrease
adsorption, and offer other benefits. However, due to the various mechanism, surfactant
flooding poses a more complex model for simulators by encountering numerical issues (e.g.,
the appearance of spurious oscillations, erratic pulses, and numerical instabilities), rendering
the methods ineffective. To address these challenges, the analytical modelling technique of
surfactant flooding was studied, leading to the development of a novel inversion method in the
MATLAB programming environment.
Numerical accuracy issues were discovered in 1D models that used typical cell sizes found in
well-scale models, leading to pulses in the oil bank and a dip in water saturation, particularly
for low levels of adsorption, highlighting the need for more refined models. Based on these
findings, we examined the surfactant flooding technique in 2D models to recover viscous oil
in short reservoir aspect ratios. Instabilities such as viscous fingering and gravity tongue were
observed on the flood fronts, and the magnitude of the viscous fingers was influenced by
vertical dispersion, resulting in errors in computed mobility values at the fronts. Interestingly,
introducing heterogeneity only minimally affected the spreading of the front and did not
significantly impact viscous fingering or numerical artifacts. To optimize the nonlinearity of
flow behaviour and degree of mobility control at the fronts, a homogenous model was
considered to develop the inversion method.
In summary, the developed inversion method accurately estimated the two-phase relative
permeability curves, which were validated using fractional flow theory. The precision of the
inverted curves was further improved using the optimization algorithm, demonstrating the
method's ability to predict outcomes closer to the observed values for 2D models with
instabilities. The obtained results are of significant value for core flood analysis, interpretation,
matching, and upscaling, providing insights into the potential of surfactant flooding for
enhanced oil recovery. Additionally, the use of the developed MATLAB Scripts promotes open
innovation and reproducibility, contributing to the benchmarking, analytical, and numerical
method development exercises for tutorials aimed at improving the overall understanding of
surfactant flooding
Integrating Traditional and Close Range Photogrammetric Bathymetric Reconstructions to Enhance Predictions of Fish Abundance and Distribution on the NSW Coast
The physical structure of marine habitat is a key determinant of the distribution and abundance of marine biota. Photogrammetry is a new method of obtaining bathymetric reconstructions using overlapping imagery. It is associated with several potential improvements over traditional bathymetric reconstruction methods (e.g., hydroacoustic and optical remote sensing), including finer resolutions, 3D mesh surfaces, and novel metrics of structural complexity. However, the greater cost of photogrammetric data collection requires evaluation of its purported benefits to marine research.
This thesis objectively assessed the potential for photogrammetry to improve predictions of marine biota abundance and distribution. Chapter 2 undertook a quantitative review and metanalysis of latest research and the relative performance of metrics. It indicated common metrics, e.g., surface-rugosity, may not always be the best performing. Chapter 3 systematically explored the relationships between metrics derived from common bathymetric reconstructions and reduced a 2,000 predictor dataset to 100 predictors, whilst maximising information captured.
Metric relative performance was assessed in Chapter 4. Photogrammetric metrics contributed to 22 / 35 fish species and 10 / 15 trophic-mobility group best performing abundance models and helped explain a third more variability compared to traditional methods. Chapter 5 extrapolated (‘engineered’) broad-scale photogrammetric metrics from traditional metrics to help alleviate the cost of photogrammetry. Using an independent dataset, the variance 26 / 50 fish species distribution models was explained best when engineered photogrammetric metrics were included.
These findings help confirm the purported benefits to marine research associated with photogrammetric metrics, which would likely improve predictions of the distribution and abundance of fish, and likely other marine biota, across Australia and worldwide. Engineered metrics would allow greater model performance to be translated to broad-extents required by marine spatial prioritisation, conservation and management. Notably, traditional metrics were important for some fish species and groups, and future studies should seek to combine these methods wherever possible
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