7,077 research outputs found
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
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation Using Economic Variables and Indices
The capability of various machine learning techniques in predicting construction project profit in residential buildings using a combination of economic variables and indices (EV&Is) and physical and financial variables (P&F) as input variables remain uncertain. Although recent studies have primarily focused on identifying the factors influencing the sales of construction projects due to their significant short-term impact on a country's economy, the prediction of these parameters is crucial for ensuring project sustainability. While techniques such as regression and artificial neural networks have been utilized to estimate construction project sales, limited research has been conducted in this area. The application of machine learning techniques presents several advantages over conventional methods, including reductions in cost, time, and effort. Therefore, this study aims to predict the sales valuation of construction projects using various machine learning approaches, incorporating different EV&Is and P&F as input features for these models and subsequently generating the sales valuation as the output. This research will undertake a comparative analysis to investigate the efficiency of the different machine learning models, identifying the most effective approach for estimating the sales valuation of construction projects. By leveraging machine learning techniques, it is anticipated that the accuracy of sales valuation predictions will be enhanced, ultimately resulting in more sustainable and successful construction projects. In general, the findings of this research reveal that the extremely randomized trees model delivers the best performance, while the decision tree model exhibits the least satisfactory performance in predicting the sales valuation of construction projects
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data
Stock market forecasting has been a challenging part for many analysts and
researchers. Trend analysis, statistical techniques, and movement indicators
have traditionally been used to predict stock price movements, but text
extraction has emerged as a promising method in recent years. The use of neural
networks, especially recurrent neural networks, is abundant in the literature.
In most studies, the impact of different users was considered equal or ignored,
whereas users can have other effects. In the current study, we will introduce
TM-vector and then use this vector to train an IndRNN and ultimately model the
market users' behaviour. In the proposed model, TM-vector is simultaneously
trained with both the extracted Twitter features and market information.
Various factors have been used for the effectiveness of the proposed
forecasting approach, including the characteristics of each individual user,
their impact on each other, and their impact on the market, to predict market
direction more accurately. Dow Jones 30 index has been used in current work.
The accuracy obtained for predicting daily stock changes of Apple is based on
various models, closed to over 95\% and for the other stocks is significant.
Our results indicate the effectiveness of TM-vector in predicting stock market
direction.Comment: 24 pag
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Machine learning applications in search algorithms for gravitational waves from compact binary mergers
Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe.
However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing.
In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software.
Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals
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