A DI Diesel Combustion and Emission Predictive Capability for Use in Cycle Simulation

1992 ◽  
Author(s):  
Z. Bazari
Keyword(s):  
Diesel Combustion ◽  
Predictive Capability ◽  
Cycle Simulation

Modeling of Ion Current Signal in Diesel Combustion

2013 ◽  
Author(s):  
Fadi Estefanous
Keyword(s):  
Internal Combustion Engines ◽  
Combustion Process ◽  
Operating Conditions ◽  
Ion Current ◽  
Ionization Mechanism ◽  
Diesel Combustion ◽  
Current Signal ◽  
Three Dimensional Model ◽  
Cycle Simulation ◽  
Soot Precursors

Ionization in internal combustion engines produces a signal indicative of in-cylinder conditions that can be used for the feedback electronic control of the engine, to meet production goals in performance, fuel economy and emissions. Most of the research has been conducted on carbureted and port injection spark ignition engines where the ionization mechanisms are well defined. A limited number of investigations have been conducted on ionization in diesel engines because of its complex combustion process. In this study, a detailed ionization mechanism is developed and introduced in a 3-D diesel cycle simulation computational fluid dynamics (CFD) code to determine the contribution of different species in the ionization process at different engine operating conditions. The CFD code is coupled with DARS-CFD, another module used to allow chemical kinetics calculations. The three-dimensional model accounts for the heterogeneity of the charge and the resulting variations in the combustion products. Furthermore, the model shows the effects of varying fuel injection pressure and engine load on the ion current signal characteristics. Ion current traces obtained experimentally from a heavy duty diesel engine were compared to the 3-D model results. The results of the simulation indicate that some heavy hydrocarbons, soot precursors play a major role, in addition to the role of NOx in ionization in diesel combustion.

scholarly journals Empirical soot formation and oxidation model

2009 ◽  
Vol 13 (3) ◽  
pp. 35-46 ◽  
Author(s):  
Karima Boussouara ◽  
Mahfoud Kadja
Keyword(s):  
Internal Combustion Engines ◽  
Diffusion Flames ◽  
Soot Formation ◽  
Internal Combustion ◽  
Combustion Model ◽  
Diesel Combustion ◽  
Combustion Engines ◽  
Particulate Emission ◽  
Cycle Simulation ◽  
Air Mixing

Modelling internal combustion engines can be made following different approaches, depending on the type of problem to be simulated. A diesel combustion model has been developed and implemented in a full cycle simulation of a combustion, model accounts for transient fuel spray evolution, fuel-air mixing, ignition, combustion, and soot pollutant formation. The models of turbulent combustion of diffusion flame, apply to diffusion flames, which one meets in industry, typically in the diesel engines particulate emission represents one of the most deleterious pollutants generated during diesel combustion. Stringent standards on particulate emission along with specific emphasis on size of emitted particulates have resulted in increased interest in fundamental understanding of the mechanisms of soot particulate formation and oxidation in internal combustion engines. A phenomenological numerical model which can predict the particle size distribution of the soot emitted will be very useful in explaining the above observed results and will also be of use to develop better particulate control techniques. A diesel engine chosen for simulation is a version of the Caterpillar 3406. We are interested in employing a standard finite-volume computational fluid dynamics code, KIVA3V-RELEASE2.

A Plan to Develop Predictive Capability for Equatorial Scintillation Storms.

1997 ◽  
Author(s):  
Michael Mendillo ◽  
Jules Aarons
Keyword(s):  
Predictive Capability

Developing a Predictive Capability for Bioluminescence Signatures

2009 ◽  
Author(s):  
Michael I. Latz ◽  
Grant Deane ◽  
M. D. Stokes ◽  
Mark Hyman
Keyword(s):  
Predictive Capability

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

Research on Protection Methods of Embedded Operating System Software Based on Hardware Compiler

2013 ◽  
Vol 443 ◽  
pp. 556-560
Author(s):  
Gao Ming He
Keyword(s):  
Operating System ◽  
Practical Significance ◽  
Embedded Operating System ◽  
System Software ◽  
Software Protection ◽  
Performance Loss ◽  
Cycle Simulation ◽  
Protection Technology ◽  
Program Flow ◽  
High Level

This paper describes a system; CODESSEAL can provide protection and evaluation to system software. CODESSEAL was designed to protect embedded systems with sufficient expertise and resources to capture attack equipment and manipulator, not only to protect software but also to protect hardware. By using the reconfigurable hardware allows CODESSEAL to provide confidentiality, integrity of security services and a platform-independent program flow without having to redesign the processor. System uses software and data protection technology and designs cycle simulation methods for data analysis. Experimental results show that the protected instructions and data with a high level of safety can be realized a low, which in most cases the performance loss can be reduced to below 10%, so the research of software protection methods of the embedded operating system of hardware compiler has important practical significance.

scholarly journals Comparison of pretherapeutic osseous tumor volume and standard hematology for prediction of hematotoxicity after PSMA-targeted radioligand therapy

2021 ◽  
Author(s):  
Liam Widjaja ◽  
Rudolf A. Werner ◽  
Tobias L. Ross ◽  
Frank M. Bengel ◽  
Thorsten Derlin
Keyword(s):  
Multivariate Analysis ◽  
Tumor Volume ◽  
Predictive Performance ◽  
Castration Resistant Prostate Cancer ◽  
Operating Characteristics ◽  
Predictive Capability ◽  
Radioligand Therapy ◽  
Castration Resistant ◽  
Pet Ct ◽  
Late Stages

Abstract Purpose Hematotoxicity is a potentially dose-limiting adverse event in patients with metastasized castration-resistant prostate cancer (mCRPC) undergoing prostate-specific membrane antigen (PSMA)-directed radioligand therapy (RLT). We aimed to identify clinical or PSMA-targeted imaging-derived parameters to predict hematological adverse events at early and late stages in the treatment course. Methods In 67 patients with mCRPC scheduled for 177Lu-PSMA-617 RLT, pretherapeutic osseous tumor volume (TV) from 68Ga-PSMA-11 PET/CT and laboratory values were assessed. We then tested the predictive capability of these parameters for early and late hematotoxicity (according to CTCAE vers. 5.0) after one cycle of RLT and in a subgroup of 32/67 (47.8%) patients after four cycles of RLT. Results After one cycle, 10/67 (14.9%) patients developed leukocytopenia (lymphocytopenia, 39/67 [58.2%]; thrombocytopenia, 17/67 [25.4%]). A cut-off of 5.6 × 103/mm3 for baseline leukocytes was defined by receiver operating characteristics (ROC) and separated between patients with and without leukocytopenia (P < 0.001). Baseline leukocyte count emerged as a stronger predictive factor in multivariate analysis (hazard ratio [HR], 33.94, P = 0.001) relative to osseous TV (HR, 14.24, P = 0.01). After four cycles, 4/32 (12.5%) developed leukocytopenia and the pretherapeutic leukocyte cut-off (HR, 9.97, P = 0.082) tended to predict leukocytopenia better than TV (HR, 8.37, P = 0.109). In addition, a cut-off of 1.33 × 103/mm3 for baseline lymphocytes separated between patients with and without lymphocytopenia (P < 0.001), which was corroborated in multivariate analysis (HR, 21.39, P < 0.001 vs. TV, HR, 4.57, P = 0.03). After four cycles, 19/32 (59.4%) developed lymphocytopenia and the pretherapeutic cut-off for lymphocytes (HR, 46.76, P = 0.007) also demonstrated superior predictive performance for late lymphocytopenia (TV, HR, 5.15, P = 0.167). Moreover, a cut-off of 206 × 103/mm3 for baseline platelets separated between patients with and without thrombocytopenia (P < 0.001) and also demonstrated superior predictive capability in multivariate analysis (HR, 115.02, P < 0.001 vs.TV, HR, 12.75, P = 0.025). After four cycles, 9/32 (28.1%) developed thrombocytopenia and the pretherapeutic cut-off for platelets (HR, 5.44, P = 0.048) was also superior for the occurrence of late thrombocytopenia (TV, HR, 1.44, P = 0.7). Conclusions Pretherapeutic leukocyte, lymphocyte, and platelet levels themselves are strong predictors for early and late hematotoxicity under PSMA-directed RLT, and are better suited than PET-based osseous TV for this purpose.

scholarly journals A New Method to Determine the Impact of Individual Field Quantities on Cycle-to-Cycle Variations in a Spark-Ignited Gas Engine

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4136
Author(s):  
Clemens Gößnitzer ◽  
Shawn Givler
Keyword(s):  
Kinetic Energy ◽  
Flow Field ◽  
Turbulent Kinetic Energy ◽  
Negative Impact ◽  
Operating Conditions ◽  
Engine Operation ◽  
Gas Engine ◽  
Cycle Simulation ◽  
Simulation Results ◽  
The Impact

Cycle-to-cycle variations (CCV) in spark-ignited (SI) engines impose performance limitations and in the extreme limit can lead to very strong, potentially damaging cycles. Thus, CCV force sub-optimal engine operating conditions. A deeper understanding of CCV is key to enabling control strategies, improving engine design and reducing the negative impact of CCV on engine operation. This paper presents a new simulation strategy which allows investigation of the impact of individual physical quantities (e.g., flow field or turbulence quantities) on CCV separately. As a first step, multi-cycle unsteady Reynolds-averaged Navier–Stokes (uRANS) computational fluid dynamics (CFD) simulations of a spark-ignited natural gas engine are performed. For each cycle, simulation results just prior to each spark timing are taken. Next, simulation results from different cycles are combined: one quantity, e.g., the flow field, is extracted from a snapshot of one given cycle, and all other quantities are taken from a snapshot from a different cycle. Such a combination yields a new snapshot. With the combined snapshot, the simulation is continued until the end of combustion. The results obtained with combined snapshots show that the velocity field seems to have the highest impact on CCV. Turbulence intensity, quantified by the turbulent kinetic energy and turbulent kinetic energy dissipation rate, has a similar value for all snapshots. Thus, their impact on CCV is small compared to the flow field. This novel methodology is very flexible and allows investigation of the sources of CCV which have been difficult to investigate in the past.

scholarly journals Free Energy of Metals from Quasi-Harmonic Models of Thermal Disorder

Metals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 195
Author(s):  
Pavel A. Korzhavyi ◽  
Jing Zhang
Keyword(s):  
Free Energy ◽  
First Principles ◽  
Density Functional ◽  
Helmholtz Free Energy ◽  
Complex Materials ◽  
Predictive Capability ◽  
Temperature Dependent ◽  
Disordered Alloys ◽  
Thermal Disorder ◽  
Structure Calculations

A simple modeling method to extend first-principles electronic structure calculations to finite temperatures is presented. The method is applicable to crystalline solids exhibiting complex thermal disorder and employs quasi-harmonic models to represent the vibrational and magnetic free energy contributions. The main outcome is the Helmholtz free energy, calculated as a function of volume and temperature, from which the other related thermophysical properties (such as temperature-dependent lattice and elastic constants) can be derived. Our test calculations for Fe, Ni, Ti, and W metals in the paramagnetic state at temperatures of up to 1600 K show that the predictive capability of the quasi-harmonic modeling approach is mainly limited by the electron density functional approximation used and, in the second place, by the neglect of higher-order anharmonic effects. The developed methodology is equally applicable to disordered alloys and ordered compounds and can therefore be useful in modeling realistically complex materials.

Sign in / Sign up

Export Citation Format

Share Document