Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modeling and Similitude

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

Typical Model Studies

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

Design Example: Creating a Hydraulic Model of a Dam Spillway

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.

Major Losses in Pipes

Major Losses in Pipes

When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to viscous...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Personalized treatment hierarchies in Bayesian network meta-analysis.

Research synthesis methods·2026

Same author

Iron Overlayers Facilitate Conversion of Al-Si Coatings to Intermetallics during Hot Stamping.

ACS applied materials & interfaces·2025

Same author

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach.

ACS ES&T air·2024

Same author

Raman Spectroscopic Analysis of the Reaction between Al-Si Coatings and Steel.

ACS omega·2023

Same author

Bayesian unanchored additive models for component network meta-analysis.

Statistics in medicine·2022

Same author

Laser-induced incandescence for non-soot nanoparticles: recent trends and current challenges.

Applied physics. B, Lasers and optics·2022

Same journal

Modeling Daily Plume Specific Smoke Concentrations for Health Effects Studies with Estimates of Fire Size, Plume Age, and Fuel Type.

ACS ES&T air·2026

Same journal

Enhanced Isoprene Secondary Organic Aerosol Formation with C<sub>5</sub>-alkene Triols Newly Added to Current Chemical Mechanisms.

ACS ES&T air·2026

Same journal

Controlled-Release Experiment to Optimize Emission Quantification of H<sub>2</sub> Point Sources.

ACS ES&T air·2026

Same journal

Assessing Black Carbon and Iron Oxide Aerosols: A Comparative Study between Urban and Rural Environments in the Southeastern U.S.

ACS ES&T air·2026

Same journal

Source Contributions to Brown Carbon Absorption in Toronto Air during the Wildfire Season.

ACS ES&T air·2026

Same journal

Nonlinear Contributions of NO <sub><i>x</i></sub> and Volatile Chemical Products to Air Pollution and the Associated Acute Premature Mortality.

ACS ES&T air·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

Efficient Simulation of a Leak-Detection-and-Repair Program.

Christiane Lemieux¹, Kyle J Daun², Augustine Wigle³

¹Department of Statistics and Actuarial Science, University of Waterloo, Waterloo N2L 3G1, Canada.

|February 19, 2026

Summary

This summary is machine-generated.

Monte Carlo (MC) simulations improve methane emission estimates for oil and gas leak detection and repair (LDAR) programs. An event-driven simulation with low-discrepancy sampling significantly reduces estimation errors compared to traditional methods.

Keywords:

FEMP LDAR leak detection methane methane emissions oil and gas quasi-Monte Carlo uncertainty

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Related Experiment Videos

Last Updated: Jun 23, 2026

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Area of Science:

Environmental Science
Computational Science
Energy Systems

Background:

Monte Carlo (MC) simulations are widely used for estimating methane emissions and cost-effectiveness of leak detection and repair (LDAR) programs in the oil and gas sector.
Current simulation techniques may suffer from high variance, limiting the statistical certainty of results.
Efficient simulation methods are crucial for accurate assessment of environmental impact and program efficacy.

Purpose of the Study:

To develop and demonstrate an efficient simulation methodology for estimating methane emissions and LDAR program cost-effectiveness.
To reduce the variance of estimators compared to contemporary simulation techniques.
To enhance the statistical power of analyses for LDAR program effectiveness.

Main Methods:

Implementation of an event-driven simulation approach, moving away from daily simulations.
Utilization of low-discrepancy sampling instead of plain random sampling for scenario generation.
Application of the proposed method to a simplified LDAR program model for sensitivity analysis.

Main Results:

The proposed simulation method yields estimators with significantly smaller variance.
Error reduction by factors of approximately 3 to 4 was observed for the same computation time in a sensitivity analysis.
The enhanced precision allows for more conclusive statistical evidence regarding LDAR program impact on emissions.

Conclusions:

The developed simulation approach offers a more efficient and precise method for evaluating LDAR programs.
Event-driven simulation and low-discrepancy sampling enhance the reliability of methane emission and cost-effectiveness estimates.
This methodology provides stronger statistical support for decision-making in environmental management within the oil and gas industry.