Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling with Differential Equations01:25

Modeling with Differential Equations

162
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
162
Typical Model Studies01:30

Typical Model Studies

680
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.
680
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

86
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
86
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

875
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.
875
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

419
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
419
Modeling and Similitude01:12

Modeling and Similitude

713
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...
713

You might also read

Related Articles

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

Sort by
Same author

Evidence for regulation of transpiration in nonstomatal plants: insights from bryophyte gametophytes.

The New phytologist·2026
Same author

Incorporating parameter variability into Monod models of nutrient-limited growth of non-diazotrophic and diazotrophic cyanobacteria.

Environmental microbiology·2022
Same author

Leaf microscopy applications in photosynthesis research: identifying the gaps.

Journal of experimental botany·2022
Same author

On the shape of cicada's wing leading-edge cross section.

Scientific reports·2021
Same author

Aerosol generation with various approaches to oxygenation in healthy volunteers in the emergency department.

Journal of the American College of Emergency Physicians open·2021
Same author

Contribution and consequences of xylem-transported CO<sub>2</sub> assimilation for C<sub>3</sub> plants.

The New phytologist·2019

Related Experiment Video

Updated: Mar 23, 2026

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
14:33

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Published on: October 1, 2013

15.0K

Simulating pH effects in an algal-growth hydrodynamics model(1).

Scott C James1,2, Vijayasarathi Janardhanam3, David T Hanson4

  • 1Sandia National Laboratories, Thermal/Fluid Science and Engineering, Livermore, CA, 94551-0969, USA.

Journal of Phycology
|March 24, 2016
PubMed
Summary

Numerical models optimize algal oil production by simulating growth factors like pH. This approach minimizes resource use and enhances economic competitiveness in algal cultivation.

Keywords:
CE-QUALCO2EFDCbiofuelsmodeling algae growthpH effects

More Related Videos

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases
08:41

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases

Published on: December 19, 2019

11.0K
Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests
10:08

Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests

Published on: June 14, 2017

17.4K

Related Experiment Videos

Last Updated: Mar 23, 2026

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
14:33

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Published on: October 1, 2013

15.0K
Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases
08:41

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases

Published on: December 19, 2019

11.0K
Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests
10:08

Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests

Published on: June 14, 2017

17.4K

Area of Science:

  • Environmental engineering
  • Biotechnology
  • Computational modeling

Background:

  • Numerical simulations offer cost-effective optimization for industrial processes.
  • Algal oil production is a key area for sustainable energy and resource management.
  • Accurate modeling is crucial for maximizing yield and minimizing consumption.

Purpose of the Study:

  • To enhance algal oil production through optimized system operation.
  • To integrate pH as a critical limiting factor in algal growth simulations.
  • To validate a coupled hydrodynamic and algal growth model.

Main Methods:

  • Utilized modified Environmental Fluid Dynamics Code (EFDC) and CE-QUAL water-quality code.
  • Developed a single-layer algal growth/hydrodynamic model incorporating pH limitation.
  • Verified the model against analytical solutions and greenhouse pond experimental data.

Main Results:

  • The model accurately simulates algal biomass and nutrient concentrations.
  • Inclusion of pH as a limiting factor yielded physically reasonable results.
  • Simulated algal production reached a maximum of 1.05 d⁻¹ under optimal conditions.

Conclusions:

  • The enhanced model provides a robust tool for optimizing algal cultivation systems.
  • Accurate simulation of pH is essential for predicting and maximizing algal oil yield.
  • This modeling approach can significantly improve the economic competitiveness of algal biofuel production.