Category Archives: Engineering Energy Efficiency

Modeling solar thermal power using heliostats in Energy2D

An array of heliostats in Energy2D (online simulation)
A new class of objects was added in Energy2D to model what is called a heliostat, a device that can automatically turn a mirror to reflect sunlight to a target no matter where the sun is in the sky. Heliostats are often used in solar thermal power plants or solar furnaces that use mirrors. With an array of computer-controlled heliostats and mirrors, the energy from the sun can be concentrated on the target to heat it up to a very high temperature, enough to vaporize water to create steam that drives a turbine to generate electricity.

Image credit: Wikipedia
The Ivanpah Solar Power Facility in California's Mojave Desert, which went online on February 13, 2014, is currently the world's largest solar thermal power plant. With a gross capacity of 392 megawatts, it is enough to power 140,000 homes. It deploys 173,500 heliostats, each controlling two mirrors.

A heliostat in Energy2D contains a planar mirror mounted on a pillar. You can drop one in at any location. Once you specify its target, it will automatically reflect any sunlight beam hitting on it to the target.

Strictly speaking, heliostats are different from solar trackers that automatically face the sun like sunflowers. But in Energy2D, if no target is specified, as is the default case, a heliostat becomes a solar tracker. Unlike heliostats, solar trackers are often used with photovoltaic (PV) panels that absorb, instead of reflecting, sunlight that shine on them. A future version of Energy2D will include the capacity of modeling PV power plants as well.

Visualizing the "thermal breathing" of a house in 24-hour cycle with Energy3D

The behavior of a house losing or gaining thermal energy from the outside in a 24-hour cycle, when visualized using Energy3D's heat flux view, resembles breathing, especially in the transition between seasons in which the midday can be hot and the midnight can be cold. We call this phenomenon the "thermal breathing" of a house. This embedded YouTube video in this blog post illustrates this effect. For the house shown in the video, the date was set to be May 1st and the location is set to Santa Fe, New Mexico.

This video only shows the daily thermal breathing of a house. Considering the seasonal change of temperature, we may also definite a concept "annual thermal breathing," which describes this behavior on an annual basis.

This breathing metaphor may help students build a more vivid mental picture of the dynamic heat exchange between a house and the environment. Interestingly, it was only after I realized this thermal visualization feature in Energy3D that this metaphor came to my mind. This experience reflects the importance of doing in science and engineering: Ideas often do not emerge until we get something concrete done. This process of externalization of thinking is critically important to the eventual internalization of ideas or concepts.

Using particle feeders in Energy2D for advection simulations

Fig. 1: Particle advection behind two obstacles.
Advection is a transport mechanism in which a substance is carried by the flow of a fluid. An example is the transport of sand in a river or pollen in the air. Advection is different from diffusion, whereas the more commonly known term, convection, is the combination of advection and diffusion.

Our Energy2D can simulate advection as it integrates particle dynamics in the Lagrangian frame and fluid dynamics in the Eulerian frame. Particles in Energy2D do not spontaneously diffuse -- they are driven by gravity or fluid, though we can introduce Brownian particles in the future by incorporating the Langevin Equation into Energy2D.

Fig. 2: Blowing away particles.
Over this weekend, I added a new object, the particle feeder, for creating continuous particle flow in the presence of open mass boundary. A particle feeder can emit a specified type of particle at a specified frequency. All these settings can be adjusted in its property window, which can be opened by right-clicking on it and selecting the relevant menu.

Figure 1 shows a comparison of particle advection behind a turbulent flow and a streamlined flow. Have you ever seen these kinds of patterns in rivers?

Figure 2 shows how particles of different densities separate when you blow them with a fan. There are six particle feeders at the top that continually drop particles. A fan is placed not far below the feeders.

With these new additions to Energy2D, we hope to be able to simulate more complex atmospheric phenomena (such as pollutant transport through jet streams) in the future.

A 16-year-old’s designs with Energy3D

This post needs no explanation. The images say it all.

All these beautiful structures were designed from scratch (NOT imported from other sources) by Cormac Paterson using our Energy3D CAD software.

He is only 16 years old. (We have his parents' permission to reveal his name and his work.)

Using fans to create fluid flows in Energy2D

Fig. 1: Swirling flows form between two opposite fans.
A new type of object, "fan", has been added to Energy2D to create and control fluid flows. This fan replaces the original implementation of fan that assigns a velocity to a solid part (which doesn't allow the fluid to flow through). For the CFD folks who are reading this post, this is equivalent to an internal velocity boundary.

To add a fan to the scene, use the Insert Menu to drop a fan to the last clicked location. You can then drag it anywhere and resize it any way. By default, the velocity of a fan is zero. You will need to set its velocity in the popup window that can be opened using the right-click popup menu. Currently, however, rotation has not been implemented, so a fan can only blow in four directions: left, right, up, or down -- the direction depends on the aspect ratio of the fan's shape and the value of the velocity.

Fig. 2: Eddy formation in a hole.
With this new feature, we can create a directional flow in Energy2D to simulate things such as a river or wind field. Then we can easily simulate various kinds of eddy flow and visualize them using the streamline feature of Energy2D.

For example, Figure 1 shows the continuous formation of swirling flows between two fans that blow wind in the opposite direction. If you move the fans further apart, you will find that the swirling pattern will not form. Could the mechanism shown in this simulation be related to the formation of certain types of twisters?

Fig. 3: Eddy formation behind a fin.
Figures 2 and 3 show the formation of an eddy in a hole and behind an obstacle, respectively. These eddies are common in fast-flowing rivers. Experienced fishermen know there is a higher chance to find fish in these eddies.

Accurate prediction of solar radiation using Energy3D: Part III

Predicted and measured average daily insolation for 80 cities.
In Parts I and II, we have documented our progress on solar radiation modeling with our Energy3D CAD software. In the past few weeks, our summer interns Siobhan Bailey from Rensselaer Polytechnic Institute and Shiyan Jiang from University of Miami, and I have collected data for 167 worldwide locations. We analyzed 100 US locations among them and compared the insolation data calculated by Energy3D for a horizontal surface and a south-face vertical surface with 30 years of data collected by the US Department of Energy. The results show that, on average, the calculated mean daily insolation is within ±14% of error range compared with the measured results for a horizontal surface and ±10% of error range compared with the measure results for a south-facing vertical surface, respectively. The calculation of the average accuracy is based on both temporal data of 12 months over a year and spatial data of 100 locations in the US.

With this crystal ball in the hand to predict solar radiation anywhere anytime with a reasonable accuracy, Energy3D can be used by professional engineers for real-world applications related to solar energy, such as passive solar architecture, urban planning, solar park optimization, solar thermal power plants, and so on. Stay tuned for our future reports of those applications.

Go to Part I and Part II.

Scanning radiation flux with moving sensors in Energy2D

Figure 1: Moving sensors facing a rectangular radiator.
The heat flux sensor in Energy2D can be used to measure radiative heat flux, as well as conductive and convective heat fluxes. Radiative heat flux depends on not only the temperature of the object the sensor measures but also the angle at which it faces the object. The latter is known as the view factor.

In radiative heat transfer, a view factor between two surfaces A and B is the proportion of the radiation which leaves surface A that strikes surface B. If the two surfaces face each other directly, the view factor is greater than the case in which they do not. If the two surfaces are closer, the view factor is greater.

Figure 2: Rotating sensors inside and outside a ring radiator.
To conveniently visualize the effect of a view factor, Energy2D allows you to attach a heat flux sensor to a moving or rotating particle, with a settable linear or angular velocity. In this way, we can set up sensors to automatically "scan" the field of radiation heat flux like a radar.

Figure 1 shows a moving sensor and a rotating sensor, as well as the data they record. A third sensor is also placed to the right of an object that is being heated by the radiator. This object has an emissivity of one so it also radiates. Its radiation flux is recorded by the third sensor whose data shows a slowly increasing heat flux as the object slowly warms up.

As an interesting test case, Figure 2 shows two rotating sensors, one placed precisely at the center of a ring radiator and the other outside. The almost steady line recorded by the first sensor suggests that the view factor at the center does not change, which makes sense. The small sawtooth shape is due to the limitation of discretization in our numerical simulation.

Simulating PTC and NTC heating elements with Energy2D

Figure 1: A demo simulation.
A heating element converts electricity into heat through Joule heating: Electric current passing through the element encounters resistance, causing the temperature of the element to rise. A thermistor is a type of resistor whose resistance changes significantly with temperature. In a heating element that uses a thermistor with a positive temperature coefficient (PTC), called a PTC heating element, the temperature increases rapidly. In a heating element that uses a thermistor with a negative temperature coefficient (NTC), called a NTC heating element, the heating will gradually weaken when the temperature increases.

Figure 2: Setting the temperature coefficient.
Several Energy2D users have requested adding PTC/NTC controls to the software. So this was added last night. You can now set the temperature coefficient while defining a power source, as shown in Figure 2.

Figure 1 shows the comparison of the temperature increasing in a PTC heater, a constant-power heater, and a NTC heater, with the temperature coefficients being 0.1, 0, and -0.1, respectively. Note that in the case of constant power, the temperature increases linearly in time (as per the definition of constant power), whereas PTC and NTC exhibit nonlinear behaviors.

You can click the link under the image to run the simulation yourself.

Design replay: Reconstruction of students’ engineering design processes from Energy3D logs

One of the useful features of our Energy3D software is the ability to record the entire design process of a student behind the scenes. We call the reconstruction of a design process from fine-grained process data design replay.

Design replay is not a screencast technology. The main difference is that it records a sequence of CAD models, not in any video format such as MP4. This sequence is played back in the original CAD tool that generated it, not in a video player. As such, every snapshot model is fully functional and editable. For instance, a viewer can pause the replay and click on the user interface of the CAD tool to obtain or visualize more information, if necessary. In this sense, design replay can provide far richer information than screencast (which records as much information as the pixels in the recording screen permit).

Design replay provides a convenient method for researchers and teachers to quickly look into students' design work. It compresses hours of student work into minutes of replay without losing any important information for analyses. Furthermore, the reconstructed sequence of design can be post-processed in many ways to extract additional information that may shed light on student learning, as we can use any model in the recorded sequence to calculate any of its properties.

The three videos embedded in this post show the design replays of three students' work from a classroom study that we just completed yesterday in a Massachusetts high school. Sixty-seven students spent approximately two weeks designing zero-energy houses -- a zero-energy house is a highly energy-efficient house that consumes net zero (or even negative) energy over a year due to its use of passive and active solar technologies to conserve and generate energy. These videos may give you a clue how these three students solved the design challenge.

Towards a multiphysics Energy2D

Figure 1: Particle motions driven by convective flow.
Up to yesterday, our Energy2D software has been a program for simulating, mostly, fluid and heat flows. But there are also objects in the world that are not fluids. To simulate that part of the world, we have to incorporate some other physics. A simple addition is to couple particles with fluids. This technique is commonly known as discrete phase modeling in the CFD community. It is used to model things such as suspension particles in fluids.

Figure 2: Heat traces of fireballs.
The latest version of Energy2D has a particle solver and a particle editor. Particles in Energy2D observe collision dynamics among themselves and interact with fluid and heat flows: particles can not only be moved by the fluid but also exert reaction force and transfer heat to the fluid. Figure 1 shows the motion of two types of particles driven by a convective flow. Depending on its density (relative to the fluid density), a particle may be buoyant enough to flow with the fluid or so heavy that it must sink to the bottom. This is shown in Figure 1: The black particles are the heavy ones and the white ones are the light ones; the convective force is not strong enough to move the black ones.

Particles can also transfer physical properties such as energy and momentum to the fluid while they are moving. Figure 2 shows the heat traces left by fireballs of different sizes.

Figure 3: Thermophoresis (Soret's effect)
With this new capacity, we can simulate phenomena such as thermophoresis, in which the different particle types in a mixture respond to a temperature gradient differently and thereby can be separated by just heating them up.

If you are enticed enough to want to see these simulations at work, click the links below the figures.

These new features represent an overdue step towards making Energy2D a versatile multiphysics simulation system. For engineering simulations, multiphysics is essential as real-world problems are often complicated by more than one mechanisms, each driven by its own physics.

The particle dynamics shown here is very simple (just a weekend's work). In the long run, I expect that a generic contact dynamics engine such as that of Box2D will be implemented in Energy2D. Coupling the Eulerian and Lagrangian reference frames, this integration will make Energy2D more interesting and useful. That would be a critical step towards our goal for Energy2D to simulate as many energy-related natural phenomena as possible.