Tag Archives: Computer-aided design

3D model of Ulm Minster created in just one day using Energy3D

Although our Energy3D software is billed as a piece of building simulation and engineering software, it has also become a powerful tool for constructing 3D models of buildings. With even more enhancements in the latest version (v 5.3.2), users can create incredibly complex structures in a short time.

Guanhua Chen, a graduate student from the University of Miami in Florida who joined my team this week as a summer intern, created an unbelievably detailed model of Ulm Minster -- in JUST ONE DAY. In total, his model has 373 elements.

Considering that he is very new to Energy3D (though he previously had some experiences with Maya and Unity3D), this somehow indicates just how easy Energy3D may be for 3D modeling, especially for novices. (As a matter of fact, I must confess that we cheated a bit because, as he was working on it, I rushed to add new features to the software on the fly to address his complaints. Then he just restarted the program and got onto a more performant version).


This capability will be extremely useful for engineering design, which must address both structure and function and their relationship. Being able to create complex structures rapidly and then study their functions based on the building simulation and solar simulation engines of Energy3D allows users to explore many design options and test them immediately, a feature that is critically important to engineering education.







Energy3D makes designing realistic buildings easy

The annual yield and cost benefit analyses of rooftop solar panels based on sound scientific and engineering principles are critical steps to the financial success of building solarization. Google's Project Sunroof provides a way for millions of property owners to get recommendations for the right solar solutions.



Another way to conduct accurate scientific analysis of solar panel outputs based on their layout on the rooftop is to use a computer-aided engineering (CAE) tool to do a three-dimensional, full-year analysis based on ab initio scientific simulation. Under the support of the National Science Foundation since 2010, we have been developing Energy3D, a piece of CAE software that has the goal of bringing the power of sophisticated scientific and engineering simulations to children and laypersons. To achieve this goal, a key step is to support users to rapidly sketch up their own buildings and the surrounding objects that may affect their solar potentials. We feel that most CAD tools out there are probably too difficult for average users to create realistic models of their own houses. This forces us to invent new solutions.

We have recently added countless new features to Energy3D to progress towards this goal. The latest version allows many common architectural styles found in most parts of the US to be created and their solar potential to be studied. The screenshots embedded in this article demonstrate this capability. With the current version, each of these designs took myself approximately an hour to create from scratch. But we will continue to push the limit.

The 3D construction user interface has been developed based on the tenet of supporting users to create any structure using a minimum set of building blocks and operations. Once users master a relatively small set of rules, they are empowered to create almost any shape of building as they wish.

Solar yield analysis of the first house
The actual time-consuming part is to get the right dimension and orientation of a real building and the surrounding tall objects such as trees.
Google's 3D map may provide a way to extract these data. Once the approximate geometry of a building is determined, users can easily put solar panels anywhere on the roof to check out their energy yield. They can then try as many different layouts as they wish to compare the yields and select an optimal layout. This is especially important for buildings that may have partial shades and sub-optimal orientations. CAE tools such as Energy3D can be used to do spatial and temporal analysis and report daily outputs of each panel in the array, allowing users to obtain fine-grained, detailed results and thus providing a good simulation of solar panels in day-to-day operation.

The engineering principles behind this solar design, assessment, and optimization process based on science is exactly what the Next Generation Science Standards require K-12 students in the US to learn and practice. So why not ask children for help to solarize their own homes, schools, and communities, at least virtually? The time for doing this can never be better. And we have paved the road for this vision by creating one of easiest 3D interfaces with compelling scientific visualizations that can potentially entice and engage a lot of students. It is time for us to test the idea.

To see more designs, visit this page.

Making sense of non-optimal solar panel orientations seen on Google Maps


Figure 1: Google Map
If you are thinking about putting solar panels on your roof, the conventional wisdom is that your house probably would be automatically disqualified if its roof does not have a large south-facing side for installing solar panels. If you have no idea about this, the sales representatives from solar companies would probably tell you this based on their training.

While it might be a smart strategy to target houses with most promising returns at the beginning of the solar energy industry back a decade ago, the old rules may not hold any longer. With the solar cell efficiency of commercial panels climbing above 20% and, more importantly, the environmental awareness of homeowners increasing, the solar energy market has changed a lot.

Thanks to Google Map, it is just a few mouse clicks away to get an idea about the current status of the market. So I surveyed my neighborhoods in eastern Massachusetts and was surprised to find that a significant amount of rooftop solar panel arrays do not actually face south (Figure 1). This is understandable because a significant percentage of buildings do not have a roof that has a south-facing side. So if homeowners living in those houses want to contribute to solving the environmental problems, they do not have any other choice except putting solar panels wherever they can be mounted. If you take a look at your own neighborhood in Google Map, you should spot a lot of west- and east-facing (or even north-facing!) solar panels.

Figure 2. Solar simulation in Energy3D
Figure 3: South-facing vs. west-facing
I have heard some solar installers accusing competitors of coaxing homeowners into this kind of less effective configurations in order to increase their profits at the expense of the homeowners' running cost. Before we start pointing fingers at one another, let's stop and think: Is it really such a horrible idea to put solar panels on a non-south-facing side? The problem is that few people really have an idea about how much less energy those non-ideal configurations would entail compared with the optimal south-facing situation. An estimate of this is critically important to helping homeowners make up their mind whether to go solar or not. And, by the way, it also demonstrates your business ethics and technical capability as well. Unfortunately, in reality, you cannot re-orient people's houses or roofs to figure that out.

Thanks to the funding from the National Science Foundation, this kind of estimation can be easily done using our Energy3D software, which has a decent crystal ball when it comes to solar modeling and prediction (Figure 2). I have been blogging about this capacity of the software for a while. It is time to finally put this tool into practice to help people evaluate their solar options.


Let's start with a very simple house and a 5kW solar panel system (Figure 3). The first step is to get a sense of how accurate Energy3D's prediction may be. In situations similar to the standard test conditions (STC), this system -- when oriented to the south -- should generate about 6,000 kWh per year in Boston, Massachusetts and about 8,000 kWh per year in Los Angeles, California. The results from Energy3D agree exactly with these widely-cited numbers, as shown in Figure 4.

Figure 4: BOS vs. LAX
With this validity, we can then ask the following question: What if we have only a west-facing roof? This can be easily done by rotating the model house in Energy3D 90° and then redo the calculation. It turns out that the homeowner in Boston will get about 80% of the maximal output (when the panels face south), as illustrated in Figure 5. The folks in LA will fare slightly better -- about 82%.

Figure 5: South-facing vs. west-facing outputs
I believe a large number of homeowners, if informed by the results of this simulation-based analysis, may consider 20% performance reduction as acceptable. Yes, their panels will generate less electricity than those on the roofs of houses with the optimal orientation, but many would like to do whatever they can to help reduce carbon emission. To them, doing it at a pace that is 20% slower is infinitely better than doing nothing at all, letting alone that many houses do not face exactly west and the actual performance reduction will be less than 20%.

The results of this study should give you a sense about how simulations may be useful in fostering the growth of the solar energy market. Even better, through years of development, we have made solar simulation in Energy3D so easy that anyone can do it. As an experimental step, we are now collaborating with high schools in Massachusetts to pilot-test the feasibility of engaging students to evaluate the solarization potential of their own houses. Our goal is to create an integrated education-business model that benefits both sides. We hope that the success of this project will help the world reach the goals set by the Paris Agreement.

What’s new in Visual Process Analytics Version 0.3


Visual Process Analytics (VPA) is a data mining platform that supports research on student learning through using complex tools to solve complex problems. The complexity of this kind of learning activities of students entails complex process data (e.g., event log) that cannot be easily analyzed. This difficulty calls for data visualization that can at least give researchers a glimpse of the data before they can actually conduct in-depth analyses. To this end, the VPA platform provides many different types of visualization that represent many different aspects of complex processes. These graphic representations should help researchers develop some sort of intuition. We believe VPA is an essential tool for data-intensive research, which will only grow more important in the future as data mining, machine learning, and artificial intelligence play critical roles in effective, personalized education.

Several new features were added to Version 0.3, described as follows:

1) Interactions are provided through context menus. Context menus can be invoked by right-clicking on a visualization. Depending on where the user clicks, a context menu provides the available actions applicable to the selected objects. This allows a complex tool such as VPA to still have a simple, pleasant user interface.

2) Result collectors allow users to gather analysis results and export them in the CSV format. VPA is a data browser that allows users to navigate in the ocean of data from the repositories it connects to. Each step of navigation invokes some calculations behind the scenes. To collect the results of these calculations in a mining session, VPA now has a simple result collector that automatically keeps track of the user's work. A more sophisticated result manager is also being conceptualized and developed to make it possible for users to manage their data mining results in a more flexible way. These results can be exported if needed to be analyzed further using other software tools.

3) Cumulative data graphs are available to render a more dramatic view of time series. It is sometimes easier to spot patterns and trends in cumulative graphs. This cumulative analysis applies to all levels of granularity of data supported by VPA (currently, the three granular levels are Top, Medium, and Fine, corresponding to three different ways to categorize action data). VPA also provides a way for users to select variables from a list to be highlighted in cumulative graphs.

Many other new features were also added in this version. For example, additional information about classes and students are provided to contextualize each data set. In the coming weeks, the repository will incorporate data from more than 1,200 students in Indiana who have undertaken engineering design projects using our Energy3D software. This unprecedented large-scale database will potentially provide a goldmine of research data in the area of engineering design study.

For more information about VPA, see my AERA 2016 presentation.

Energy3D V5.0 released

Full-scale building energy simulation
Insolation analysis of a city block
We are pleased to announce a milestone version of our Energy3D CAD software. In addition to fixing numerous bugs, Version 5.0 includes numerous new features that we have recently added to the software to enhance its already powerful concurrent design, simulation, and analysis capabilities.

For example, we have added cut/copy/paste in 3D space that greatly eases 3D construction. With this functionality, laying an array of solar panels on a roof is as simple and intuitive as copying and pasting an existing solar panel. Creating a village or city block is also made easier as a building can be copied and pasted anywhere on the ground -- you can create a number of identical buildings using the copy/paste function and then work to make them different.

Insolation analysis of various houses
Compared with previous versions, the properties of every building element can now be set individually using the corresponding popup menu and window. Being able to set the properties of an individual element is important as it is often a good idea for fenestration on different sides of a building to have different solar heat gain coefficients. The user interface for setting the solar heat gain coefficient, for instance, allows the user to specify whether he or she wants to apply the value to the selected window, all the windows on the selected side, or the entire building.

In a move to simulate machine-learning thermostats such as Google's Nest Thermostat to test the assertion that they can help save energy, we have added programmable thermostats. We have also added a geothermal model that allows for more accurate simulation of heat exchange between a building and the ground. New efforts for modeling weather and landscape more accurately are already on the way.

The goal of Energy3D is to create a software platform that bridges education and industry -- we are already working with leading home energy companies to bring this tool to schools and workplaces. This synergy has led to some interesting and exciting business opportunities that mutually benefit education and industry.

A bonus of this version is that it no longer requires users to install Java. We have provided a Windows installer and a Mac installer that work just like any other familiar software installer. Users should now find it easy to install Energy3D, compared with the previously problematic Java Web Start installer.

Solarizing a house in Energy3D

Fig. 1 3D model of a real house near Boston (2,150 sq ft).
On August 3, 2015, President Obama announced the Clean Power Plan – a landmark step in reducing carbon pollution from power plants that takes real action on climate change. Producing clean energy from rooftop solar panels can greatly mitigate the problems in current power generation. In the US, there are more than 130 million homes. These homes, along with commercial buildings, consume more than 40% of the total energy of the country. With improving generation and storage technologies, a large portion of that usage could be generated by home buildings themselves.

A practical question is: How do we estimate the energy that a house can potentially generate if we put solar panels on top of it? This estimate is key to convincing homeowners to install solar panels or the bank to finance it. You wouldn't buy something without knowing its exact benefits, would you? This is why solar analysis and evaluation are so important to the solar energy industry.

The problem is: Every building is different! The location, the orientation, the landscape, the shape, the roof pitch, and so on, vary from one building to another. And there are over 100 MILLION of them around the country! To make the matter even more complicated, we are talking about annual gains, which require the solar analyst to consider solar radiation and landscape changes in four seasons. With all these complexities, no one can really design the layout of solar panels and calculate their outputs without using a 3D simulation tool.

There may be solar design and prediction software from companies like Autodesk. But for three reasons, we believe that our Energy3D CAD software will be a relevant tool in this marketplace. First, our goal is to enable everyone to use Energy3D without having to go through the level of training that most engineers must go through with other CAD tools in order to master them. Second, Energy3D is completely free of charge to everyone. Third, the accuracy of Energy3D's solar analysis is comparable with that of others (and is improving as we speak!).

With these advantages, it is now possible for homeowners to evaluate the solar potential of their houses INDEPENDENTLY, using an incredibly powerful scientific simulation tool that has been designed for the layperson.

In this post, I will walk you through the solar design process in Energy3D step by step.

1) Sketch up a 3D model of your house

Energy3D has an easy-to-use interface for quickly constructing your house in a 3D environment. With this interface, you can create an approximate 3D model of your house without having to worry about details such as interiors that are not important to solar analysis. Improvements of this user interface are on the way. For example, we just added a handy feature that allows users to copy and paste in 3D space. This new feature can be used to quickly create an array of solar panels by simply copying a panel and hitting Ctrl/Command+V a few times. As trees are important to the performance of your solar panels, you should also model the surrounding trees by adding various tree objects in Energy3D. Figure 1 shows a 3D model of a real house in Massachusetts, surrounded by trees. Notice that this house has a T shape and its longest side faces southeast, which means that other sides of its roof may worth checking.
Fig. 2 Daily solar radiation in four seasons

2) Examine the solar radiation on the roof in four seasons

Once you have a 3D model of your house and the surrounding trees, you should take a look at the solar radiation on the roof throughout the year. To do this, you have to change the date and run a solar simulation for each date. For example, Figure 2 shows the solar radiation heat maps of the Massachusetts house on 1/1, 4/1, 7/1, and 10/1, respectively. Note that the trees do not have leaves from the beginning of December to the end of April (approximately), meaning that their impacts to the performance of the solar panels are minimal in the winter.

The conventional wisdom is that the south-facing side of the roof is a good place to put solar panels. But very few houses face exact south. This is why we need a simulation tool to analyze real situations. By looking at the color maps in Figure 2, we can quickly figure out that the southeast-facing side of the roof of this house is the optimal side for solar panels and we also know that the lower part of this side is shadowed significantly by the surrounding trees.

Fig. 3 Solarizing the house
3) Add, copy, and paste solar panels to create arrays

Having decided which side to lay the solar panels, the next step is to add them to it. You can drop them one by one. Or drop the first one near an edge and then copy and paste it to easily create an array. Repeat this for three rows as illustrated in Figure 3. Note that I chose the solar panels that have a light-electricity conversion efficiency of 15%, which is about average in the current market. New panels may come with higher efficiency.

The three rows have a total number of 45 solar panels (3 x 5 feet each). From Figure 2, it also seems the T-wing roof leaning towards west may be a sub-optimal place to go solar. Let's also put a 2x5 array of panels on that side. If the simulation shows that they do not worth the money, we can just delete them from the model. This is the power of the simulation -- you do not have to pay a penny for anything you do with a virtual house (and you do not have to wait for a year to evaluate the effect of anything you do on its yearly energy usage).

4) Run annual energy analysis for the building

Fig. 4 Energy graphs with added solar panels
Now that we have put up the solar panels, we want to know how much energy they can produce. In Energy3D, this is as simple as selecting "Run Annual Energy Analysis for Building..." under the Analysis Menu. A graph will display the progress while Energy3D automatically performs a 12-month simulation and updates the results (Figure 4).

I recommend that you run this analysis every time you add a row of solar panels to keep track of the gains from each additional row. For example, Figure 4 shows the changes of solar outputs each time we add a row (the last one is the 10 panels added to the west-facing side of the T-wing roof). The following lists the annual results:
  • Row 1, 15 panels, output: 5,414 kWh --- 361 kWh/panel
  • Row 2, 15 panels, output: 5,018 kWh (total: 10,494 kWh) --- 335 kWh/panel
  • Row 3, 15 panels, output: 4,437 kWh (total: 14,931 kWh) --- 296 kWh/panel
  • T-wing 2x5 array, 10 panels, output: 2,805 kWh (total: 17,736 kWh) --- 281 kWh/panel
These results suggest that 30 panels in Rows 1 and 2 are probably a good solution for this house -- they generate a total of 10,494 kWh in a year. But if we have better (i.e., high efficiency) and cheaper solar panels in the future, adding panels to Row 3 and the T-wing may not be such a bad idea.

Fig. 5 Comparing solar panels at different positions
5) Compare the solar gains of panels at different positions

In addition to analyze the energy performance of the entire house, Energy3D also allows you to select individual elements and compare their performances. Figure 5 shows the comparison of four solar panels at different positions. The graph shows that the middle positions in Row 3 are not good spots for solar panels. Based on this information, we can go back to remove those solar panels and redo the analysis to see if we will have a better average output of Row 3.

After removing the five solar panels in the middle of Row 3, the total output drops to 16,335 kWh, meaning that the five panels on average output 280 kWh each.

6) Decide which positions are acceptable for installing solar panels

The analysis results thus far should provide you enough information with regard to whether it worth your money to solarize this house and, if yes, how to solarize it. The real decision depends on the cost of electricity in your area, your budget, and your expectation of the return of investment. With the price of solar panel continuing to drop, the quality continues to improve, and the pressure to reduce fossil energy usage continues to increase, building solarization is becoming more and more viable.

Solar analysis using computational tools is typically considered as the job of a professional engineer as it involves complicated computer-based design and analysis. The high cost of a professional engineer makes analyzing and evaluating millions of buildings economically unfavorable. But Energy3D reduces this task to something that even children can do. This could lead to a paradigm shift in the solar industry that will fundamentally change the way residential and commercial solar evaluation is conducted. We are very excited about this prospect and are eager to with the energy industry to ignite this revolution.

Daily energy analysis in Energy3D

Fig. 1: The analyzed house.
Energy3D already provides a set of powerful analysis tools that users can use to analyze the annual energy performance of their designs. For experts, the annual analysis tools are convenient as they can quickly evaluate their designs based on the results. For novices who are trying to understand how the energy graphs are calculated (or skeptics who are not sure whether they should trust the results), the annual analysis is sometimes a bit like a black box. This is because if there are too many variables (which, in this case, are seasonal changes of solar radiation and weather) to deal with at once, we will be overwhelmed. The total energy data are the results of two astronomic cycles: the daily cycle (caused by the spin of the Earth itself) and the annual cycle (caused by the rotation of the Earth around the Sun). This is why novices have a hard time reasoning with the results.

Fig. 2: Daily light sensor data in four seasons.
To help users reduce one layer of complexity and make sense of the energy data calculated in Energy3D simulations, a new class of daily analysis tools has been added to Energy3D. These tools allow users to pick a day to do the energy analyses, limiting the graphs to the daily cycle.

For example, we can place three sensors on the east, south, and west sides of the house shown in Figure 1. Then we can pick four days -- January 1st, April 1st, July 1st, and October 1st -- to represent the four seasons. Then we run a simulation for each day to collect the corresponding sensor data. The results are shown in Figure 2. These show that in the winter, the south-facing side receives the highest intensity of solar radiation, compared with the east and west-facing sides. In the summer, however, it is the east and west-facing sides that receive the highest intensity of solar radiation. In the spring and fall, the peak intensities of the three sides are comparable but they peak at different times.

Fig. 3: Daily energy use and production in four seasons.
If you take a more careful look at Figure 2, you will notice that, while the radiation intensity on the south-facing side always peaks at noon, those on the east and west-facing sides generally go through a seasonal shift. In the summer, the peak of radiation intensity occurs around 8 am on the east-facing side and around 4 pm on the west-facing side, respectively. In the winter, these peaks occur around 9 am and 2 pm, respectively. This difference is due to the shorter day in the winter and the lower position of the Sun in the sky.

Energy3D also provides a heliodon to visualize the solar path on any given day, which you can use to examine the angle of the sun and the length of the day. If you want to visually evaluate solar radiation on a site, it is best to combine the sensor and the heliodon.

You can also analyze the daily energy use and production. Figure 3 shows the results. Since this house has a lot of south-facing windows that have a Solar Heat Gain Coefficient of 80%, the solar energy is actually enough to keep the house warm (you may notice that your heater runs less frequently in the middle of a sunny winter day if you have a large south-facing window). But the downside is that it also requires a lot of energy to cool the house in the summer. Also note the interesting energy pattern for July 1st -- there are two smaller peaks of solar radiation in the morning and afternoon. Why? I will leave that answer to you.

Energy3D in Colombia

Camilo Vieira Mejia, a PhD student of Purdue University, recently brought our Energy3D software to a workshop, which is a part of Clubes de Ciencia -- an initiative where graduate students go to Colombia and share science and engineering concepts with high school students from small towns around Antioquia (a state of Colombia).

Students designed houses with Energy3D, printed them out, assemble them, and put them under the Sun to test their solar gains. They probably have also run the solar and thermal analyses for their virtual houses.

We are glad that our free software is reaching out to students in these rural areas and helping them to become interested in science and engineering. This is one of the many examples that a project funded by the National Science Foundation also turns out to benefit people in other countries and impact the world in many positive ways. In this sense, the National Science Foundation is not just a federal agency -- it is a global agency.

If you are also using Energy3D in your country, please consider contacting us and sharing your stories or thoughts.

Energy3D is intended to be global -- It currently includes weather data from 220 locations in all the continents. Please let us know you would like to include locations in your country in the software so that you can design energy solutions for your own area. As a matter of fact, this was exactly what Camilo asked me to do before he headed for Colombia. I would have had no clue which towns in Colombia should be added and where I could retrieve their weather data (which is often in a foreign language).

[With the kind permission of these participating students, we are able to release the photos in this blog post.]

Geothermal simulation in Energy3D


Fig.1: Annual air and ground temperatures (daily averages)
A building exchanges heat not only with the outside air but also with the ground. The ground temperature depends on the location and the depth. At six meters under and deeper, the temperature remains almost constant throughout the year. That constant temperature roughly equals to the mean annual air temperature, which depends on the latitude.
Fig.2: Daily air and ground temperatures on 7/1

The ground temperature has a variation pattern different from that of the air temperature. You may experience this difference when you walk into the basement of a house from the outside in the summer or in the winter at different times of the day.

For our Energy3D CAD software to account for the heat transfer between a building and the ground at any time of a year at the 220 worldwide locations that it currently supports, we must develop a physical model for geothermal energy. While there is an abundance of weather data, we found very little ground data (ground data are, understandably, more difficult and expensive to collect). In the absence of real-world data, we have to rely on mathematical modeling.

Fig.3: Daily air and ground temperatures on 1/1
This mission was accomplished in Version 4.9.3 of Energy3D, which can now simulate the heat transfer with the ground. This geothermal model also opens up the possibility to simulate ground source heat pumps -- a promising clean energy solution, in Energy3D (which aims to ultimately include various renewable energy sources in its design capacity to support energy engineering).

Exactly how the math works can be found in the User Guide. In this blog post, I will show you some results. Figure 1 shows the daily averages of the air and ground temperatures throughout the year in Boston, MA. There are two notable features of this graph: 1) Going more deeply, the temperature fluctuation decreases and eventually diminishes at six meters; and 2) the peaks of the ground temperatures lag behind that of the air temperature, due to the heat capacity of the ground (the ground absorbs a lot of thermal energy in the summer and slowly releases them as the air cools in the fall).

Fig. 4: Four snapshots of heat transfer with the ground on a cold day.
In addition to the annual trend, users can also examine the daily fluctuations of the ground temperatures at different depths. Figure 2 shows the results on July, 1. There are three notable features of this graph: 1) Overall the ground temperature decreases when we go deeper; 2) the daily fluctuation of the ground temperature decreases when we go deeper; and 3) the peaks of the ground temperatures lag behind the peak of air temperature. Figure 3 shows the results on January 1 with a similar trend, except that the ground temperatures are higher than the air temperature.

Figure 4 shows four snapshots of the heat transfer between a house and the ground at four different times (12 am, 6 am, 12 pm, and 6 pm) on January 1. The figure shows arrays of heat flux vectors that represent the direction and magnitude of heat flow. To exaggerate the visualization, the R-values of the floor insulation and the windows were deliberately set to be low. If you observe carefully, you will find that the change in the magnitude of the heat flux vectors into the ground lags behind that of those into the air.

The geothermal model also includes parameters that allow users to choose the physical properties of the ground, such as thermal diffusivity. For example, Dry land tends to have smaller thermal diffusivity than wet land. With these properties, geology also becomes a design factor, making the already interdisciplinary Energy3D software even more so.