Smart Home Technology and Simulation Solutions

smart home technologyThe concept of the “automated home and smart home” was first introduced over 80 years ago, and has been facing different technical limitations since then.

Recently, service providers and home appliance manufacturers have launched a new initiative to bring the concept of smart homes to reality allowing subscribers to remotely manage and monitor different home devices from anywhere via smart phones or over the web with no physical distance limitations.

With the ongoing development of mass-deployed broadband internet connectivity and wireless technology, the concept of a smart home has become a reality where all devices are integrated and interconnected via through the wireless network. Smart home technology become the future of residential related technology which is designed to deliver and distribute number of services inside and outside the house via networked devices in which all the different applications and the intelligence behind them are integrated.

In today’s world, the Smart Home is not enough to the environmentally-conscious user, but energy efficiency is a key. The IoT provides a strong tool that not only connects wireless communication devices but wireless sensors for heating/cooling or any needed utility within the house to better manage energy usage as well as enhance the living experience in modern homes.

Recent simulation work was done with ANSYS products where a house CAD model is analyzed to demonstrate the comprehensive simulation studies on consumed energy reduction for lighting as well as home cooling and heating as shown in Figure 1. Various multiphysics simulations were conducted on the kitchen room using ANSYS products.
simulated smart home model

Integration of the different smart technologies is also studied including smartwatch communication with home control unit as an example of customizing the Smart Home for the user-based experience as shown in Figure 2.

Camera/motion sensor were used as part of the home security system, placed at the entrance of the kitchen, and were coupled with the home light and HVAC control system located in the middle area to remotely switch on/off the lights and turn on/off the heating/cooling system when a person enters or leaves the room.

Finally, the coupling/RF interference (RFI) between the antennas integrated within the house’s smart devices within their RF circuitries are investigated. Therefore, it is vital for the integrity of the system to study the fields generated by the different antennas, using ANSYS HFSS, employed by the smart devices: triple band energy control unit (900 MHz, 2.45 GHz, 5.8 GHz), security/motion sensor and surveillance camera (5.8 GHz), LED light bulbs (2.45 GHz), and actuator of the HVAC duct dampers (900 MHz). Figure 3 shows the different antenna models along with their antenna reflection coefficients and far field gain patterns.

smart home simulated reflection coeffiecient

The energy control unit antenna is designed to cover all three frequency bands as shown in Figure 4.

triple band energy control unit antenna model

Having modeled the antenna performance using ANSYS HFSS, ANSYS EMIT can be used to simulate the performance of the Smart Home’s sensors. ANSYS EMIT provides built-in library and behavioral models for the sensors used in the house. In this case, the sensors operate in unlicensed spectrum at the 900 MHz, 2.45 GHz and 5.8 GHz bands using available protocols such as Zigbee. The antenna performance results can then be used with the available RF models to first compute the RF link margin between the sensors and the home control unit (HCU) in the home without any other RF sources.

The results are summarized in the Table I. Our target for the wireless system was to obtain a 10dB link margin in this interference-free environment to allow for a comfortable margin.  The results show an acceptable 14.6dB link margin for the link between the actuator and HCU. For the motion sensor, the link margin is excessive at 40dB. While this would ensure more than adequate performance for this link, the link is over-designed and changes should be considered to reduce the potential for harmful interference to other links and to reduce power consumption.

Finally, the Lightbulb-to-HCU link is only 2.2 dB which, while non-negative, falls short of our 10dB goal. This is somewhat troublesome, particularly in the congested 2.45 GHz band as this link will be particularly susceptible to interference from other sources of RF energy in the home or due to fluctuations in the propagation channel.

TABLE 1. Summary of RF link margin between the sensors and the home control unit (HCU)

RF Link Link Margin (10dB Goal)
Actuator ↔ HCU 14.6 dB
Light Bulb ↔ HCU 2.2 dB
Motion Sensor ↔ HCU        40 dB

ANSYS EMIT is also used to evaluate the interference from other RF sources within the home. As an example, we can evaluate the impact of a typical wireless speaker system. In this case, we place a wireless speaker that uses a Texas Instruments PurePathTM Wireless Audio chipset in the home as shown notionally in Figure 5a. The obtained results indicate that the speaker system will cause severe interference to the light-bulb/HCU link (red square) in Figure 5b but will not cause problems with the other links (green) squares. The 21.2 dB EMI Margin is severe and requires further evaluation of the RF environment in the home.

RFI block diagram

On the other hand, Smart LEDs, with an embedded antenna, not only help energy efficiency but also the overall efficiency of the system (home) through wireless communication with other IoT devices. It is important to study the performance of the LED under various operating conditions. At higher temperatures, the antenna’s operating frequency shifts from its nominal value due to changes in the PCB’s substrate dielectric constant as well as the electric resistivity of the different metallic parts.

In addition, thermal stress may cause deformation for both antenna and circuit components causing a drop in the antenna radiation performance. Thermal analysis is performed using ANSYS Icepak on the LED installed in the kitchen ceiling. The LED dissipates heat via conduction to the ceiling, and via natural convection and radiation to its surroundings. The detailed PCB layout is imported for accurate representation of the conduction paths. The computed temperature distribution from the thermal analysis is used to re-evaluate the material properties of the dielectric and conductor materials in the electromagnetic analysis setup. The updated electromagnetic analysis determines a drift in the antenna’s operating frequency.

The maximum temperature of the LED in this simulation is in line with reported values (by LED manufacturers) for a 13 Watts LED light bulb as shown in Figure 6. Additionally, thermal stress and deformation analyses were conducted using ANSYS Mechanical. The total deformation of internal components is computed and verified for structural integrity and performance as shown in Figure 6d.

LED temperature contour

Finally, a virtual model of flow and heat distribution in the kitchen area of the home with a “zonal” cooling system is demonstrated. A computational fluid dynamics (CFD) model is built using ANSYS FLUENT that includes the ducting from the HVAC unit to the kitchen and its surroundings. In this model, two duct dampers (valves) are considered; the first damper in the vertical duct is slightly open and feeds cool air to the second floor while the second damper is mounted in the horizontal duct and is used to cool the kitchen temperature.

The CFD model has a 3.9 million computational grid with polyhedral elements. The grid has two prism layers on dampers, horizontal duct and vanes of the vent. A k-w SST turbulence model is used to account for turbulent flow in the ducting and the house. At start time (t = 0 sec), the duct is initialized with a temperature of 55 F, which is equal to the cool air supply temperature from the HVAC unit. The kitchen temperature is initialized with 90 F, a typical room temperature on a warm summer day. The damper opens at the start of the simulation with a speed of 90 degrees/0.5sec (30 RPM). The initial time step of the model is 0.0055 sec (equal to 1 degree of rotation).

After the damper is fully open (i.e., t = 0.5 s), the time step gradually increases to capture longer operating times (it takes much longer to stabilize the temperature in the room to a comfortable temperature such as 72 F). The CFD model in Figure 7 shows details of flow and temperature distribution in the ducts and the kitchen after opening of the damper.

The streamlines of velocity in the domain at t = 36.6 s show the cold air from the HVAC unit dissipating in the kitchen area. Additionally, a surface in the flow with a constant temperature of 72 F is shown and its color indicates the flow velocity of this surface. This provides feedback on how the room temperature is decreasing to the intended comfort temperature of 72 F. The CFD model outputs can provide valuable insight on the response time of cooling system to zonal damper opening or closing. It can also provide a virtual tool to examine whether this smart IoT based energy control procedure satisfies acceptable limits for temperature, pressure and flow velocity in the ducting and in the living areas.

For more information, you can download our brochure RF Cosite and Coexistence
RFI Modeling and Mitigation. Please feel free to ask questions in the comments section and I’ll try to answer them for you.