Simulators - Science topic

It is easy. You should watch the tutorial videos on YouTube. Use LOAD and BOundary condition in ABAQUS.

Based on what you will simulate is different. Please explain more about your aim with picture in detail.

There are a lot of general transient analysis in youtube.

For mobility, random mobility models as well as user defined mobility patterns via a CSV input file can be used. For varying one or more parameters across a range and analyzing the impact on the network performance, the multi parameter sweeper utility can be used. Refer to https://www.tetcos.com/pdf/v14/NetSim-Multi-Parameter-Sweeper_Python_v14.pdf for more details.

Alessio Pricci few days back i sent u a message thru RG. I just wanted to know if u have received that ?

See https://www.researchgate.net/post/How_can_I_accurately_determine_the_count_of_handovers_taking_place_within_a_5G_network_when_simulating_with_NetSim/1, its been answered there.

Thank you Smrity Dwivedi for sharing

More information is needed

as Ricardo Tadeu Maia said the reasons of the absence of match can be different. If the positions of peaks are different maybe errors in the unit cell (space group, lattice parameters..) are present, while when the intensities are different the reasons could be due to the cell content or to preferred orientation in the powder. You should provide more details

I have the same doubts. Did you find a solution?

Dear Lee Heyojea,

To update your vehicle's state variable in a simulator, you typically need to access the simulation environment's API or scripting interface. Here is a general outline of the steps that could help you:

1. First, Identify the specific state variable you want to update for the vehicle in the simulator. This could be any variable like position, velocity, orientation, or any other relevant parameters.

2. Next, using the provided API or scripting interface, retrieve the current state of the vehicle. This may involve querying the simulator for the current values of the state variables.

3. Then modify the state variable according to your requirements. You could set new values for those parameters (Velocity, Position, etc.).

4. Thereafter, update the vehicle's state in the simulator by sending the modified state information back to the simulation environment using the appropriate API calls or commands.

5. Finally, verify that the changes have been applied correctly by observing the updated state of the vehicle in the simulator.

But, don't forget to consult the documentation (manual) or resources provided by the simulator to understand the specific instructions, methods and commands available for updating vehicle state variables.

In addition to mentioning multiplicity 3, your basis set for Nickel is incomplete. You used LANL2DZ for Ni but didn't mention the pseudo-potential as well as pseudo=read.

There is.

Just create a Builder project that's based on STARS simulator, and you'll find what you need in the Process Wizard.

Can you give me the titles of the papers discussing nanofluids simulations you mentioned in your question? I'm working on something similar and they would be beneficial.

Notice that the longer line exhibits a phase transition at 8.4 GHz from -180 to +180 degrees. If you linearize the plot by subtracting 360 degrees for all responses above 8.4 GHz, the phase at 10 GHz becomes 145-360 = 215 degrees, very close to the 214 degrees you expected. The circuit is simulation responding normally.

See this discussion https://www.researchgate.net/post/Using_NetSim_for_military_radio_simulation/2

Simulation in general, and discrete event simulation DES in particular, is not done for the sake of simulation itself. Yes, the sequence of events can be simulated. However, the following steps in DES model design should always be followed:

-Problem description

-Questions to be answered by the model

- Performance criteria (usually defined by the stakeholders)

- Decision variables and their constraints

- Design scenarios to be analyzed using the validated and verified model

-Conclusions and recommendations to stakeholders and decision-makers

DES has become indispensable and demonstrated its power and efficiency for years in addressing such problems as capacity, resource allocation, workflow and bottleneck forming, playing various scenarios of the process performance.

Because of this, to answer your question " would this be a sufficient way..or not" you have to first address the above basic steps.

These simulators offer the necessary features for simulating ad hoc communication between UAVs and centralized OpenFlow communication between UAVs and SDN controllers.

Hi Zhuoyuan Leng , could you provide more details of the boundary conditions you are using ?

Anastasia Klimova The closer the object is to the inlet, the more important the turbulence boundary conditions at the inlet are, as I mentioned earlier. An analogous case is the flow around an obstacle in a pipe. If we set a parabolic profile at the inlet, the hydraulic run-up can be much shorter than when a piston flow is set at the inlet. Depending on the flow, often characterized by the Reynolds number, there is a minimum distance required for the velocity profile to be established, etc. Hydraulic run-up limitation shortens the calculations because a smaller computational domain means fewer computational cells, but it forces the engineer to pay more attention to the selection of appropriate boundary conditions.

Dear Mr. Singh!

My understanding is that you are after specific case study applications. I found one:

Abaid Ullah Zafar, Mohsin Shahzad, Khuram Shahzad, Andrea Appolloni, Islam Elgammal, Gamification and sustainable development: Role of gamified learning in sustainable purchasing, Technological Forecasting and Social Change, Volume 198, 2024, https://doi.org/10.1016/j.techfore.2023.122968, Available at: https://www.sciencedirect.com/science/article/abs/pii/S0040162523006534

Yours sincerely, Bulcsu Szekely

This is a convergence issue which can be due to a variety of reasons. My advice is to reformulate your z-matrix (avoid Cartesian coords in possible) so that the starting geometry has the highest possible symmetry, i.e. belongs to a point group with the largest number of symmetry elements (which is probably Th or -worse- D2h). Second, start with a simple basis set (def2svp) and increase its complexity only if needed. Third, try different convergence algorithms (for example a slower quadratic one, qc in Gaussian).

Finally, and maybe most importantly, there is already at least a paper on this topic, dealing exactly [V(OH2)6]3+. Give it a glance:

Yes, you can simulate various aspects of solid polymers using Aspen Plus. AspenTech offers Aspen Polymers, a process simulation technology designed to optimize production rates, yield, and quality by modeling polymerization processes. Aspen Plus includes features like "Physical Properties" and "Phase Equilibrium," which are essential for modeling polymer behavior. Additionally, Aspen Plus supports customizable unit operation models and user-defined models, allowing for tailored simulations of polymer processes. Users have successfully employed Aspen Plus to simulate processes such as polylactide (PLA), poly(triethylene glycol) (PTT), and high-impact polypropylene via the Spheripol PP process.

Hello Sehili Ammar

I have been using SCAPS-1D for educational purposes, and I initially I tried to replicate the results of papers that used it. Search for them and check the parameters they considered. There are plenty of papers describing many properties of the materials used in SCAPS.

Best regards,

Ricardo

Hello RG Community in the above instance subsequent to the ffTK first tab blanks will be drawn for psf, par etc., accordingly working through all tabs depending will function best:).

Aaron Ocansey Please use planar monopole rectangular antennas

Autoclaving may cause some water evaporation resulting in different salt concentrations. Filtering is probably a better choice.

The problem of chip piling up at tool tip is usually induced by too large strain. Try the following method:

1. Decrease the criterial strain of damage initiation;

2. Decrease the strain hardening exponent n if you're using JC model.

Good luck!

If you have mathematical model, then you can.

Simbiology is indeed closely intertwined with MATLAB, offering several ways to leverage its capabilities for biological modeling and simulation. Here's a breakdown of how they interact:

Note: Simbiology is not the only way to use MATLAB for biological modeling. Alternative toolboxes and libraries exist, each with its own strengths and weaknesses. Choosing the right approach depends on your specific needs and expertise.

Have you discovered it?

Dear Ali,

One should always start with the most simple problem, which is the propagation of sound als low frequencies in a straight pipe. B y low frequencies I mean that only plane waves propagates. For a duct with height H this corresponds to frequencies f = W/(2 pi) such that c/(f H)>2, with c the speed of sound in the fluid. For circular pipes of radius a this implies W a/c<1.84. Even in this apparently trivial case propagation is not easy to predict. Of course if the fluid is stagnant the main condition for a reasonable prediction of sound propagation is a "dedicated" numerical scheme with low damping and at least about 10 grid points per wave length. Various scheme have been proposed. When turbulent flow is present, there is sound absorption by the viscous dominated sub-boundary layers of the turbulent flow at the wall. This has been extensively studied for low subsonic flows by among other D. Ronneberger, M.C.A.M. Peters, M.S. Howe and more recently the team of KTH (Stockholm). It is essential to have a numerical scheme that does resolve the boundary layer structure (very thin!). For higher Mach numbers there are additional complexities due to bending of acoustic waves by convection. If you furthermore introduce a cavity as shown in your drawing, there will be a shear layer separating the main flow from the "dead water region" in the cavity. The acoustic perturbations will induce a modulation of the vorticity in the shear layer. At Strouhal numbers f B/U (B cavity width, U main flow velocity) of order unity, whistling can occur (sound amplification). There are a lost of papers on this subject. A review paper is that of Devis Tonon on closed side branches in ducts. If you consider high frequencies the problem becomes more complex from a propagation point of view but the interaction with the shear-layer is mainly due to convective effects (bending of waves). There is no production of sound. Good luck. Keep it simple.

Check this: https://github.com/eclipse-sumo/sumo/issues/12120

The conclusion is: If you configure some of the drivers to be aggressive (lcAssertive > 1), you will see more lane changes into the inner lane.

Can you maybe clarify what you mean, as bogie wheels should not need different control when going around a bend - the wheels on the solid axle are conical for that reason. Unless you're referring to the resultant small delta in effective wheel diameter that occurs - but I wouldn't think that it would require major control system intervention. Wheel spinning occurs when the tractive effort exceeds the adhesive friction limits, and wheel sliding/slipping occurs when the braking force exceeds the adhesive friction limits. Maybe explain your question with a little more detail?

Easiest way to create local cs is to use workplane, which is not just a plane, but also a work cs.

In the rolling simulation, you might also just pin the wheel axis and simply move the rail, in which case you would not need any local cs.

I will be happy if this research work helps

请问有得到解答吗，我也在被这个问题困扰

Hi, I hope you're very well. It will help if you use the piezoelectric devices branch of the AC/DC COMSOL module. Also, you may visit COMSOL's applications library to familiarize yourself with piezoelectric devices.

using the piezoelectric device module is possible to define completely the reverse and direct piezoelectric effects as in SAW devices.

I would recommend giving the Ligpargen server a try.

You can use Master-slave boundary conditions or Lattice Pair boundary conditions to simulate the unit cell of SRR.

Analyisis means that I should enter the equations?

try this one: https://pypsa.org/

A few of them here:

1. CloudSim

2. CloudAnalyst

3. iFogSim

4. GSS

Good luck

Dear friend Saurav Gautam

Hey there! Now, when it comes to diving into the world of ellipsometry simulation, I have got your back. Simulation tools are crucial for understanding the intricate details of thin films and their optical properties. While I might not have real-time information, let me recommend a few software options that were popular for ellipsometry simulations:

1. **FilmWizard by J.A. Woollam**: This software is designed for spectroscopic ellipsometry data analysis and simulation. It's widely used in both academia and industry.

2. **CompleteEASE by J.A. Woollam**: Another tool from J.A. Woollam, CompleteEASE is a comprehensive ellipsometry data analysis and simulation software.

3. **WVASE32 by J.A. Woollam**: This is a powerful software tool for spectroscopic ellipsometry data analysis and simulation.

4. **DeltaPsi2 by HORIBA Jobin Yvon**: This software is part of the ellipsometer offerings by HORIBA and is known for its user-friendly interface.

5. **TFCalc by Software Spectra Inc.**: While primarily known for thin film design, TFCalc also supports ellipsometric analysis and simulation.

Remember, the availability of specific features might vary across these tools, so it's a good idea to explore the documentation or contact the software providers for more detailed information.

Now, go forth and unravel the mysteries of your doped thin films with the power of simulation! If you Saurav Gautam need further insights or have any other questions, just shout out. I am here to assist!

Aspen Plus excels in dynamic simulations and rigorous process modeling while Aspen Hysys focuses on steady-state simulations and conceptual design, Aspen Plus and Aspen Hysys can be used for same application in many industries, when you start a new simulation you can identify that Aspen Plus fits better for fine chemistry, or all other non petro processes, such as acids, pharma, etc, while Aspen Hysys has more features related to for petrochemical.However , Aspen HYSYS (or simply HYSYS) is a chemical process simulator currently developed by Aspen Tech used to mathematically model chemical processes, from unit operations to full chemical plants and refineries.

Aspen plus used for Predict and eliminate energy waste though use of an integrated design and modeling tools. Optimize separation processes. Design and optimize adsorption processes to improve purity and throughput with deeper insights using rigorous models..

You can use an infinite plane wave excitation from any direction you choose, which may be what you want. You can have far-field results.

If it has a ground plane and you are looking at propagation across the array then you can use a waveguide port full width along one of the edges, from the ground to several board thicknesses above the top of the board. You can choose which or how many modes to use.

To resolve this issue:

1. Double-check the file or directory path: Verify that the file or directory in the error message actually exists in the specified location. Make sure there are no typos or missing files.

2. Check the working directory

3. Include search paths

4. Verify file permissions

5. Check dependencies

Good luck

Here is an LTSPICE version utilizing the analogy between electrical and mechanical dynamics. I am sure you can convert easily.

Choosing the best simulator for implementing encryption on resource-constrained IoT devices depends on several factors, including:

Here are some popular simulators for implementing encryption on resource-constrained IoT devices:

thanks Ned Patton But how we are going extract the mechanical properties (E1 and E2) for ONE ply lamina from this coupon test results?

Thanks，I have used COMSOL.

Unfortunately, my model often encounters issues such as non convergence, incorrect solutions, and so on.@Abeer Abd EI-Salam

No, they are not related.

But

dBm/square metre is related to V/m for a plane electromagnetic wave by the impedance of the medium it is travelling in.

Power density of 0 dBm/square metre = 1 milliwatt/square metre

A 1 V/m rms EM wave has an average power density of (1)²/Z watts per square metre,

where Z is the impedance of the medium, approximately 377 ohms for free space.

See https://www2.ph.ed.ac.uk/~mevans/em/lec14.pdf or similar.

For multi-hop, data passes through multiple underwater devices nodes before reaching its destination. This type of simulation needs ad hoc routing where each underwater devices can act as a router. NetSim has one example of ad hoc routing for underwater networks using depth-based routing (DBR). DBR is a ad hoc routing protocol for underwater wireless networks. It utilizes the depth of nodes to make forwarding decisions. The document (https://www.tetcos.com/pdf/v13.3/NetSim-UWAN_DBR-protocol-implementation.pdf) provides implementation details of DBR in NetSim.

Follow these steps:

1. Define a Route: Create a route file (e.g., `myroute.rou.xml`) that defines the routes the vehicles will follow. Specify the desired start and end edges for each route.

2. Define a Vehicle Type: Create a vehicle type file (e.g., `myvtype.add.xml`) that defines the vehicle types and their properties, such as maximum speed. You can define multiple vehicle types with different speed values to simulate vehicles moving at different speeds.

3. Generate Vehicle Trajectories: Use the `randomTrips.py` script provided with SUMO to generate vehicle trajectories. This script allows you to specify the number of vehicles to generate, their depart times, and their routes. For example, you can gradually increase the number of vehicles generated over time.

Here's an example command to generate vehicles using `randomTrips.py`:

````

randomTrips.py -n mynetwork.net.xml -r myroute.rou.xml -e 1000 -o mytrips.trips.xml --additional-files myvtype.add.xml

```

This command generates 1000 trips (`-e 1000`) based on the defined route file, and the additional vehicle type file is specified with `--additional-files`.

4. Run SUMO Simulation: Launch the SUMO simulation using the generated vehicle trajectories:

````

sumo-gui -n mynetwork.net.xml -r mytrips.trips.xml -a myvtype.add.xml

```

This command opens the SUMO-GUI with the network layout, vehicles, and simulation settings. You can visualize the simulation in real-time and observe the congestion scenario.

To extract the average speed from SUMO and use it in Python to plot the results, you can utilize the TraCI interface and follow these steps:

1. Import TraCI in Python: Import the TraCI library in your Python script to establish a connection with the SUMO simulation and retrieve simulation data.

````python

import traci

```

2. Connect to SUMO: Establish a connection with the running SUMO simulation within your Python script.

````python

traci.init(port=<port_number>)

```

3. Retrieve Vehicle Speeds: Within a simulation loop, retrieve the speeds of the vehicles at each time step using the `traci.vehicle.getSpeed()` function. Store the speeds in a list or any data structure for further analysis.

````python

speeds = []

for step in range(simulation_steps):

traci.simulationStep()

vehicle_ids = traci.vehicle.getIDList()

for vehicle_id in vehicle_ids:

speed = traci.vehicle.getSpeed(vehicle_id)

speeds.append(speed)

```

4. Calculate Average Speed: After the simulation loop, calculate the average speed from the collected speed data.

````python

average_speed = sum(speeds) / len(speeds)

```

5. Plot the Results: Use a plotting library such as Matplotlib to visualize the results. You can plot the speed values over time or create histograms to analyze the distribution of speeds.

````python

import matplotlib.pyplot as plt

# Plotting speed over time

time = range(simulation_steps)

plt.plot(time, speeds)

plt.xlabel('Time Step')

plt.ylabel('Speed')

plt.title('Vehicle Speed Over Time')

plt.show()

# Plotting speed distribution

plt.hist(speeds, bins=20)

plt.xlabel('Speed')

plt.ylabel('Frequency')

plt.title('Vehicle Speed Distribution')

plt.show()

```

Remember to close the TraCI connection and end the simulation when you are done:

```python

traci.close()

```

Hope it helps: credit AI.

You may perform cross-correlation by FFT check out the link below:

https://www.mathworks.com/matlabcentral/fileexchange/53570-xcorr2_fft-a-b

Hi. The calculations are probably correct as it is a simple case/ geometry. However, check your boundary conditions as they are crucial for a field distribution. You could try https://www.comsol.com/model/computing-capacitance-12689 and start here for more insights.

Perhaps you can check some microcell models developed for Silicon Phtomultipliers studies, e.g. https://re.public.polimi.it/retrieve/handle/11311/971982/75905/2015%20-%20Villa%20-%20SPICE%20Electrical%20Models%20and%20Simulations%20of%20Silicon%20Photomultipliers%20-%20post-print.pdf

Properly set up the temperature and pressure conditions to ensure they are applied to the workpiece. If you're encountering issues where the temperature and pressure are not being applied, here are some potential troubleshooting steps:

1. Verify material properties: Ensure that you have assigned appropriate material properties to the workpiece in DEFORM 2D. The material properties, such as thermal conductivity and specific heat, directly affect how temperature is applied and distributed in the workpiece.

2. Check boundary conditions: Review the boundary conditions you have specified for the workpiece. Make sure you have properly defined the temperature and pressure boundary conditions at the appropriate regions of the workpiece. Check that the boundary conditions are correctly assigned and activated in the simulation setup.

3. Check element types: Ensure that the elements used to mesh the workpiece are capable of incorporating temperature and pressure effects. In DEFORM 2D, elements such as "convection" or "heat transfer" elements are typically used to model thermal effects. Similarly, "pressure" or "contact" elements may be necessary to capture pressure effects. Verify that the elements used in the workpiece mesh support the desired thermal and pressure behavior.

4. Element connectivity: Double-check that the mesh connectivity is correct. Ensure that the elements are correctly connected to each other and form a coherent mesh. Inaccurate connectivity can lead to improper transfer of temperature and pressure between elements, resulting in issues with their application.

5. Review simulation settings: Review the simulation settings and parameters in DEFORM 2D. Verify that you have set up the simulation correctly, including time steps, material models, solver settings, and any specific parameters related to temperature and pressure application. Incorrect settings could prevent the appropriate application of temperature and pressure.

6. Consult DEFORM 2D documentation or support: If you have followed the above steps and are still experiencing issues, it's recommended to refer to the DEFORM 2D software documentation or contact their technical support. They can provide specific guidance and help troubleshoot the issue based on your simulation setup and the specific version of the software you are using.

Hope it helps

Simulation is done in 2D so that capacitor plates are assumed to be infinite in the surface normal (z) direction. Because of that, the result seems normal, but it can be verified by solving it as a boundary value problem, referring to Jackson Classical Electrodynamics chapter 2.

You can simulate most of the molecular biology experiments using tools like Snapgene or Geneious Prime. You can get trial versions for about a month.

Best!

Anitha Golkonda Wa Alaikum Salaam,

Here is one approach to simulate a fuzzy logic controller for a 4-switch 3-phase inverter in MATLAB:

1. Design the fuzzy membership functions and rule for the controller based on your control objectives. For example, error and change of error for output current/voltage.

2. Build a Simulink model of the 3-phase inverter and PWM generator connected to a load.

3. Add the Fuzzy Logic Controller block in Simulink and configure it with the designed fuzzy membership functions and rules.

4. Feed the measured current/voltage signals from the 3-phase load back to the fuzzy controller as inputs.

5. Connect the fuzzy controller output to the PWM generator block to modulate the inverter switches.

6. Add scopes to visualize the inverter outputs and monitor the performance of the fuzzy controller.

7. Run the simulation and tune the fuzzy rules and parameters as needed to achieve desired response.

The key is properly designing the fuzzy logic system and integrating it effectively within the inverter model. I would be glad to help explain this process in more detail or provide sample models to work from.

thanks. but since the linked files on the tutorial are updated to v 5 and of v. 4 is no longer available I wonder if i can continue v 4 with v.5. any idea?

Dear Joshua,

Have you calibrated the model for your case study? Are you sure about the calibration and validation process? How about your observation data? you need to recheck your data and modeling stages.

I strongly suggest you consider the "sensitivity analysis stage" in your calculations. it does not only let you know how much each input parameter affects your daily runoff, but you probably be able to infer what is the most impactful parameter in your case study. After sensitivity analysis, you can have a meaningful calibration and the calibrated input parameters will make sense, as well as discharge results.

By the way, I can guess your extremely high values may be related to the soil infiltration rate and your soil data.

Totally agree with you Venus.

In fact, my doctoral work pulled out several embedded mathematical hypotheses and equations presently in use that mimic cultural practices besides those I had derived myself from just one of the aspects of the cultural practices of a race in Africa - The Igbo People. You can check my paper here

You can also reach me for collaboration

Why you want to use Monte Carlo code?

Up

Simulating an Archimedes Screw using Computational Fluid Dynamics (CFD) involves modeling the fluid flow within the screw geometry. While I can't provide real-time links to specific tutorials or documents, I can certainly guide you on the steps involved in simulating an Archimedes Screw using CFD software. You can find relevant tutorials on popular CFD software platforms' official websites or academic platforms like ResearchGate, Google Scholar, or university research publications.

Here are the general steps you would follow:

1. Understanding the Geometry:

2. Mesh Generation:

3. Setting Boundary Conditions:

4. Defining the Physics:

5. Running the Simulation:

6. Post-Processing:

7. Validation and Iteration:

Remember, every CFD software might have slightly different steps and interfaces, so it's crucial to refer to the specific user manual or tutorial related to the software you are using. Many CFD software providers also have online forums and communities where you can ask specific questions related to your simulations.

If we design a unit cell with open on the x-axis, magnetic on the z-axis, and electric on the y-axis. and getting the desired results like the real part of mu -ve, the real part of epsilon -ve,, and the real part of n -ve. can we use that unit cell as an array in our design antenna? Or should I first form an array of that unit cell and simulate the same in the CST? But as I did this. I got different results meaning mu, epsilon, and n all become positive. just want to confirm that am I doing something wrong.

Rohit Nimje

To examine the behavior of the metal-semiconductor junction under various circumstances, use software tools for device simulation and modeling, such as TCAD (Technology Computer-Aided Design) tools.

I want to connect aspen properties with MATLAB. like this:

AspenProperties.Application.Tree.FindNode("\Data\Properties\Analysis\BINRY-1\Output\Prop Data\PROPTAB\VAPOR MOLEFRAC BENZENE 2").value

But its showing an error: Dot indexing is not supported for variables of this type.

Please help me out

I don't know what DLD is, I haven't modeled microfluidics, I just have to study the process and see how to find the appropriate expressions that simulate it.

So after spending quite some time on this problem, I eventually figured it out and Keivan is completely correct. if you want to solve the full-order P2D model, you should use an iterative solving method.

The P2D model equations have multiple spatial boundary conditions and you can choose one of them as the convergence criterion for the iterative method to minimize. In my experience, after trialing multiple different BCs, one of the best to use is the charge conservation or current conservation BCs at the anode/separator boundary and separator/cathode boundary.

You also need an initial guess to start the iterative method. Multiple different works have used different convergence criteria and initial guesses but it is the engineer's choice what to use. Also, the discretization method is key as well, I personally prefer using FVM as it implements the spatial BCs well. But other works have used FDM and FEM with very good performance as well.

I recently published a paper on my solver for an isothermal P2D model, DOI is below for anyone who is interested.

T. Wickramanayake, M. Javadipour and K. Mehran, "A Novel Root-Finding Algorithm to Solve the Pseudo-2D Model of a Lithium-ion Battery," 2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), Venice, Italy, 2023, pp. 1-6, doi: 10.1109/ESARS-ITEC57127.2023.10114840.

Setting up user-defined trajectories to model handovers between cells, ping-pong handovers, etc, can be achieved using the File-Based Mobility model that NetSim supports. You can configure a specific movement trajectory by providing the appropriate coordinates for the devices in an excel file. What makes this powerful is the convenience it offers in using Excel’s feature set to the input values. Let’s say you want the UE to go from left to right. You can set the initial two coordinates in excel and then use the drag-and-pull functionality in excel to generate the rest.

For more details see the Mobility model help and a simple example involving handover in 5G from the below links: https://tetcos.com/help/v13.3/Technology-Libraries/MANETs.html#file_based_mobility_model https://tetcos.com/help/v13.3/Experiments-Manual.html#simulate_and_study_5g_handover_procedure_level_2

The standard approach used here is that the response of the individual meta-atom is dependent on the neighboring meta-atoms. Under periodic boundary conditions, the unit-cell simulated is assumed to be identical. When the full meta-surface is designed, for any meta-atom, closest neighbors will not be identical generally to induce the desired effect on the electromagnetic field. However, suppose the coupling between the metaatoms is weak enough, or the effect from the neighboring metaatoms is slowly varying the field behavior. In that case, simulated metaatoms can be used as an individual building block of metasurface for calculated effect from periodic boundary conditions. Such results will generally yield nice enough results for holography. To improve the design of the metasurface, there are also several different methodologies, like implementing the Gerchberg Saxton algorithm for several iterations dependent on the desired error or utilizing machine learning tools.

QuestionAnswer @#Google AI BARD "IT" = code "HowWay = Cite" @https://www.researchgate.net/post/Can_anyone_define_how_to_CITE_such_a_reference