Scientific Computing Software Tools

One of the most frustrating parts of early-stage design? You spend more time managing tools than testing ideas. I’d have SketchUp open for massing, GIS for overlays, and Excel for calculations - all running at once. It was clunky, slow, and completely broke my design flow. Recently, I tried Giraffe Technology on a project, and it turned out to be one of the most useful upgrades to my workflow. 🚀 I tested three different design options in one sitting. No switching tools. No reformatting. No waiting. Here’s what stood out: ✅ Instant site analysis with contextual overlays ✅ Real-time solar radiation and shadow studies ✅ Rapid conceptual designs with built-in flexibility ✅ Live yield and area metrics ✅ Export-ready reports ✅ Seamless collaboration with team members My personal favorites? 👉 The Site Analysis Annotations : it pulled together zoning, setbacks, and overlays in one neat layer. 👉 And the Solar Radiation Tool - gave me intuitive, visual insights that usually take hours to compile. If you’re working on anything that involves early-stage planning or site strategy, Giraffe Technology is worth exploring. ✨ Watch the tutorial attached to see how I used it.

⚡ Modeling and Simulation of a Hybrid PV–Wind–Battery–Fuel Cell System Connected to Grid | MATLAB Simulink I’m excited to share a comprehensive MATLAB Simulink project where I modeled and simulated a hybrid renewable energy system combining: ☀️ Solar PV 🌬️ Wind Turbine with MPPT using Optimal Torque Control 🔋 Battery Energy Storage System (BESS) 🔋 SOFC Fuel Cell + DC-DC Boost Converter feeding an 800 V DC grid This hybrid system demonstrates stable grid integration, efficient power flow, and smart energy management, representing a key step toward smart grids and sustainable power systems. 📽️ Full simulation: https://lnkd.in/dA4eVPHq 🔹 PV–Battery System Hybrid PV and BESS connected to the grid for efficient load balancing and renewable energy utilization. 📽️ Full simulation: 👉 https://lnkd.in/dKVsM8n3 🌬️ Wind Energy System Wind turbine with MPPT using optimal torque control to extract maximum power and ensure stable grid integration. 📽️ Watch here: 👉 https://lnkd.in/dSQZrnRJ 🔋 Fuel Cell System (SOFC + Boost Converter) A detailed SOFC fuel cell model (25 kW, 630 V nominal) operating at 860°C, connected to a boost converter delivering a stable 800 V DC bus. Key SOFC parameters: ⚙️ Model: SOFC – 25 kW – 630 Vdc ⚡ Nominal point: 40 A / 630 V 🔋 Max point: 44.7 A / 604 V 🧩 Number of cells: 900 🌡️ Temperature: 860 °C 💨 Air flow rate: 8260 lpm ⚗️ Efficiency: 42% 🔧 Fuel/Air pressure: 1.2 / 1 bar 📽️ Full simulation: 👉 https://lnkd.in/dzM-_5vJ I’m open to collaboration on research projects, MATLAB/Simulink simulations, and renewable energy applications. Feel free to reach out anytime. #MATLAB #Simulink #RenewableEnergy #HybridEnergySystem #PV #SolarEnergy #WindEnergy #BatteryStorage #FuelCell #BESS #SmartGrid #PowerElectronics #EnergyStorage #HybridSystem #CleanEnergy #SustainableEnergy #Engineering #ElectricalEngineering #ControlSystems #Simulation #PVSystem #WindTurbine #FuelCellTechnology #Microgrid #GridIntegration

I’m a power systems modeler and a lot of folks wonder what it is we do all day. I’d like to go over what power systems modeling is and how we do it. Now what is a grid model? It’s a mathematical representation of the grid that can be used in simulations. We take the entire power grid that you see in the field, all those power lines, substations, and generators and convert that into a form that can be used in computer simulations to predict its behavior. As a power systems modeler, we build models of the grid to be used in various simulations. For us a model is a snapshot of the grid at a specific point in time meant to simulate a specific operating state. We are ultimately in the business of collecting data that represents these grid elements, then figuring out how to manage it and store that data before validating it. Then finally we transform the data into different formats for different simulators. Power systems modelers typically have to work in an interdisciplinary manner combining both power systems engineering and software development. You need to know how devices operate at a fundamental level but also how to collect and manage large amounts of data using programming and software development skills. Knowing programming languages like SQL, Python, Java, C#, and more are very useful in this role. You may use software like PSSE, PowerWorld, TARA, PSLF, Aspen Oneliner, CAPE, PSCAD, or EMTP. You may also use the Common Information Model or CIM to convey operational modeling information. On a daily basis the work is very cross-functional. A modeler is typically gathering data from many other stakeholders both internal and external. Then they need to understand their customers needs to deliver the models they need to plan and operate the grid. To do this you’ll have to spend a lot of time communicating with stakeholders. You’ll need to work with engineers who design infrastructure, planners, operators, and more. Ultimately the models we develop can be used in both the operations and planning horizons. These models may include steady state, dynamic, short circuit, electromagnetic transient, geomagnetic disturbance, market models, operations models, and more. At the end of the day we create a model representing the grid as shown in the image below for many different softwares. It’s a constant effort to keep the models up to date as the grid changes. But ultimately our work forms the foundation of reliable power system operations and planning. Without good models every study would be wrong. #powersystems #powersystemsmodeling #engineering

🔋 How to Size Solar Panels Using PVsyst — A Beginner’s Guide Designing a solar system without proper panel sizing is like building a house without a foundation. ⚡ Luckily, PVsyst makes this process structured and accurate. Here’s a simple breakdown of how to size solar panels using PVsyst 👇 🌞 1. Define Project Site & Irradiation Choose your location or import a custom meteo file. PVsyst auto-generates solar radiation data — this defines how much sunlight is available. 🧱 2. Input System Constraints Decide whether it's a grid-tied, off-grid, or hybrid system. Set desired system size (e.g., 100 kW), or let PVsyst calculate it based on energy needs. 🔋 3. Choose Panel Specs Select a PV module from the database or enter custom specs (Wattage, Voc, Isc, etc.) Define the number of panels in series & parallel to match your inverter’s input range. ⚙️ 4. Optimize Tilt & Azimuth Set tilt based on latitude or optimize using simulation. Define azimuth (angle from south) to improve annual yield. 🔌 5. Match with Inverter Choose an inverter from the library. Ensure your string configuration is compatible with its voltage & power range. 📈 6. Run Simulation & Analyze Losses PVsyst provides a detailed loss diagram: mismatch, shading, temperature, wiring losses, etc. You get the final expected energy output (kWh/year). 📉 Result? A realistic system sizing report that helps: ✅ Clients understand expected generation ✅ Designers avoid oversizing or inverter mismatch ✅ Installers reduce surprises on-site --- 💬 Want a complete PVsyst sizing report template or help with a simulation? Drop a comment or DM me — I’d be glad to share! 📞 Let’s connect! 🔹 WhatsApp: +923073558882 🔹 Email: imamsolardesign31525@gmail.com 🔹 Instagram: @solar_design_engineer #PVsyst #SolarDesign #SolarEngineering #FreelancingEngineers #RenewableEnergy #SolarPanels #SystemSizing #ElectricalEngineering #CleanEnergy #FreelancerTips #PakistanSolar

Hydrogen System Simulation Software [Updated Dec. 2024) 🟦 1) Aspentech - Industrial scale alkaline electrolysis analysis with Aspen HYSYS/ Aspen Plus - Green ammonia production via electrolysis analysis with Aspen HYSYS - Hydrogen supply chain analysis with carbon emission reduction with Aspen PIMS-AO 🟦 2) MathWorks Simulink® Software Simscape Electrical™ and Simscape Fluids™ provide model libraries for hydrogen electrolyzer simulation. You can employ these models to study the hydrogen electrolyzer as an electric load within a more extensive electrical system. With Simulink®, you can use electrolyzer models to examine electrolyzer system controls, such as: - closed-loop controls, and - supervisory logic design. 🟦 3) ProSim Software ProSim added an electrolyzer module to the latest version of its ProSimPlus software. By combining this module with other equipment from the ProSimPlus library, you can simulate and optimize the hydrogen production system (Alkaline electrolyzer, PEM Electrolyzer and SOEC Electrolyzer) by water electrolysis. 🟦 4) Ansys Fluent Hydrogen Simulation Ansys Fluent hydrogen simulation include: - Green hydrogen production, - Hydrogen consumption in fuel cells. 🟦 5) Dymola Tool Dymola is a modelling and simulation tool utilised to model-based design complex systems. The Hydrogen library includes features for modelling PEM fuel cell stacks and fuel cell systems. 🟦 6) Flownex® Software Flownex® can be utilised for hydrogen electrolyzer modelling: - Analysis of system behaviour at critical modes - Plant optimization - Cooling loops - Sensor failure simulation - Product design 🟦 7) TRNSYS Transient System Simulation Tool With TRNSYS (a transient system simulation program), you can conduct parametric analyses to discover possible system configurations. TRNSYS models a hydrogen system consisting of the following: - electrolyzers, - a photovoltaic (PV) cell array, - fuel cell, - hydrogen (H2) storage, - lead-acid battery, - catalytic burner, - DC/AC inverters, diodes, - DC/DC converters, - solar collector, and - water storage tank. 🟦 8) COMSOL Multiphysics® software The Fuel Cell & Electrolyzer Module is an add-on to the COMSOL Multiphysics® software that allows you to better understand fuel cells, electrochemical cells and electrolyzer systems. The hydrogen system in COMSOL includes: 1- hydroxide exchange (alkaline) fuel cells (AFCs), 2- Proton exchange membrane fuel cells (PEMFCs), 3- Solid oxide fuel cells (SOFCs), 4- Alkaline electrolyzers (AEs), and 5- PEMs electrolyzers, 6- Solid oxide electrolysis cells (SOECs). 🟦 9) OpenFOAM First released as open source by OpenCFD Ltd. in 2004, it has since become the leading software for Computational Fluid Dynamics, supported by partnerships in industry and academia. This post is based on my experience and is for educational purposes only. 👇 Which software do you utilize for simulating hydrogen systems?

Once again, another study shows that the RETScreen Software, which uses monthly data for energy simulation, produces equal or more accurate results than an hourly simulation tool - in this case, PVsyst. Unfortunately, there is still a misconception in industry and academia that hourly energy simulations produce more accurate results than energy simulations using monthly data. This results in significantly wasted time and money at the feasibility stage. Working with monthly data (12 data points) vs. hourly data (8,760 data points) is far easier, faster, cheaper and less prone to user input errors. And monthly energy simulations produce equal or more accurate results than hourly simulations. That is why we intentionally limited energy simulations in RETScreen to monthly input data. See the study attached for an existing 7 MW photovoltaic (PV) plant in Malbaza, Niger. It compares measured data from the plant over three years with the simulated output of these two energy simulation tools. According to the authors: We obtained a mean bias error (MBE) of 5.81% (PVsyst) and 0.14% (RETSceen) and a normalized mean bias error (NMBE) of 3.81% (PVsyst) and 0.27% (RETScreen). There is good agreement between the experimental measurements and the theoretical values. The RETScreen software has less mean bias error (MBE) and normalized mean bias error (NMBE) than PVsyst, giving a better estimate of the real values.

Keysight Technologies Releases Quantum Circuit Simulation Tool with Breakthrough Flux Quantization Capability Keysight Technologies, Inc. has introduced Quantum Circuit Simulation (Quantum Ckt Sim), an advanced environment designed to accelerate the development of superconducting quantum circuits. This innovative tool sets a new industry standard by incorporating frequency-domain flux quantization, a critical feature achieved through a collaboration with Google Quantum AI. Key Features and Capabilities 1. Flux Quantization Modeling: • The simulation accurately models magnetic flux quantization in superconducting loops, a fundamental property that ensures precise circuit functionality in quantum computing. • By addressing this challenge, the tool improves the resilience and efficiency of quantum circuit designs. 2. Partnership with Google Quantum AI: • The collaboration enables the integration of advanced flux quantization techniques into circuit solvers. • This partnership enhances simulation fidelity, providing researchers with robust tools to optimize superconducting quantum circuits. 3. Technical Milestone: • Detailed in the academic paper “Modeling Flux-Quantizing Josephson Junction Circuits in Keysight ADS”, the methodology demonstrates substantial advancements in Josephson junction circuits, a cornerstone of quantum computing. Impact on Quantum Computing • Precision and Efficiency: The novel approach empowers researchers to design circuits that can operate reliably under quantum conditions, improving scalability for next-generation quantum technologies. • Accelerated Development: By leveraging frequency-domain tools, the solution reduces the time required to develop and test complex circuits. • Enhanced Superconducting Technologies: The tool enables detailed simulations that are critical for creating resilient systems capable of overcoming challenges such as noise and decoherence. Setting a New Benchmark Keysight’s Quantum Circuit Simulation represents a transformative leap in quantum circuit design, establishing a higher standard for modeling and simulation in the quantum industry. The ability to precisely quantify flux in the frequency domain not only strengthens superconducting circuit research but also advances the entire field of quantum computing. As quantum technologies evolve, this tool is expected to play a pivotal role in shaping the future of high-performance quantum systems.

🚀 Super excited to share our latest paper from the BlueQubit team! We’ve just published a new method and open-source SDK for Pauli Path Simulation (PPS) — a hardware-agnostic, scalable, and transparent classical simulator capable of modeling utility-scale (50+ qubit) quantum circuits, including IBM’s 127-qubit kicked Ising model from their 2023 Nature paper. 🧠 PPS sits at the intersection of quantum hardware and classical algorithmic innovation. Unlike other simulation methods that require hardware-specific tuning, PPS is general-purpose — making it a valuable tool for: 📍 Benchmarking and validating quantum experiments 📍 Guiding the next wave of quantum advantage claims 📍 Establishing rigorous classical baselines for quantum utility ⚛️ PPS empowers researchers to explore the true boundary where classical simulation ends and quantum advantage begins. We believe tools like this are essential for building trust and confidence in the progress of quantum computing. 🔍 Curious about how PPS works or how to get started? Check out our paper 🔗 https://lnkd.in/dGj9DaN5 #QuantumComputing #QuantumAdvantage #PauliPathSimulation #BlueQubit #QuantumResearch #HighPerformanceComputing #OpenSource

📣Exciting News! 🚀Keysight Technologies has just formally introduced Quantum Circuit Simulation the First Circuit Environment with Frequency-Domain Flux Quantization. 💡In the realm of superconducting quantum circuits, accurately modeling flux quantization is paramount. This fundamental property ensures that the magnetic flux through a superconducting loop is quantized in discrete units, a critical aspect for the operation of quantum circuits. Google Quantum AI and Keysight have collaborated to address this challenge and enhance quantum circuit simulations through the integration of frequency-domain flux quantization into circuit solvers. By precisely modeling flux quantization, the new solution enables researchers to design more reliable and efficient superconducting circuits. ✨It’s thrilling to witness the accurate modeling of frequency domain flux quantization of superconducting circuits using an EDA tool for the first time. This significant milestone leverages EDA capabilities to streamline the design of superconducting microwave circuits for quantum applications and beyond. We anticipate this advancement will empower quantum engineers to enhance the performance of parametric quantum circuits, particularly in terms of power handling and bandwidth, which are crucial for the readout of qubits in quantum computers. 1️⃣ For the press release, visit https://lnkd.in/gtQFRhHk 2️⃣ For Quantum Ckt Sim, visit https://lnkd.in/grCGUZs3 🔎 If you haven’t checked out the technical article, "Modeling Flux-Quantizing Josephson Junction Circuits in Keysight ADS," I highly recommend giving it a read, https://lnkd.in/gR_zrrSv 👉Try it out today and experience the future of quantum design! #QuantumEDA #QuantumCktSim #QuantumTechnology #QuantumComputing