System Dynamics Simulation

🔄 Power System Dynamic Simulation: Cracking the DAE Code! ⚡ Ever wondered how we simulate faults, switching events, or dynamic responses in power systems? Behind the scenes, it's all about solving Differential-Algebraic Equations (DAEs) — a powerful mathematical framework that blends electrical physics with numerical methods! 🧠⚙️ Here’s a crisp breakdown of the simulation journey ⬇️ 🔧 1: Model the Subsystems We start by modeling each component using physics-based equations: 🔹 Synchronous Generators (in dq0 reference frame) 🔹 Excitation systems 🎛️ 🔹 Turbine-Governor dynamics ⚙️ 🔹 Transmission Network & Loads (static ZIP & dynamic motor) 🔹 Optional: HVDC systems & FACTS devices (like STATCOM, TCSC, etc.) Each of these has its own differential or algebraic equations depending on whether they have dynamic states or steady constraints. 🌀 2: Axis Transformation Since dq axes differ per generator, we align all machine variables to the common network reference frame (R-I). This requires a transformation like: 📐 ER = Ed·sinδ + Eq·cosδ 📐 EI = Eq·sinδ - Ed·cosδ So now, everyone speaks the same "electrical language"! ⚡🗣️ 📚 3: DAE Framework! All those individual models come together into a DAE system: 🔸 Differential equations for dynamic components (like rotor angle, speed, field current):   📌 𝑥̇ = f(x, V) 🔸 Algebraic equations for the network (Kirchhoff’s laws, load flow, constraints):   📌 I = g(x, V) = Y·V Where: 🧮 x = system state vector (machine states, control system states) 🔌 V = bus voltage vector (real & imaginary) ⚡ I = current injections into the network 📊 Y = network admittance matrix Together, these form the full picture of system dynamics and network interactions. ⏱️ 4: Solve It Smartly! Two major solution approaches: 1️⃣ Partitioned Solution with Explicit Integration  • Solve differential & algebraic equations separately but iteratively  • Methods like Runge-Kutta help simulate time steps 2️⃣ Simultaneous Solution with Implicit Integration  • Solve the whole DAE set in one go using methods like Backward Euler  • More stable, especially for stiff systems 💥 At fault inception, voltages may change instantly, but state variables (like δ, ω, Eᶠ) remain continuous — a key assumption in simulations! 🎯 Big Picture When all subsystems are synchronized via the DAE framework, we can simulate: ⚡ Faults 🔁 Transients 🎯 Stability margins 🧪 Control system responses …all from millisecond to minute-level timeframes. Power system dynamic simulation isn't just about solving equations — it's about replicating how the grid thinks, reacts, and stabilizes in real time. As grids become more complex with renewables, HVDC, and FACTS, mastering this integrated DAE approach is essential for building resilient and intelligent power systems of the future. ⚡📈🌍 https://lnkd.in/gqD6ygpC

New paper out in Urbanization, Sustainability and Society #USS Emerald Publishing Amir Ghaffarianhoseini Proud to see this work coming out of AUT School of Future Environments, where our focus sits at the intersection of #resilience, #sustainability, and #decision #quality in the #built #environment. #Whole #life #cost is often treated as a spreadsheet exercise. Our research approaches it as a systems problem. Early design and construction decisions such as #material #durability, #seismic #detailing, #energy #strategy, and #maintenance #technologies propagate through feedback loops and time delays, shaping cost outcomes many years later. New Zealand makes this dynamic highly visible. Seismic exposure, climate variability, regulatory shifts, supply chain constraints, and occupant behaviour interact in ways that static WLC methods struggle to capture. What we did #SystematicLiteratureReview #IndustryInterviews #NewZealandStakeholders #QuantitySurveyors #ProjectManagers #Architects #Engineers #FacilityManagers #Regulators #Homeowners #EightyInfluencingFactors #AnalyticHierarchyProcess #CausalLoopDiagrams #StockAndFlowModelling #SystemDynamics What emerged #FeedbackLoops #ReinforcingDynamics #BalancingDynamics #SeismicResilience #EnergyEfficiency #TechnologyDepreciation #OccupantBehaviour #MaintenanceCycles #ContextSpecificFactors #RegionalRisk #BehaviouralDrivers #ActionableModelling #LifecycleCostBehaviour This is the kind of work we actively encourage and grow within #SoFE. Research that connects theory, modelling, and real industry conditions to improve how decisions are made across the lifecycle of buildings. Samadhi Samarasekara Purushothaman Mahesh Babu Dr. Funmilayo Ebun Rotimi #SchoolOfFutureEnvironments #AUT #WholeLifeCost #LifeCycleCosting #SystemDynamics #HousingAffordability #SustainableConstruction #Resilience #BuiltEnvironment #ResearchImpact

𝙒𝙝𝙖𝙩 𝙞𝙛 𝙮𝙤𝙪𝙧 𝙛𝙖𝙘𝙩𝙤𝙧𝙮 𝙤𝙣𝙡𝙮 𝙬𝙤𝙧𝙠𝙨 𝙗𝙚𝙘𝙖𝙪𝙨𝙚 𝙧𝙚𝙖𝙡𝙞𝙩𝙮 𝙝𝙖𝙨𝙣’𝙩 𝙩𝙚𝙨𝙩𝙚𝙙 𝙞𝙩 𝙮𝙚𝙩? Most plants look stable— until demand shifts, a resource slips, or variability shows up where no one expected it. That’s when leaders realize the system wasn’t designed for reality. It was designed for assumptions. This is why simulation-based decision making—especially Discrete Event Simulation (DES)—has become essential for smart plants. Not to predict the future. But to stress-test the system before the system is forced to respond. Here’s what DES actually validates—end to end: 1️⃣ 𝙋𝙧𝙤𝙘𝙚𝙨𝙨 𝙁𝙡𝙤𝙬 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙖𝙩𝙞𝙤𝙣 DES shows how material and information truly move—not how the routing sheet claims they do. 2️⃣ 𝙀𝙦𝙪𝙞𝙥𝙢𝙚𝙣𝙩 𝙐𝙩𝙞𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣 𝘼𝙣𝙖𝙡𝙮𝙨𝙞𝙨 High utilization can hide starvation and blocking. DES exposes when assets look busy but flow is unhealthy. 3️⃣ 𝘽𝙤𝙩𝙩𝙡𝙚𝙣𝙚𝙘𝙠 𝙄𝙙𝙚𝙣𝙩𝙞𝙛𝙞𝙘𝙖𝙩𝙞𝙤𝙣 Constraints aren’t static. DES reveals where the bottleneck migrates under different conditions. 4️⃣ 𝙋𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙤𝙣 𝘾𝙖𝙥𝙖𝙘𝙞𝙩𝙮 𝙋𝙡𝙖𝙣𝙣𝙞𝙣𝙜 Capacity isn’t a fixed number. DES models how throughput behaves under variability, downtime, and mix changes. 5️⃣ 𝘽𝙪𝙛𝙛𝙚𝙧 𝙎𝙞𝙯𝙞𝙣𝙜 Too much buffer masks instability. Too little amplifies it. DES finds the point where flow stays resilient. 6️⃣ 𝘾𝙮𝙘𝙡𝙚 𝙏𝙞𝙢𝙚 𝘿𝙞𝙨𝙩𝙧𝙞𝙗𝙪𝙩𝙞𝙤𝙣 Averages lie. DES reveals the spread—and where volatility is introduced. 7️⃣ 𝙍𝙚𝙨𝙤𝙪𝙧𝙘𝙚 𝘼𝙡𝙡𝙤𝙘𝙖𝙩𝙞𝙤𝙣 People, machines, and automation interact as a system. DES tests the balance before locking it in. 8️⃣ 𝘿𝙚𝙢𝙖𝙣𝙙 𝙁𝙡𝙤𝙬 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙖𝙩𝙞𝙤𝙣 DES connects demand patterns to execution reality—without overloading the system. 9️⃣ 𝙏𝙧𝙞𝙖𝙡 𝘽𝙪𝙞𝙡𝙙 𝙎𝙘𝙚𝙣𝙖𝙧𝙞𝙤 𝘼𝙣𝙖𝙡𝙮𝙨𝙞𝙨 Instead of learning after launch, DES lets teams explore “what if” scenarios before they become problems. 🔟 𝘿𝙖𝙩𝙖-𝘿𝙧𝙞𝙫𝙚𝙣 𝙄𝙣𝙫𝙚𝙨𝙩𝙢𝙚𝙣𝙩 𝘿𝙚𝙘𝙞𝙨𝙞𝙤𝙣𝙨 Every capex decision is validated against system behavior—not isolated ROI logic. This is the real shift leaders are making: 𝙁𝙧𝙤𝙢 𝙩𝙧𝙞𝙖𝙡 𝙗𝙪𝙞𝙡𝙙𝙨 → 𝙩𝙤 𝙫𝙖𝙡𝙞𝙙𝙖𝙩𝙚𝙙 𝙨𝙘𝙚𝙣𝙖𝙧𝙞𝙤𝙨 𝙁𝙧𝙤𝙢 𝙤𝙥𝙞𝙣𝙞𝙤𝙣𝙨 → 𝙩𝙤 𝙚𝙫𝙞𝙙𝙚𝙣𝙘𝙚 𝙁𝙧𝙤𝙢 𝙛𝙞𝙧𝙚𝙛𝙞𝙜𝙝𝙩𝙞𝙣𝙜 → 𝙩𝙤 𝙙𝙚𝙨𝙞𝙜𝙣𝙚𝙙 𝙨𝙩𝙖𝙗𝙞𝙡𝙞𝙩𝙮 Simulation doesn’t improve factories. It reveals whether the system was ever ready. 𝙄𝙛 𝙮𝙤𝙪’𝙧𝙚 𝙨𝙘𝙖𝙡𝙞𝙣𝙜 𝙥𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙤𝙣, 𝙞𝙣𝙩𝙧𝙤𝙙𝙪𝙘𝙞𝙣𝙜 𝙖𝙪𝙩𝙤𝙢𝙖𝙩𝙞𝙤𝙣, 𝙤𝙧 𝙧𝙚𝙗𝙖𝙡𝙖𝙣𝙘𝙞𝙣𝙜 𝙘𝙖𝙥𝙖𝙘𝙞𝙩𝙮— 𝙩𝙝𝙚 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣 𝙞𝙨𝙣’𝙩 𝙘𝙖𝙣 𝙩𝙝𝙚 𝙡𝙞𝙣𝙚 𝙧𝙪��?

If production is noisy and decisions are time-bound, then waiting on full high-fidelity runs costs uptime and raises risk. A better path is pairing deep physics with fast models so your team can read asset health from the data you already collect.     Here’s how that looks in practice. Use computational fluid dynamics to understand flow behavior and temperature gradients in a heat exchanger, then correlate those temperatures with stress through finite element analysis. Train a reduced-order model on a small set of known operating cases, validate against measured temperatures, and run it live. The ROM turns sensor temperature histories into stress response, updated fatigue life, and remaining life so engineers can plan operation and maintenance in real time.     The same approach applies to subsea thermal management. Build a system simulation of a jumper, tune local heat transfer coefficients with one benchmark case, and validate against several more. That calibration aligns the fast model with high-fidelity results and gives clear guidance on the no-touch period and hydrate risk under changing conditions.     Here's how to begin: start with a few trusted scenarios, train a reduced model, connect it to your sensors, and publish one view your operators can use today: stress, remaining life, and the safe operating window.     If you want a simple way to move from raw readings to decisions that protect uptime, let’s compare notes. 

BHA Dynamic Behavior: If You’re Not Modeling It, You’re Guessing Most drilling dysfunctions are not bit problems, motor problems, or tool problems. They are system behavior problems. A BHA is not static. It is a coupled, non-linear mechanical system where axial, torsional, and lateral motions interact continuously while drilling. Static BHA analysis only describes shape. It does not describe performance. Axial instability shows up as bit bounce when applied WOB exceeds the bit’s real cutting capacity. The result is unstable ROP, shock loading on MWD/LWD, and cumulative cutter damage that rarely shows up immediately. Torsional instability, stick–slip, remains the most destructive and least controlled dysfunction in drilling. When downhole torque demand exceeds available torque at the bit, energy is stored in the drillstring and released violently. Bit overspeed, motor failures, and erratic toolface behavior are the predictable outcome not bad luck. Lateral instability is equally unforgiving. Off center rotation and backward whirl generate high alternating stresses that rapidly wear stabilizers, enlarge the hole, and drive fatigue failures. By the time it’s visible at surface, the damage is already done. What makes BHA dynamics dangerous is their non-linearity. Small changes in RPM or WOB can move the system from stable to destructive in minutes. Averaging formation properties or relying on ��experience” is not control its exposure. Dynamic modeling turns drilling from reactive to deliberate. It defines stable operating windows, supports stabilizer and BHA design decisions, and justifies motor or RSS selection even in vertical sections. Without dynamic modeling: • Parameters are adjusted after tools are damaged • ROP is sacrificed to protect hardware • Failures are misdiagnosed and repeated With dynamic modeling: • Drilling stays inside stable regimes • High ROP is maintained without punishment • Tool life and reliability improve measurably Downhole performance is governed by physics, not optimism. If you’re not modeling BHA dynamics, you’re not managing risk you’re accepting it.

Just published a new article I wrote on one of the most critical (yet often under-appreciated) aspects of today’s power systems: Dynamic and Transient Simulations. As we integrate more renewables, energy storage, and power electronics into the grid, the old steady-state assumptions no longer cut it. Accurate dynamic and transient modeling has become the backbone for ensuring stability, resilience, and reliable operation—whether we’re talking about generator rotor angle stability, fault ride-through of inverter-based resources, or sub-synchronous oscillations. In the article, I dive into: • Why dynamic and transient simulations are indispensable in modern grid planning and operation • The key differences in how various software tools (PSSE, PowerFactory, PSCAD/EMTDC, ETAP, etc.) model these phenomena • Practical insights on choosing the right tool and modeling approach depending on the study objective If you’re involved in power system studies, protection design, grid code compliance, or renewable integration, this one’s for you. 👉 You can read the full article here: https://lnkd.in/d4_ha53Q #PowerSystems #TransientSimulation #DynamicSimulation #RenewableIntegration #GridStability #PowerEngineering

We often come across the 𝙎𝙞𝙢𝙥𝙡𝙚 𝙋𝙚𝙣𝙙𝙪𝙡𝙪𝙢 early in our engineering journey. It is predictable, rhythmic, and follows a clean, elegant path. But in the real world, adding just one or two more variables changes everything. As you can see in today's numerical showcase, what starts as a perfect oval in a phase portrait quickly transforms into a complex "tangled web." This is the threshold where order meets 𝘾𝙝𝙖𝙤𝙨 𝙏𝙝𝙚𝙤𝙧𝙮. It’s a powerful visualization of how increasing the degrees of freedom (DOF) can cause the complexity of mechanical systems to grow exponentially, turning predictable motion into rich and highly sensitive dynamics. For a simple pendulum, the motion is predictable and governed by: θ¨ + (g/L) sin(θ) = 0 However, once multiple pendulums are coupled together, the equations become strongly nonlinear and extremely sensitive to initial conditions, which is a classical example of deterministic chaos. The phase-space plots (velocity vs displacement) clearly show the transition: • Simple pendulum → closed periodic orbit • Double pendulum → quasi-chaotic trajectories • Triple pendulum → highly complex chaotic motion Although pendulum systems appear simple, they are fundamental in studying: • Nonlinear dynamics • Energy transfer • Multibody dynamics • Chaos theory • Numerical stability in time integration All simulations were performed using explicit solver in LS-DYNA with all parts modelled as rigid bodies. The keyword *𝗖𝗢𝗡𝗦𝗧𝗥𝗔𝗜𝗡𝗘𝗗 𝗝𝗢𝗜𝗡𝗧 𝗥𝗘𝗩𝗢𝗟𝗨𝗧𝗘 was used to model the connection between the pendulum rods. To learn more about the available constraints options in LS-DYNA, check out the examples here: https://lnkd.in/gSHjPnNh #LSDYNA #Simulation #FiniteElementAnalysis #ChaosTheory #NonlinearDynamics #MultibodyDynamics #EngineeringSimulation #CAE #MechanicalEngineering #NumericalSimulation

Nonlinear Structural Dynamic Time History Analysis: Newmark + Newton–Raphson Nonlinear dynamic time history analysis allows engineers to simulate real structural behavior during earthquakes, capturing: • Material yielding • Stiffness degradation • P-Δ effects • Energy dissipation The governing dynamic equilibrium equation is: M * u¨(t) + C * u˙(t) + f_s(u,t) = P(t) Where: M = mass matrix C = damping matrix u = displacement u˙ = velocity u¨ = acceleration f_s(u,t) = nonlinear restoring force P(t) = external dynamic load Because f_s depends on displacement, the system is nonlinear and must be solved iteratively at each time step. Direct Time Integration – Newmark Method Displacement update: u(t+Δt) = u(t) + Δt·u˙(t) + (0.5 − β)Δt²u¨(t) + βΔt²u¨(t+Δt) Velocity update: u˙(t+Δt) = u˙(t) + (1 − γ)Δt u¨(t) + γΔt u¨(t+Δt) Typical parameters: γ = 0.5 β = 0.25 These provide stable time integration for structural dynamic analysis. Newton–Raphson Iterative Equilibrium Residual force: R_i = P(t+Δt) − f_int(u_i) − M u¨_i − C u˙_i Correction equation: K_t Δu_i = R_i Update: u_(i+1) = u_i + Δu_i Iterations continue until force or displacement convergence is reached. Coupled Solution Process 1. Newmark predicts response for the next time step 2. Internal forces are evaluated 3. Newton–Raphson iterations enforce equilibrium 4. Tangent stiffness is updated 5. Process repeats until convergence This predictor–corrector framework is fundamental to performance-based seismic design, enabling engineers to realistically evaluate structural response and resilience. #StructuralEngineering #EarthquakeEngineering #StructuralDynamics #SeismicDesign #PerformanceBasedDesign

Simulation-Based Scientific Analysis of Production Lines For Finding Most Effective Improvement Opportunities: Lean manufacturing professionals focus on: 1. Finding and reducing waste in systems and improving flow on a continuous basis  2. Improvement of human aspects and work culture in systems 3. Improvement of leadership skills. However, in lean manufacturing, I do not find much enthusiasm for systems analysis in a scientific way for more efficient improvement drive. Although there is a feverish pitch for flow, waste elimination and continuous improvement in Lean, scientific analysis of production is mostly ignored in Lean. Lack of scientific analysis prompts people to pursue every visible improvement opportunity without any advance assessment of the impact on system KPIs. For scientific analysis of a certain type of production lines, nowadays I am proposing simple, easy, fast, effortless, discrete event simulation "without a need for animation". Educated engineers and managers can quickly and effortlessly run simulations for those production lines without any simulation knowledge using simple, powerful and scientific tools like FlowshopSim. The simulation model in FlowshopSim for those production lines considers the following 7 factors. 1. Number of parallel resources at workstations 2. Allowed inventory levels at workstations 3. The average cycle times at workstations 4. Statistical variation in cycle times 5. Resource speeds 6. Resource failures and repairs 7. Resource calendars. These factors have interaction effects on production KPIs. High speed discrete event simulation in FlowshopSim helps with extensive analysis of a production line with respect to the 7 factors. It will enhance users' understanding of the dynamic nature of the production line and helps with identifying most effective improvement opportunities through simulation-based what-if analysis. Recently, a few people witnessed simulation and analysis of production lines in FlowshopSim through my one-on-one demonstrations over web. They include Ivan Lim, Vijayakumar P, Jean-Pierre Goulet, P. Eng., M. Sc. A., Zafar Inayat and ROHIT VAIKUNTH SARDESAI. I am sure they noticed the simplicity, versatility, speed and power of FlowshopSim for simulation and analysis of production lines. I do not believe there is another tool that is as simple, fast, versatile and powerful tool as FlowshopSim for simulating and analyzing production lines with respect to the above-mentioned factors. An old YouTube video of FlowshopSim shows an old, crude version of FlowshopSim. #kaizen #continuousimprovement #productionline #taktplanning #manufacturing #operationsmanagement