How to advance engineering simulation for energy transition and decarbonization - full webinar

How to advance engineering simulation for energy transition and decarbonization (webinar)

Advanced engineering simulation is a core enabler for the energy transition, decarbonization and ESG goals — turning increasing complexity (big data, multi‑physics, HPC, cloud) into competitive advantage via integrated simulation workflows and digital threads.

Overview

• The webinar positioned simulation tools (within Siemens Xcelerator) as part of an end‑to‑end digital engineering strategy spanning R&D, design and operations.
• Two primary use patterns were highlighted:
  1. Move simulation earlier in product/plant/system development for what‑if studies and design‑space exploration.
  2. Reuse design models as operational digital twins for real‑time decisioning, monitoring and control.

Key industry trends & challenges

• Pressures driving adoption:
  • Demand for superior financial performance, accelerating digitalization, and urgency to meet Paris/net‑zero targets by 2050.
• Grid transformation:
  • Decentralization (e.g., rooftop solar) creates stability challenges (frequency control) and requires grid expansion plus digital approaches (smart meters, grid digital twins).
• Skills gap:
  • Value from modelling is often limited by operational users’ lack of expertise — training and change management are essential.
• Complexity management approaches discussed:
  • Multi‑physics simulation, collaborative/cloud workflows, design‑space exploration, reduced‑order models (ROMs) and federated simulations.

Data / analyst highlights

• Aberdeen Group survey results cited:
  • 73% use simulation earlier in development.
  • 53% saw benefits from collaborative simulation.
  • 49% are combining more physics in models.
  • 42% are capturing simulation expertise for reuse.

Simulation approaches, capabilities & example tools

• Systems‑level (system‑of‑systems) modeling:
  • Technical‑economic studies, multi‑criteria decision analysis and optimization across energy systems (example tool: Amesim).
• Detailed multi‑physics design models:
  • CFD, conjugate heat transfer, chemical/phase‑change models for component/asset design and safety (example: STAR‑CCM+).
• Reduced‑order and surrogate models:
  • Accelerate design‑space exploration and operational deployment with acceptable accuracy for many optimization tasks.
• Federated simulations:
  • Combine detailed models and ROMs to study larger systems and run faster scenario sweeps.
• Digital twins & operational models:
  • Reuse reduced/optimized models for real‑time monitoring, sensor fusion, scenario planning and control optimization.
• Cloud‑enabled collaborative simulation:
  • Improves change management, data reuse and remote teamwork.
• Design space exploration / automated optimization:
  • Discover non‑intuitive solutions and reduce human bias.

Case studies & examples

1. Battery / grid storage — techno‑economic analysis for liquid flow battery manufacturing
   • Coupled system performance models (Amesim) plus NPV and investment studies identified a strong business case.
2. Wind‑farm layout optimization — reduced‑order modeling
   • Complex CFD wake models reduced to a 5‑variable ROM with ~0.5% accuracy to speed layout optimization.
3. Battery‑powered trains — system modeling for battery sizing
   • Modeled gradients, stop patterns, charging windows and infrastructure to size batteries that meet operational and space constraints.
4. Large hydrogen fuel cell design and safety
   • STAR‑CCM+ CFD used to optimize flow uniformity and catalyst life; safety analyses included dispersion, flammability, deflagration‑to‑detonation transition risk and FEA for vessel integrity.
5. Liquid hydrogen sloshing in aircraft tanks (detailed example)
   • Problem: sloshing‑induced boil‑off/pressurization risk for fuselage tanks.
   • Approach: multi‑phase CFD + conjugate heat transfer, validated with surrogate liquid‑nitrogen experiments.
   • Computational innovation: decoupled fluid‑flow and transient heat‑transfer timescales to achieve >100× speed‑up with controlled accuracy trade‑offs; used STAR‑CCM+ with Rohsenow boiling closure and custom closure improvements.
   • Result: sloshing reduced tank pressurization contrary to initial concern; baffles would have worsened heat paths — an important design insight.

Technical & operational recommendations

• Use a hybrid approach of detailed models and ROMs to balance fidelity with compute time; where possible, reuse design models as operational digital twins.
• Leverage cloud and collaborative platforms to scale access, capture expert knowledge and mitigate workforce turnover.
• Validate multi‑physics models with surrogate experiments when direct data is scarce; be prepared to develop closure models or user‑defined functions (UDFs).
• Invest in training and change management for operational users so system models yield actionable outcomes.

Product / tool mentions & resources

• Siemens Xcelerator platform (digital threads & integrated toolset)
• Siemens Advanced Engineering Simulation (brand/offerings)
• STAR‑CCM+ (CFD, multiphase, conjugate heat transfer)
• Amesim (system modeling)
• Design exploration and cloud‑enabled simulation/test solutions (Siemens offerings)
• Additional resources and trials: sw.siemens.com
• Upcoming related webinars referenced: operational excellence; integrated design & configuration; digital lifecycle excellence

Main speakers & sources

• Host: Sarah Allspaw (Siemens)
• Speakers:
  • John Lusty — Energy & Utilities Industry Marketing Lead, Siemens Digital Industry Software
  • Dr. Simon Rees — Projects Director, Norton Straw Consultants / Element Digital Engineering (Element Materials Technology)
• Referenced analysts & organizations: Aberdeen Group, International Energy Agency (IEA), Aerospace Technology Institute, Dutch government study, research by Stanley & Ning (wind layout ROM example)

Contact & follow‑up

• Recording and follow‑up email were promised.
• Contact: sarah.allspaw@siemens.com
• LinkedIn connections encouraged.