Summary of "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

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.
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.
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.
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.
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)