Fidelity Matters: What “High-fidelity” Really Means
Most simulation vendors say they provide high-fidelity power plant simulators, but what does the term “high-fidelity” really mean? Everyone is abuzz about the “Digital Twin,” and the soft copy of your plant that enables a wide variety of engineering and optimization applications. However, not all twins are identical.
In the nuclear power realm, the ANSI/ANS 3.5 standard essentially defines high-fidelity for operator training simulators. The simulator must perform with certain tolerances, and no negative training can occur because of differences between simulator and plant response. Moreover, performance must be in real-time and repeatable. For fossil fuel power plant simulators, the ANSI/ISA 77.20 standard provides functional requirements and addresses fidelity in both physical and functional realism.
Realistically, however, the question of fidelity is often tied directly to purse strings. Some plants opt for low to medium fidelity simulators because they initially appear to be a lower cost option. But, as Richard W. Vesel notes in his article “Power Plant Training Simulators Explained” (Power Magazine 12/01/2013), “Cost notwithstanding, many users choose high-fidelity simulators to train operators so that the trainees get as deeply exposed to the plant as possible, without actually touching it.”
If you require a high-fidelity power plant simulator, either by law or by virtue, keep in mind that not all high-fidelity simulators are created equal. Fidelity is essentially a combination of three elements:
- Technology – the engineering quality of the equations used in the modeling tools
- Engineering Rigor – the thoroughness of the model and the scope to which plant systems are modeled
- Experience – the experience of the simulation engineer and the “test operator” to assure realism in real-time
These combined elements provide the desired steady state and dynamic performance during normal and off-normal operations.
To determine the engineering quality of a simulator’s modeling tools, simply answer this question. Does the simulator use equations to predict results or empirical correlations that model known data?
Many high-fidelity nuclear power plant simulators model known data. This method complies with the ANS 3.5 standard, but it doesn’t account for predictive modeling. When simulating phenomena that have not happened in real life (thankfully), the models must have the engineering muster to accurately predict behavior. That way, operator actions can provide a realistic response to the situation. See if your model technology uses six equations to accurately model mass, energy and momentum (in both gas and liquid phases) or simply uses correlated phenomena.
For example, let’s say you have a combined cycle gas turbine plant with a triple pressure heat recovery steam generator (HRSG). Solving the pressure, temperature and enthalpy as a single solution for the high, intermediate and low pressure stages of the HRSG provides the right response for the operator during start up. Make the plant a 2×1 or 3×1 configuration, and the integrated operation complexity of steam mixing, headers, startups/shutdowns and transients demand effective model equations. For utilities with supercritical or ultra-supercritical coal units, high-quality equations combined with extended steam tables are essential to model performance.
Ideally, you want a simulator that offers accurate performance across a wide range of operating conditions, from cold iron to rated power. To make that happen, the technology must rely on engineering principles rather than a limited set of data. Your engineers must also understand the plant design and operations before they can nodalize the model and ensure accurate and seamless responses across the entire operation.
2. Engineering Rigor
The size of your plant model influences your simulator fidelity. When you model more areas and functions of your plant, you gain additional uses for your simulator beyond training. Your return on investment increases significantly when engineering, human factors, safety analysis, I&C and other parts of the organization benefit from the tool.
Before deciding on a simulator vendor, it is important to consider the desired size for your plant model. Should you model additional plant systems or only the areas required for training the control room operator? While providing correct information to the control room operator is essential, a high-fidelity simulator has other important uses. You could virtually commission new control logic and equipment and plan plant efficiency improvements.
For example, a nodalized, matrix-based solution offers a more accurate response than complex thermodynamics (or electrical) systems. Why? Because it features a simultaneous equation solver. The model should tell you what is happening in the plant, not the other way around. When you calculate pressure, temperature, enthalpy, etc. at the nodal level, you can often see what’s happening in areas without instrumentation. This level of insight gives your engineers a better understanding of plant performance.
Simulation technology has grown leaps and bounds since the early days of flight simulation. But what really defines an effective simulator is the engineers who build the models. The experience of the engineer to understand plant data and its interactions, determine how detailed the model needs to be and implement the high-fidelity solution is paramount to ensuring the simulator is capable of addressing the user’s performance requirements and expectations.
Are your vendor’s engineers really writing control loops to make the simulator behave? Or are they mechanical engineers applying thermohydraulic principles to create dynamic effects?
Simulators will likely remain your plant’s most valuable resource for training operators, engineering, technical and maintenance personnel. However, their use now extends far beyond training. Today, innovative power plants and process plants use these simulators for engineering aides, virtual commissioning, analytics, cyber security, verification and validation. If you want to take advantage of your “Digital Twin,” you can’t base critical operating decisions on weaker fidelity models.
If you’re looking to invest in a new power plant simulator or expand the application of your simulator beyond training, remember that fidelity matters. Is your simulator truly high-fidelity?
Learn how a combined cycle power plant used a high-fidelity simulation for extensive distributed control system testing and operator training prior to plant commission.