Eliminate Thermal Limit Bias in BWRs with ThermalLimits.ai
ThermalLimits.ai – A prediction platform for online Thermal Limits in BWRs
ThermalLimits.ai is a state-of-the-art tool that yields real world, high value results via Machine Learning. It enables powerful predictive capability into crucial operating limits – ensuring compliance with technical specifications, enabling reduced reload fuel costs, and eliminating operational challenges.
NEW METHODOLOGY
Blue Wave AI Labs is pioneering online thermal limit prediction capability with the creation of ThermalLimits.ai to address these deficiencies. Our proprietary physics-informed approach uses machine learning (ML) to leverage historical fuel cycle data, outputs from core simulators, and past online thermal performance to construct a reliable offline surrogate to enable predicting the online thermal limits for a future cycle.. The underlying methodology used to develop these models is the notion of an error-correction deep neural network that leverages the offline nodal or bundle thermal limit array in conjunction with additional offline datasets to train a network that predicts the corresponding online thermal limit nodal or bundle array, thereby enabling predictive capability for online thermal limits from offline nodal power distributions.
REAL WORLD
Accurate predictions of core-wide and local behavior are crucial to assuring that targeted margins to the operating limits are maintained. Deviation between measured performance and design predictions can lead to operational challenges, such as unplanned derated conditions, premature coastdown, or increased fuel costs by loading more fuel than required for targeted energy production. Historically, inability to accurately predict online thermal limits from offline methods has challenged core design and cycle management. While actual operations may at times depart from cycle design basis projections, there exists an inherent bias between offline and online methods that stems from the nature of the two systems. Both methodologies rely on a three-dimensional neutronics simulator model to calculate the reactor’s power, moderator, void, and flow distributions —from which margin to thermal limits can be determined. However, these calculations are approximations, and the offline quantities determined from them are inexact estimates that lead to uncertainty in thermal limits. Online methods, on the other hand, employ an adaptive process through feedback directly from in-core nuclear instrumentation while the reactor is online. Up until recently, there has been no reliable method to bridge the gap between online and offline methods leading to inaccurate and inconsistent predictions of online thermal limits.
HIGH-VALUE RESULTS
The predictive capability of ThermalLimits.ai is illustrated below for a typical test cycle for a large BWR. Individual models for each of the MFLCPR, MFLPD, and MAPRAT distributions demonstrate an average reduction in the observed bias by 47% (1.9x) for MFLCPR, 75% (4.0x) for MFLPD, and 72% (3.5x) for MAP . RAT. More-over, across all fuel cycles independently tested, the maximum steady-state bias between online values and model predictions never exceeds 3.9% (for MAPRAT and MFLPD) and 1.5% for MFLCPR.
FEATURES
ThermalLimits.ai is a robust state-of-the-art SaaS application for the nuclear power industry that provides unparalleled accuracy for online thermal limit forecasting in both reload core design and cycle management engineering applications. Additional capabilities of these models include:
–An average bias of less than 0.75% for the max MFLPD and MAPRAT, compared to more than a 4% mean bias for conventional offline methods.
– An average bias of less than 0.30% for MFLCPR, representing a 80% reduction in the bias from conventional offline methods.
– An average bias of less than 0.39% for MFLCPR, representing a 46% reduction in the bias from conventional offline methods.
– A 2x reduction in mean bias for MFLCPR compared to conventional methods.
– More accurate predictions of the full nodal distributions for MAPRAT and MFLPD, reducing the average nodal bias by more than a factor of two.
– Correct identification of the online most limiting node/bundle location more than 85% of the time, compared to 60% from off-line methods.
– No degradation in model performance when used for mixed cores or during fuel transitions. With these predictive capabilities, BWR operators can design the most economical and efficient reload cores by eliminating excess design margin, reducing rework, and avoiding operational challenges that often result in power derates or increased coastdown lengths.
Ensure optimal core conditions, reduce reload fuel costs, and eliminate premature coastdown with ThermalLimits.ai.
Individual models for each of the MFLCPR, MFLPD, and MAPRAT distributions demonstrate an average reduction in the observed bias by 47% (1.9x) for MFLCPR, 75% (4.0x) for MFLPD, and 72% (3.5x) for MAPRAT.
DATA REQUIREMENTS
Typical situations require approximately three fuel cycles worth of offline and online datasets for a given reactor unit. ThermalLimits.ai is compati4ble with outputs from most vendor and vendor-independent nuclear fuel analysis software and methods.
REQUIREMENTS FOR ThermalLimits
ThermalLimits.ai is accessed via a web browser and is available for all standard computing platforms with a high-speed Internet connection, running most modern 32- and 64-bit operating systems and mobile operating systems: Linux, Windows, macOS, Android, iOS, and UNIX architectures are all acceptable environments for ThermalLimits.ai.
ENERGIZE RELOAD DESIGN
Energize reload design with the BWnuclear.ai software suite. These AI-based predictive algorithms integrate seamlessly, whether it be for reload core design or cycle management applications.
Our Thermal Limits predictions bridge the gap between offline and online methods yielding unparalleled core efficiency
The Problem:
Inability to accurately predict online thermal limits from offline methods has challenged core design and cycle management.
Offline thermal limits calculations are approximations, and the quantities determined from them are inexact estimates that lead to uncertainty in thermal limits.
If not accurately predicted, the result is:
Inaccurate and inconsistent predictions of online thermal limits yield inefficent core conditions, premature coastdown, lost revenue, and/or increased fuel costs.
Or loading more fresh fuel than necessary (Direct Cost Impact)
The Significance:
Energize reload design with BWnuclear.ai Software Suite. Our AI-based proprietary predictive algorithms integrate seamlessly, whether it be for reload core design or cycle management applications.
Example demonstrating the accuracy between the offline, online, and model predictions for location of max MFLPD
