CoreDesigner.ai

NEW METHODOLOGY

Blue Wave AI Labs is pioneering MCO prediction capability with the creation of MCO.ai to address these deficiencies and peer inside the ‘black box’ of MCO to meet the demands of current and future core designs. Our proprietary physics-constrained approach uses artificial intelligence (AI) coupled with machine learning (ML) to leverage historical fuel cycle data, outputs from core simulators, and past MCO measurements.

By using AI and ML, we construct a neural representation of MCO dynamics, yielding high-value results in the form of powerful predictive capability. Through feature engineering and a physical understanding of the underlying mechanisms, we transform the datasets into a “canonical set” of key drivers of MCO. This enables the development of high-fidelity models with parameters that core designers and operators can control, giving the models not only predictive power but, just as important, corrective power.

REAL WORLD

The amount of liquid water mixed with steam leaving a boiling water reactor’s moisture separators, referred to as moisture carryover (MCO), has been nearly impossible to predict by conventional methods. There are design specifications limiting how much MCO is permissible before operators must take remedial action (of which one costly option is a power derate). New boiling water reactor (BWR) core designs and aggressive operating strategies can push steam separators beyond optimal performance windows, causing elevated levels of MCO.

Excess moisture in the steam is problematic for many reasons, most importantly due to its ability to carry impurities dissolved in the water throughout the entire plant. MCO can increase erosion of the internal surfaces of the main steam isolation valves (MSIVs) and at the turbine, potentially causing costly repairs. Perhaps even more troublesome, soluble Cobalt-60 is carried over with the steam which increases plant dose rates and the collective radiation exposure of plant personnel. Beyond this, a small reduction in electrical output occurs with high MCO.

Up until recently, there has been no reliable method to forecast future MCO levels prior to or during a new fuel cycle. Consequently, the primary method to mitigate high MCO is to design the core with a larger-than-required reload batch size, thereby introducing potentially unnecessarily high reload fuel costs.

HIGH-VALUE RESULTS

The predictive capability of MCO.ai is illustrated below for two BWR units. Since the time the model was first deployed at these units in 2018, the average prediction error is percent MCO. The exceptional level of performance at this generating station is now limited only by the resolution imposed from the MCO measurement uncertainty. Comparable levels of accuracy have been obtained at multiple other BWRs that have adopted this enabling technology.

FEATURES

MCO.ai is a robust, state-of-the-art SaaS application for the nuclear power industry that provides unparalleled accuracy for MCO forecasting in both reload core design and cycle management engineering applications. Additional features include the abilities to:

 – Upload historical data in multiple formats (.zip, .dat or HDF5) for model evolution and training, resulting in powerful predictive capabilities. – Upload cycle depletions in multiple formats for prediction reports and scenario planning. – Generate, delete, download, and email MCO projection reports in multiple formats. – Graph and view historical cycle data for trending and comparisons.    – Filter and sort data tables quickly and seamlessly. – Create ‘Design’ or ‘Operating’ scenarios to mitigate MCO levels during design or manage MCO during operation. – Store and archive historical cycle data, design files, and MCO projection reports.