Motivation: Methods that focus on secondary structures, such as Position Specific Scoring Matrices and Hidden Markov Models, have proved useful for assigning proteins to families. However, for assigning proteins to an attribute class within a family these methods may introduce more free parameters than are needed. There are fewer members and there is less variability among sequences within a family. We describe a method for organizing proteins in a family that exhibits up to an order of magnitude reduction in the number of parameters. The basis is the log odds ratio commonly used to measure similarity. We adapt this to characterize the sequence dissimilarities that give rise to attribute differentiation. This leads to the definition of Class Attribute Substitution Matrices (CLASSUM), a dual of the BLOSUM. Results: The method was applied to classify sequences hierarchically in the lambda and kappa subgroups of the immunoglobulin superfamily. Positions conferring class were identified based on the degree of amino acid variability at a position. The CLASSUM computed for these positions classified better than 90% of test data correctly compared with 35–50% for BLOSUM-62. The expected value for a random matrix is 14%. The results suggest that family-specific data-derived substitution matrices can improve the resolution of automated methods that use generic substitution matrices for searching for and classifying proteins.

Transmission of information using ultrasonic elastic waves on existing metallic pipes provides an alternative communication option across physical barriers in a highly partitioned industrial complex, such as a nuclear facility. This work investigates the feasibility of the transmission of digital images over metallic pipes. Ultrasonic communication systems for transmission of images on a nuclear-grade stainless steel pipe were assembled for bench-scale demonstration. Information carriers in this system are refracted shear waves transmitted and received with piezoelectric transducers (PZTs) operating at 2-MHz nominal frequency. The refraction and propagation of ultrasonic shear waves were modeled with COMSOL software. An amplitude shift keying (ASK) communication protocol for image transmission was developed and implemented in the GNURadio software-defined radio (SDR) environment. Digital information was converted to analog ultrasonic signals using Red Pitaya electronic boards. The performance of the ASK protocol is evaluated at the output of every block in the GNURadio program by monitoring the transmission of select characters. Using the ASK communication protocol, the transmission of the 32-KB image was demonstrated at 2-Kbps bitrate across 6-ft-long stainless steel pipe. Preliminary evaluation of ultrasonic communication on the piping of a nuclear facility, such as signal transmission on bent pipes, was performed with COMSOL computer simulations.

The interaction of the active control system with passive safety behavior is investigated for sodium-cooled fast reactors. A claim often made of advanced reactors is that they are passively safe against unprotected upset events. In practice, such upset events are not analyzed in the context of the plant control system, but rather the analyses are performed without considering the normally programmed response of the control system (open-loop approach). This represents an oversimplification of the safety case. The issue of passive safety override arises since the control system commands actuators whose motions have safety consequences. Depending on the upset involving the control system (operator error, active control system failure, or inadvertent control system override), an actuator does not necessarily go in the same direction as needed for safety. So neglecting to account for control system action during an unprotected upset is nonconservative from a safety standpoint. It is important then, during the design of the plant, to consider the potential for the control system to work against the inherent and safe regulating effects of purposefully engineered temperature feedbacks.

Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity V_L as the main features driving MCO. Using a Q and a V_L for each fuel bundle gives 1528 Q and a V_L feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual inputs to the neural network model. The second approach involved the use of statistical techniques such as Pearson’s correlation and k-means analysis. The identified groupings of bundles behaved similarly. Treating each grouping as a single feature further reduced the input variable set to a manageable number. A model selection criterion is proposed, and results are presented along with a discussion of related issues.

Advanced reactors are often claimed to be passively safe against unprotected upset events. In common practice, these events are not considered in the context of the plant control system, i.e., the reactor is subjected to classes of unprotected upset events while the normally programmed response of the control system is assumed not to be present. However, this approach constitutes an oversimplification since, depending on the upset involving the control system, an actuator does not necessarily go in the same direction as needed for safety. In this work, dynamic simulations are performed to assess the degree to which the inherent self-regulating plant response is safe from active control system override. The simulations are meant to characterize the resilience of the plant to unprotected initiators. The initiators were represented and modeled as an actuator going to a hard limit. Consideration of failure is further limited to individual controllers as there is no cross-connect of signals between these controllers. The potential for passive safety override by the control system is then relegated to the single-input single-output controllers. The results show that when the plant control system is designed by taking into account and quantifying the impact of the plant control system on accidental scenarios there is very limited opportunity for the preprogrammed response of the control system to override passive safety protection in the event of an unprotected initiator.

This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.