This paper describes ongoing advancement of airborne hazard modeling capabilities in support of multiple agencies through the National Atmospheric Release Advisory Center (NARAC) and the Interagency Atmospheric Modeling and Atmospheric Assessment Center (IMAAC). A suite of software tools developed by Lawrence Livermore National Laboratory (LLNL) and collaborating organizations includes simple stand-alone, local-scale plume modeling tools for end user's computers, Web- and Internet-based software to access advanced 3-D flow and atmospheric dispersion modeling tools and expert analysis from the national center at LLNL, and state-of-the-science high-resolution urban models and event reconstruction capabilities.

According to theories of cognitive processing style or cognitive control mode, human performance is more effective when an individual’s cognitive state (e.g., intuition/scramble vs. deliberate/strategic) matches his/her ecological constraints or context (e.g., utilize intuition to strive for a "good-enough" response instead of deliberating for the "best" response under high time pressure). Ill-mapping between cognitive state and ecological constraints are believed to lead to degraded task performance. Consequently, incorporating support systems which are designed to specifically address multiple cognitive and functional states e.g., high workload, stress, boredom, and initiate appropriate mitigation strategies (e.g., reduce information load) is essential to reduce plant risk. Utilizing the concept of Cognitive Control Models, this paper will discuss the importance of tailoring support systems to match an operator's cognitive state, and will further discuss the importance of these ecological constraints in selecting and implementing mitigation strategies for safe and effective system performance. An example from the nuclear power plant industry illustrating how a support system might be tailored to support different cognitive states is included.

Smaller and multi-modular reactor (MMR) will be highly technologically-advanced systems allowing more system flexibility to reactors configurations (e.g., addition/deletion of reactor units). While the technical and financial advantages of systems may be numerous, MMR presents many human factors challenges that may pose vulnerability to plant safety. An important human factors challenge in MMR operation and performance is the monitoring of data from multiple plants from centralized control rooms where human operators are responsible for interpreting, assessing, and responding to different system’s states and failures (e.g., simultaneously monitoring refueling at one plant while keeping an eye on another plant’s normal operating state). Furthermore, the operational, safety, and performance requirements for MMR can seriously change current staffing models and roles, the mode in which information is displayed, procedures and training to support and guide operators, and risk analysis. For these reasons, addressing human factors concerns in MMR are essential in reducing plant risk.